AI Grammar Rephrase

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Wix.com

Wix.com Ltd. (Hebrew: וויקס.קום, romanized: wix.com) or simply Wix is an Israeli software company, publicly listed in the US, that provides cloud-based web development services. It offers tools for creating HTML5 websites for desktop and mobile platforms using online drag-and-drop editing. Along with its headquarters and other offices in Israel, Wix also has offices in Brazil, Canada, Germany, India, Ireland, Japan, Lithuania, Poland, the Netherlands, the United States, Ukraine, and Singapore. Users can add applications for social media, e-commerce, online marketing, contact forms, e-mail marketing, and community forums to their websites. The Wix website builder is built on a freemium business model, earning its revenues through premium upgrades. According to the W3Techs technology survey website, Wix was used by 2.5% of websites as of September 2023; at the end of May 2025, it was 3.8%. == History == === Corporate affairs === Wix was founded in 2006 by Israeli developers Avishai Abrahami, Nadav Abrahami, and Giora Kaplan. With its main offices in Tel Aviv, Wix was backed by investors Insight Venture Partners, Mangrove Capital Partners, Bessemer Venture Partners, DAG Ventures, and Benchmark Capital. By April 2010, Wix had 3.5 million users and raised US$10 million in Series C funding provided by Benchmark Capital and existing investors Bessemer Venture Partners and Mangrove Capital Partners. In March 2011, Wix had 8.5 million users and raised US$40 million in Series D funding, bringing its total funding to that date to US$61 million. By August 2013, the Wix platform had more than 34 million registered users. On 5 November 2013, Wix had an initial public offering on NASDAQ, raising about US$127 million for the company and some share holders. In 2016, Mark Tluszcz became the chair of the board of directors. In 2020, Wix's revenue increased to $989 million, a 30% rise year-on-year, primarily due to the shift of businesses online during the coronavirus pandemic. The company added over 31 million new registered users in 2020, reaching a total of 196.7 million by year's end. Wix added approximately 1 million net new premium subscriptions in 2020, surpassing $1 billion in annual collections for the first time. By the end of the year, there were 5.5 million premium subscriptions, a 22% increase compared to the end of 2019. As of its most recent reporting in June 2024, Wix has over 260 million users worldwide. === Product development === ==== 2000s ==== Wix entered an open beta phase in 2007 using a platform based on Adobe Flash. ==== 2010s ==== In June 2011, Wix launched the Facebook store module, making its first step into social commerce. In March 2012, Wix launched a new HTML5 site builder, replacing the Adobe Flash technology. In October 2012, Wix launched an app market for users to sell applications built with the company's automated web development technology. In August 2014, Wix launched Wix Hotels, a booking system for hotels, bed and breakfasts, and vacation rentals that use Wix websites. In June 2016, Wix introduced Wix ADI (Artificial Design Intelligence), a platform that uses artificial intelligence to design websites. ==== 2020s ==== In 2020, Wix launched an additional CMS, EditorX, which included additional CSS features to the original builder. In July 2023, Wix announced that it would be building on its ADI technology to create an AI powered website generator In October 2023, Wix launched the Wix Studio website builder. Co-founder and CEO, Avishai Abrahami described the platform as a “product for agencies”. In March 2024, the AI web builder, which uses a chatbot to help users create content was launched to the public. In March 2025, the digital publisher CNET has identified Wix as the "Best overall website builder overall." In August 2025, Wix announced it would launch banking services—including checking accounts and loans for small businesses—via a partnership with Israeli fintech Unit Finance, as it sought to diversify amid what it described as threats to its core website-building business from artificial intelligence. In January 2026, Wix launched Wix Harmony. Wix harmony is an AI website builder that uses agentic technology, generative design and vibe coding—with manual editing features for additional control. In May 2026, Wix announced layoffs affecting approximately 1,000 employees, or 20% of its workforce. CEO Avishai Abrahami cited two factors: the need to restructure around artificial intelligence and the appreciation of the Israeli shekel against the US dollar, which increased the cost of its Israel-based workforce relative to its dollar-denominated revenue. === Acquisitions === In April 2014, Wix announced the acquisition of Appixia, an Israeli startup for creating native mobile commerce (mCommerce) apps. In October 2014, Wix announced its acquisition of OpenRest, a developer of online ordering systems for restaurants. In April 2015, Wix acquired Moment.me, a mobile website builder for events and marketing tools for social lead generation. On 23 February 2017, Wix acquired the online art community DeviantArt for US$36 million. In January 2017, the company acquired Flok, a provider of customer loyalty programs tools. In February 2020, Wix acquired Inkfrog for eBay sellers, a web design company that provides customized business management software for eBay sellers. On 2 March 2021, Wix acquired SpeedETab, a Miami-based restaurant online technology provider. In May 2021, Wix acquired Rise.ai, a gift card and customer re-engagement package for online brands. A month later, Wix acquired Modalyst, a marketplace and drop-shipping platform. In May 2025, Wix acquired Hour One, a startup specializing in AI-powered video creation tools, to enhance its generative AI capabilities. In June 2025, the company acquired Base44, owned by independent entrepreneur Maor Shlomo, with the intention of integrating Base44's artificial intelligence capabilities and conversational interface into Wix's website and app building platform. == Description == Wix uses a freemium business model. Users can create websites for free then must purchase premium packages to connect their sites to their own domains, remove Wix ads, access the form builder, add e-commerce capabilities, or buy extra data storage and bandwidth. Wix provides customizable website templates and a drag-and-drop HTML5 website builder that includes apps, graphics, image galleries, fonts, vectors, animations, and other options. Users also may opt to create their web sites from scratch. In October 2013, Wix introduced a mobile editor for mobile viewing customization. Wix App Market offers both free and subscription-based applications, with a revenue split of 80% for the developer and 20% for Wix. Customers can integrate third-party applications into their own web sites, such as photograph feeds, blogging, music playlists, online community, e-mail marketing, and file management. Custom JavaScript code can be inserted into Wix webpages using the Velo API. == Controversies == === Use of WordPress code === In October 2016, there was a controversy over Wix's use of WordPress's GPL-licensed code. In response, Avishai Abrahami, Wix's CEO, published a response describing which open-source code was used and how Wix says it collaborates with the open-source community. However, it was subsequently noted that collaboration with the open-source community was not sufficient under the terms of the GPL license, which requires any code built on GPL-licensed code to be released under the same license. === Censorship === On 31 May 2021, 2021 Hong Kong Charter, a Wix-hosted website run by exiled Hong Kong activists, was shut down at the request of the Hong Kong Police. This was the first known case of Hong Kong's National Security Law being used to censor content on an overseas website. Wix later apologized for "mistakenly removing the website" and reinstated the website after it had been down for four days. In October 2023, Wix fired an employee in Dublin, Ireland, for having made social media posts critical of Israel. This incident led to criticism of Wix from members of the Oireachtas (the Irish parliament) and from the head of the Irish government, Taoiseach Leo Varadkar, who said it was "not okay to dismiss somebody because of their political views". Deputy head of government, Tánaiste Micheál Martin, also condemned their dismissal, stating "we tolerate debate with freedom of speech, freedom of opinion, and people have different opinions on these issues." The dismissed employee, Courtney Carey, successfully sued the company for unfair dismissal. Wix did not contest the charge, admitting liability. === Outreach abroad === In October 2023, The Irish Times reported that an Israeli advertising agency advised Wix staff how they can tailor posts for "outreach abroad". This included advice for Wix employees to “show Westernity” in social media posts supporting Israel, stating that “unlike the Gazans,
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Desktop Window Manager

Desktop Window Manager (DWM, previously Desktop Compositing Engine or DCE in builds of pre-reset Windows Longhorn) is the compositing window manager in Microsoft Windows since Windows Vista that enables the use of hardware acceleration to render the graphical user interface of Windows. It was originally created to enable portions of the new "Windows Aero" user experience, which allowed for effects such as transparency, 3D window switching and more. It is also included with Windows Server 2008, but requires the "Desktop Experience" feature and compatible graphics drivers to be installed. == Architecture == The Desktop Window Manager is a compositing window manager, meaning that each program has a buffer that it writes data to; DWM then composites each program's buffer into a final image. By comparison, the stacking window manager in Windows XP and earlier (and also Windows Vista and Windows 7 with Windows Aero disabled) comprises a single display buffer to which all programs write. DWM works in different ways depending on the operating system (Windows 7 or Windows Vista) and on the version of the graphics drivers it uses (WDDM 1.0 or 1.1). Under Windows 7 and with WDDM 1.1 drivers, DWM only writes the program's buffer to the video RAM, even if it is a graphics device interface (GDI) program. This is because Windows 7 supports (limited) hardware acceleration for GDI and in doing so does not need to keep a copy of the buffer in system RAM so that the CPU can write to it. Because the compositor has access to the graphics of all applications, it easily allows visual effects that string together visuals from multiple applications, such as transparency. DWM uses DirectX to perform the function of compositing and rendering in the GPU, freeing the CPU of the task of managing the rendering from the off-screen buffers to the display. However, it does not affect applications painting to the off-screen buffers – depending on the technologies used for that, this might still be CPU-bound. DWM-agnostic rendering techniques like GDI are redirected to the buffers by rendering the user interface (UI) as bitmaps. DWM-aware rendering technologies like WPF directly make the internal data structures available in a DWM-compatible format. The window contents in the buffers are then converted to DirectX textures. The desktop itself is a full-screen Direct3D surface, with windows being represented as a mesh consisting of two adjacent (and mutually inverted) triangles, which are transformed to represent a 2D rectangle. The texture, representing the UI chrome, is then mapped onto these rectangles. Window transitions are implemented as transformations of the meshes, using shader programs. With Windows Vista, the transitions are limited to the set of built-in shaders that implement the transformations. Greg Schechter, a developer at Microsoft has suggested that this might be opened up for developers and users to plug in their own effects in a future release. DWM only maps the primary desktop object as a 3D surface; other desktop objects, including virtual desktops as well as the secure desktop used by User Account Control are not. Because all applications render to an off-screen buffer, they can be read off the buffer embedded in other applications as well. Since the off-screen buffer is constantly updated by the application, the embedded rendering will be a dynamic representation of the application window and not a static rendering. This is how the live thumbnail previews and Windows Flip work in Windows Vista and Windows 7. DWM exposes a public API that allows applications to access these thumbnail representations. The size of the thumbnail is not fixed; applications can request the thumbnails at any size - smaller than the original window, at the same size or even larger - and DWM will scale them properly before returning. Aero Flip does not use the public thumbnail APIs as they do not allow for directly accessing the Direct3D textures. Instead, Aero Flip is implemented directly in the DWM engine. The Desktop Window Manager uses Media Integration Layer (MIL), the unmanaged compositor which it shares with Windows Presentation Foundation, to represent the windows as composition nodes in a composition tree. The composition tree represents the desktop and all the windows hosted in it, which are then rendered by MIL from the back of the scene to the front. Since all the windows contribute to the final image, the color of a resultant pixel can be decided by more than one window. This is used to implement effects such as per-pixel transparency. DWM allows custom shaders to be invoked to control how pixels from multiple applications are used to create the displayed pixel. The DWM includes built-in Pixel Shader 2.0 programs which compute the color of a pixel in a window by averaging the color of the pixel as determined by the window behind it and its neighboring pixels. These shaders are used by DWM to achieve the blur effect in the window borders of windows managed by DWM, and optionally for the areas where it is requested by the application. Since MIL provides a retained mode graphics system by caching the composition trees, the job of repainting and refreshing the screen when windows are moved is handled by DWM and MIL, freeing the application of the responsibility. The background data is already in the composition tree and the off-screen buffers and is directly used to render the background. In pre-Vista Windows OSs, background applications had to be requested to re-render themselves by sending them the WM_PAINT message. DWM uses double-buffered graphics to prevent flickering and tearing when moving windows. The compositing engine uses optimizations such as culling to improve performance, as well as not redrawing areas that have not changed. Because the compositor is multi-monitor aware, DWM natively supports this too. During full-screen applications, such as games, DWM does not perform window compositing and therefore performance will not appreciably decrease. On Windows 8 and Windows Server 2012, DWM is used at all times and cannot be disabled, due to the new "start screen experience" implemented. Since the DWM process is usually required to run at all times on Windows 8, users experiencing an issue with the process are seeing memory usage decrease after a system reboot. This is often the first step in a long list of troubleshooting tasks that can help. It is possible to prevent DWM from restarting temporarily in Windows 8, which causes the desktop to turn black, the taskbar grey, and break the start screen/modern apps, but desktop apps will continue to function and appear just like Windows 7 and Vista's Basic theme, based on the single-buffer renderer used by XP. They also use Windows 8's centered title bar, visible within Windows PreInstallation Environment. Starting up Windows without DWM will not work because the default lock screen requires DWM unlike the fallback lockscreen that appears as a command line interface program when Windows.UI.Logon.dll isn't present on Windows versions such as 1507 and later, so it can only be done on the fly, and does not have any practical purposes. Starting with Windows 10, disabling DWM in such a way will cause the entire compositing engine to break, even traditional desktop apps, due to Universal App implementations in the taskbar and new start menu. Windows can still be partially usable without the presence of DWM but requires Sihost.exe to not be present due to it relying on DWM. Most of the applications in Windows 11 require DWM to render UI elements and transparency, Windows 11's new task manager requires dwm to render menus unlike the fallback -d version. Unlike its predecessors, Windows 8 supports basic display adapters through Windows Advanced Rasterization Platform (WARP), which uses software rendering and the CPU to render the interface rather than the graphics card. This allows DWM to function without compatible drivers, but not at the same level of performance as with a normal graphics card. DWM on Windows 8 also adds support for stereoscopic 3D. == Redirection == For rendering techniques that are not DWM-aware, output must be redirected to the DWM buffers. With Windows, either GDI or DirectX can be used for rendering. To make these two work with DWM, redirection techniques for both are provided. With GDI, which is the most used UI rendering technique in Microsoft Windows, each application window is notified when it or a part of it comes in view and it is the job of the application to render itself. Without DWM, the rendering rasterizes the UI in a buffer in video memory, from where it is rendered to the screen. Under DWM, GDI calls are redirected to use the Canonical Display Driver (cdd.dll), a software renderer. A buffer equal to the size of the window is allocated in system memory and CDD.DLL outputs to this buffer rather than the video memory. Another buffer is allocated in the video memory to represent t
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Conference app

A conference app, also known as an event app or meeting app, is a mobile app developed to help attendees and meeting planners manage their conference experience. It typically includes conference proceedings and venue information, allowing users to create personalized schedules and engage with other users. A conference app can be a native app or web-based. In recent years, conference apps have gained in popularity as a sustainable solution for event management by reducing paper produced by printed materials. Advanced features often include real-time notifications for updates or changes, integration with virtual meeting platforms for hybrid or fully online events, and analytics tools for organizers to measure attendance and engagement. Additionally, some apps support sponsorship and exhibitor features, enabling businesses to showcase their products or services directly within the app.
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Resel

In image analysis, a resel (from resolution element) represents the actual spatial resolution in an image or a volumetric dataset. The number of resels in the image may be lower or equal to the number of pixel/voxels in the image. In an actual image the resels can vary across the image and indeed the local resolution can be expressed as "resels per pixel" (or "resels per voxel"). In functional neuroimaging analysis, an estimate of the number of resels together with random field theory is used in statistical inference. Keith Worsley has proposed an estimate for the number of resels/roughness. The word "resel" is related to the words "pixel", "texel", and "voxel". Waldo R. Tobler is probably among the first to use the word.
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Belief–desire–intention model

For popular psychology, the belief–desire–intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental models of the world are theories. It was used as a basis for developing the belief–desire–intention software model. == Applications == BDI was part of the inspiration behind the BDI software architecture, which Bratman was also involved in developing. Here, the notion of intention was seen as a way of limiting time spent on deliberating about what to do, by eliminating choices inconsistent with current intentions. BDI has also aroused some interest in psychology. BDI formed the basis for a computational model of childlike reasoning CRIBB.
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Verbal overshadowing

Verbal overshadowing is a phenomenon where giving a verbal description of sensory input impairs formation of memories of that input. This was first reported by Schooler and Engstler-Schooler (1990) where it was shown that the effects can be observed across multiple domains of cognition which are known to rely on non-verbal knowledge and perceptual expertise. One example of this is memory, which has been known to be influenced by language. Seminal work by Carmichael and collaborators (1932) demonstrated that when verbal labels are connected to non-verbal forms during an individual's encoding process, it could potentially bias the way those forms are reproduced. Because of this, memory performance relying on reportable aspects of memory that encode visual forms should be vulnerable to the effects of verbalization. == Initial findings == Schooler and Engstler-Schooler (1990) were the first to report findings of verbal overshadowing. In their study, participants watched a video of a simulated robbery and were instructed to either verbally describe the robber or engage in a control task. Those who engaged in giving a verbal description were less likely to correctly identify the robber from a test lineup, compared to those who engaged in the control task. A larger effect was detected when the verbal description was provided 20, rather than 5, minutes after the video, and immediately before the test lineup. A meta-analysis by Meissner and Brigham (2001) supported the effects of verbal overshadowing, showing a small but reliably negative effect. == General effects of verbal overshadowing == The effects of verbal overshadowing have been generalized across multiple domains of cognition that are known to rely on non-verbal knowledge and perceptual expertise, such as memory. Memory has been known to be influenced by language. Seminal work by Carmichael and collaborators (1932) demonstrated that labels attached to, or associated with, non-verbal forms during memory encoding can affect the way the forms were subsequently reproduced. Because of this, memory performance that relies on reportable aspects of memory that encode visual forms should be vulnerable to the effects of verbalization. Pelizzon, Brandimonte, and Luccio (2002) found that visual memory representations appear to incorporate visual, spatial, and temporal characteristics. It is explained as follows: With the temporal code (where the only information available is the sequence of the stimuli), performance levels remain high, unless participants are required to retrieve the stimuli in a different order from that used at encoding (visual cue). In this case, performance is significantly impaired, even in the presence of a visual cue. The study showed that order information acts as a link between the two separate representations of figure and background, hence preventing verbal overshadowing at encoding (temporal component) or attenuating its influence at retrieval (spatial component).(p. 960) Hatano, Ueno, Kitagami, and Kawaguchi found that verbal overshadowing is likely to occur when participants verbally described targets in detail. Detailed verbal descriptions resulted in more frequently inaccurate descriptions that in turn created inaccurate representations in the memories of participants. Inaccuracies are also likely to occur when face recognition comes immediately after verbalization. Other forms of non-verbal knowledge affected by verbal overshadowing include the following: [Verbal overshadowing] has also been observed when participants attempt to generate descriptions of other 'difficult-to-describe' stimuli such as colors (Schooler and Engstler-Schooler, 1990) or abstract figures (Brandimonte et al., 1997), or other non-visual tasks such as wine tasting (Melcher and Schooler, 1996), decision making (Wilson and Schooler, 1991), and insight problem-solving. (p. 871) (Schooler et al., 1993) Verbalization of stimuli leads to the disruption of non-reportable processes that are necessary for achieving insight solutions, which are distinct from language processes. Schooler, Ohlsson, and Brooks (1993) found that face recognition requires information that cannot be adequately verbalized, giving rise to difficulty in describing factors in recognition judgments. Subjects were less effective in solving insight problems when compelled to put their thoughts in words, which suggests that language may interfere with thought. The verbal overshadowing effect was not seen when participants engaged in articulatory suppression. Performance was reduced in both the verbal and non-verbal description conditions. This is evidence that verbal encoding plays a role in face recognition. By testing with distracting faces presented between study and test, Lloyd-Jones and Brown (2008) suggested a dual-process approach to recognition memory took place, that verbalization influenced familiarity-based processes at first, but its effects were later seen on recollection, when discrimination between items became more difficult. == Verbal overshadowing in facial recognition == The verbal overshadowing effect can be found for facial recognition because faces are predominately processed in a holistic or configurable manner. (Tanaka & Farah, 1993; Tanaka & Sengco, 1997) Verbalizing one's memory for a face is done using a featural or analytic strategy, leading to a drift from the configurable information about the face and to impaired recognition performance. However, Fallshore & Schooler (1995) found that the verbal overshadowing effect was not found when participants described faces of races different from their own. A study by Brown and Lloyd-Jones (2003) found that there was no verbal overshadowing effect found in car descriptions; it was only seen in facial descriptions. The authors noted that descriptions were no different on any measure including accuracy. It is suggested that less expertise in verbalizing faces rather than cars invokes a stronger shift in verbal and featural processing. This supports the concept of a transfer inappropriate retrieval framework and addresses some limitations of the effect. Wickham and Swift (2006) suggested that the verbal overshadowing effect is not seen in describing all faces, and one aspect that determines this is distinctiveness. Results showed that typical faces produce verbal overshadowing, while distinctive faces did not. In studies of eyewitness reports, variation in response criteria given by participants influenced the quality of the descriptions generated and accuracy on identification task, known as the retrieval-based effect. Face recognition was also impaired when subjects described a familiar face, such as a parent, or when describing a previously seen but novel face. Dodson, Johnson, and Schooler (1997) found that recognition was also impaired when participants were provided with a description of a previously seen face, and they were able to ignore provided versus self-generated descriptions more easily. This finding of verbal overshadowing suggested that eyewitness recognition is not only affected by their own descriptions, but of descriptions heard from others, such other eyewitness testimonies. == Voice recognition == The verbal overshadowing effect has also been found to affect voice identification. Research shows that describing a non-verbal stimuli leads to a decrease in recognition accuracy. In an unpublished study by Schooler, Fiore, Melcher, and Ambadar (1996), participants listened to a tape-recorded voice, after which they were asked either to verbally describe it or to not do so, and then asked to distinguish the voice from 3 similar distractor voices. The results showed that verbal overshadowing impaired accuracy of recognition based on gut feeling, suggesting an overall verbal overshadowing for voice recognition. Due to the forensic relevance of voices heard over the telephone and harassing phone calls that are often a problem for police, Perfect, Hunt, and Harris (2002) examined the influence of three factors on accuracy and confidence in voice recognition from a line-up. They expected to find an effect, because voice represents a class of stimuli that is difficult to describe verbally. This meets Schooler et al.'s (1997) modality mismatch criterion, meaning that describing the speakers age, gender, or accent is difficult, making voice recognition susceptible to the verbal overshadowing phenomenon. It was found that the method of memory encoding had no impact on performance, and that hearing a telephone voice reduced confidence but did not affect accuracy. They also found that providing a verbal description impaired accuracy but had no effect on confidence. The data showed an effect of verbal overshadowing in voice recognition and provided yet another disassociation between confidence and performance. Although there was a difference in confidence level, witnesses were able to identify voices over the telephone as accurately as voices heard direc
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Comparison of video editing software

This is a comparison of non-linear video editing software applications. See also a more complete list of video editing software. == General information == This table gives basic general information about the different editors: === Active === === Discontinued / Inactive === ==== Definition ==== professional: used for full length Hollywood movies; professional (small): mainly used for paid commercials, short films or podcasts/YouTube channels; prosumer: Mainly targeting private use, anything that can do more than just trimming a film; basic: trimming a film; == System requirements == This table lists the operating systems that different editors can run on without emulation, as well as other system requirements. Note that minimum system requirements are listed; some features (like High Definition support) may be unavailable with these specifications. "Unix" includes the similar Linux, BSD and Unix-like operating systems. == High definition/High resolution import == The table below indicates the ability of each program to import various High Definition video or High resolution video formats for editing. == Feature set == == Output options == Please note that recording to Blu-ray does not imply 1080@50p/60p . Most only support up to 1080i 25/30 frames per second recording. Also not all formats can be output.
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Scientific Working Group – Imaging Technology

The Scientific Working Group on Imaging Technology was convened by the Federal Bureau of Investigation in 1997 to provide guidance to law enforcement agencies and others in the criminal justice system regarding the best practices for photography, videography, and video and image analysis. This group was terminated in 2015. == History == As technology has advanced through the years, law enforcement has needed to stay abreast of emerging technological advances and use these in the investigation of crime. A factor that is considered when new technology is used in these investigations is the determination of whether the use of that new technology will be admissible in court. The judicial system in the United States currently has two standards used in the determination of admissibility of testimony regarding scientific evidence; the Daubert Standard and the Frye Standard. These standards guide the courts in the admissibility of testimony derived from the use of new technologies and scientific techniques. The Federal Bureau of Investigation (FBI), seeking to address possible admissibility issues with such testimony, established Scientific Working Groups starting with the Scientific Working Group on DNA Analysis and Methods (SWGDAM) in 1988. The goal of these groups is to open lines of communication between law enforcement agencies and forensic laboratories around the world while providing guidance on the use of new and innovative technologies and techniques. This guidance can lead to admissibility of evidence and/or testimony, provided proper methods in the collection of evidence and its analysis are employed. In 2009, the National Academy of Sciences released a report entitled, "Strengthening Forensic Science in the United States: A Path Forward." This report addresses many topics including challenges and disparities facing the forensic science community, standardization, certification of practitioners and accreditation of their respective entities, problems related to the interpretation of forensic evidence, the need for research, and the admission of forensic science evidence in litigation. This report mentions the Scientific Working Groups and their role in forensic science. The history of imaging technology (photography) can be said to extend back to the times of Chinese philosopher Mo-Ti (470-390 B.C.) who described the principles behind the precursor to the camera obscura. Since that time, advances in imaging technology include the discovery of chemical photographic processes in the 19th century and the use of electronic imaging technology that includes analog video cameras and digital video and still cameras. By the mid 1990s, it was apparent that technologically advanced camera systems such as these were being adopted for use in the criminal justice system. This led the FBI to convene a meeting of individuals working in the field of forensic imaging from federal, state, local, and foreign law enforcement, and the U.S. military, during the summer of 1997. As a result of this meeting, the Technical Working Group on Imaging Technology was formed from a core group of the meeting’s participants. This group later became the Scientific Working Group on Imaging Technology (SWGIT). Prior to the inception of SWGIT, some law enforcement agencies began adopting digital imaging technology. Due to the lack of guidelines or standards, some of these agencies attempted to replace all their film cameras with substandard digital cameras, only to find that the equipment they had purchased was not capable of accomplishing the mission for which they were intended. At that time only low resolution digital cameras were deemed affordable by some law enforcement agencies. Some of these agencies were forced to rethink their photography procedures and reverted to the use of film cameras or replaced their low-resolution digital cameras with higher quality, more expensive equipment. Also lacking at this early stage was guidance on how to store and archive digital image files. When SWGIT was formed, it was tasked with providing guidance to law enforcement and others in the criminal justice system by releasing documents that describe the best practices and guidelines for the use of imaging technology, to include these concerns and many others. This group was terminated in 2015. == SWGIT Function == During its existence, SWGIT provided information on the appropriate use of various imaging technologies including both established and new. This was accomplished through the release of documents such as the SWGIT Best Practices documents. As changes in technology occurred, these documents were updated. Over the course of its existence, SWGIT collaborated with other Scientific Working Groups to address imaging concerns within their respective disciplines. SWGIT published over 20 documents that dealt specifically with imaging technology. SWGIT also co-published documents with the Scientific Working Group on Digital Evidence (SWGDE) that had a component or components dealing with imaging technology. SWGIT also provided imaging technology guidance and input for documents from the Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST), the Scientific Working Group for Forensic Document Examination (SWGDOC), and the Scientific Working Group on Shoeprint and Tire Tread Evidence (SWGTREAD). SWGIT assisted the American Society of Crime Lab Directors/Laboratory Accreditation Board (ASCLD/LAB) in the writing of definitions and standards for the accreditation of Digital and Multimedia Evidence sections of crime laboratories. In addition to releasing documents, SWGIT members disseminated best practices for law enforcement professionals where imaging technology was concerned. This was carried out by attending and lecturing at meetings and conferences of various forensic organizations that included: The American Academy of Forensic Sciences (AAFS) The International Association for Identification (IAI) The Law Enforcement and Emergency Services Video Association (LEVA) The American Society of Crime Lab Directors (ASCLD) The SWGIT membership consisted of approximately fifty scientists, photographers, instructors, and managers from more than two dozen federal, state, and local law enforcement agencies, as well as from the academic and research communities. The membership elected its officers from within. SWGIT was composed of the Executive Committee, four standing subcommittees, and ad hoc subcommittees appointed on an as-needed basis. The standing subcommittees were: Image Analysis, Forensic Photography, Video, and Outreach. This group was terminated in 2015. == Legal Proceedings == The following court cases have conducted Daubert v. Merrell Dow Pharm., Inc., 509 U.S. 579 (1993) hearings in which SWGIT best practice documents have been cited as accepted protocol, methodology, and as generally accepted techniques in the forensic community: U. S. v. Rudy Frabizio, U.S. District Court, Boston, MA, 2008 (Image Authentication) U.S. v. Nobumochi Furukawa, U.S. District Court, Minnesota, 2007 (Video Authentication) U.S. v. John Stroman, U.S. District Court, South Carolina, 2007 (Facial Comparison Analysis) State of Texas v. Daniel Day, Tarrant County Texas, 2005 (Camera Identification to Images) U.S. v. Marc Watzman, U.S. District Court, Northern Illinois, 2004 (Video Authentication) U.S. v. McKreith, U.S. District Court, Fort Lauderdale, FL, 2002 (Photo comparison of shirt) == Termination == This group was unfunded by the FBI in 2015.
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Color quantization

In computer graphics, color quantization or color image quantization is quantization applied to color spaces; it is a process that reduces the number of distinct colors used in an image, usually with the intention that the new image should be as visually similar as possible to the original image. Computer algorithms to perform color quantization on bitmaps have been studied since the 1970s. Color quantization is critical for displaying images with many colors on devices that can only display a limited number of colors, usually due to memory limitations, and enables efficient compression of certain types of images. The name "color quantization" is primarily used in computer graphics research literature; in applications, terms such as optimized palette generation, optimal palette generation, or decreasing color depth are used. Some of these are misleading, as the palettes generated by standard algorithms are not necessarily the best possible. == Algorithms == Most standard techniques treat color quantization as a problem of clustering points in three-dimensional space, where the points represent colors found in the original image and the three axes represent the three color channels. Almost any three-dimensional clustering algorithm can be applied to color quantization, and vice versa. After the clusters are located, typically the points in each cluster are averaged to obtain the representative color that all colors in that cluster are mapped to. The three color channels are usually red, green, and blue, but another popular choice is the Lab color space, in which Euclidean distance is more consistent with perceptual difference. The most popular algorithm by far for color quantization, invented by Paul Heckbert in 1979, is the median cut algorithm. Many variations on this scheme are in use. Before this time, most color quantization was done using the population algorithm or population method, which essentially constructs a histogram of equal-sized ranges and assigns colors to the ranges containing the most points. A more modern popular method is clustering using octrees, first conceived by Gervautz and Purgathofer and improved by Xerox PARC researcher Dan Bloomberg. If the palette is fixed, as is often the case in real-time color quantization systems such as those used in operating systems, color quantization is usually done using the "straight-line distance" or "nearest color" algorithm, which simply takes each color in the original image and finds the closest palette entry, where distance is determined by the distance between the two corresponding points in three-dimensional space. In other words, if the colors are ( r 1 , g 1 , b 1 ) {\displaystyle (r_{1},g_{1},b_{1})} and ( r 2 , g 2 , b 2 ) {\displaystyle (r_{2},g_{2},b_{2})} , we want to minimize the Euclidean distance: ( r 1 − r 2 ) 2 + ( g 1 − g 2 ) 2 + ( b 1 − b 2 ) 2 . {\displaystyle {\sqrt {(r_{1}-r_{2})^{2}+(g_{1}-g_{2})^{2}+(b_{1}-b_{2})^{2}}}.} This effectively decomposes the color cube into a Voronoi diagram, where the palette entries are the points and a cell contains all colors mapping to a single palette entry. There are efficient algorithms from computational geometry for computing Voronoi diagrams and determining which region a given point falls in; in practice, indexed palettes are so small that these are usually overkill. Color quantization is frequently combined with dithering, which can eliminate unpleasant artifacts such as banding that appear when quantizing smooth gradients and give the appearance of a larger number of colors. Some modern schemes for color quantization attempt to combine palette selection with dithering in one stage, rather than perform them independently. A number of other much less frequently used methods have been invented that use entirely different approaches. The Local K-means algorithm, conceived by Oleg Verevka in 1995, is designed for use in windowing systems where a core set of "reserved colors" is fixed for use by the system and many images with different color schemes might be displayed simultaneously. It is a post-clustering scheme that makes an initial guess at the palette and then iteratively refines it. In the early days of color quantization, the k-means clustering algorithm was deemed unsuitable because of its high computational requirements and sensitivity to initialization. In 2011, M. Emre Celebi reinvestigated the performance of k-means as a color quantizer. He demonstrated that an efficient implementation of k-means outperforms a large number of color quantization methods. The high-quality but slow NeuQuant algorithm reduces images to 256 colors by training a Kohonen neural network "which self-organises through learning to match the distribution of colours in an input image. Taking the position in RGB-space of each neuron gives a high-quality colour map in which adjacent colours are similar." It is particularly advantageous for images with gradients. Finally, one of the newer methods is spatial color quantization, conceived by Puzicha, Held, Ketterer, Buhmann, and Fellner of the University of Bonn, which combines dithering with palette generation and a simplified model of human perception to produce visually impressive results even for very small numbers of colors. It does not treat palette selection strictly as a clustering problem, in that the colors of nearby pixels in the original image also affect the color of a pixel. See sample images. == History and applications == In the early days of PCs, it was common for video adapters to support only 2, 4, 16, or (eventually) 256 colors due to video memory limitations; they preferred to dedicate the video memory to having more pixels (higher resolution) rather than more colors. Color quantization helped to justify this tradeoff by making it possible to display many high color images in 16- and 256-color modes with limited visual degradation. Many operating systems automatically perform quantization and dithering when viewing high color images in a 256 color video mode, which was important when video devices limited to 256 color modes were dominant. Modern computers can now display millions of colors at once, far more than can be distinguished by the human eye, limiting this application primarily to mobile devices and legacy hardware. Nowadays, color quantization is mainly used in GIF and PNG images. GIF, for a long time the most popular lossless and animated bitmap format on the World Wide Web, only supports up to 256 colors, necessitating quantization for many images. Some early web browsers constrained images to use a specific palette known as the web colors, leading to severe degradation in quality compared to optimized palettes. PNG images support 24-bit color, but can often be made much smaller in filesize without much visual degradation by application of color quantization, since PNG files use fewer bits per pixel for palettized images. The infinite number of colors available through the lens of a camera is impossible to display on a computer screen; thus converting any photograph to a digital representation necessarily involves some quantization. Practically speaking, 24-bit color is sufficiently rich to represent almost all colors perceivable by humans with sufficiently small error as to be visually identical (if presented faithfully), within the available color space. However, the digitization of color, either in a camera detector or on a screen, necessarily limits the available color space. Consequently there are many colors that may be impossible to reproduce, regardless of how many bits are used to represent the color. For example, it is impossible in typical RGB color spaces (common on computer monitors) to reproduce the full range of green colors that the human eye is capable of perceiving. With the few colors available on early computers, different quantization algorithms produced very different-looking output images. As a result, a lot of time was spent on writing sophisticated algorithms to be more lifelike. === Quantization for image compression === Many image file formats support indexed color. A whole-image palette typically selects 256 "representative" colors for the entire image, where each pixel references any one of the colors in the palette, as in the GIF and PNG file formats. A block palette typically selects 2 or 4 colors for each block of 4x4 pixels, used in BTC, CCC, S2TC, and S3TC. === Editor support === Many bitmap graphics editors contain built-in support for color quantization, and will automatically perform it when converting an image with many colors to an image format with fewer colors. Most of these implementations allow the user to set exactly the number of desired colors. Examples of such support include: Photoshop's Mode→Indexed Color function supplies a number of quantization algorithms ranging from the fixed Windows system and Web palettes to the proprietary Local and Global algorithms for generating palettes suited to a particu
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Logogen model

The logogen model of 1969 is a model of speech recognition that uses units called "logogens" to explain how humans comprehend spoken or written words. Logogens are a vast number of specialized recognition units, each able to recognize one specific word. This model provides for the effects of context on word recognition. == Overview == The word logogen can be traced back to the Greek-language word logos, which means "word", and genus, which means "birth". British scientist John Morton's logogen model was designed to explain word recognition using a new type of unit known as a logogen. A critical element of this theory is the involvement of lexicons, or specialized aspects of memory that include semantic and phonemic information about each item that is contained in memory. A given lexicon consists of many smaller, abstract items known as logogens. Logogens contain a variety of properties about given word such as their appearance, sound, and meaning. Logogens do not store words within themselves, but rather they store information that is specifically necessary for retrieval of whatever word is being searched for. A given logogen will become activated by psychological stimuli or contextual information (words) that is consistent with the properties of that specific logogen and when the logogen's activation level rises to or above its threshold level, the pronunciation of the given word is sent to the output system. Certain stimuli can affect the activation levels of more than one word at a time, usually involving words that are similar to one another. When this occurs, whichever of the words' activation levels reaches the threshold level, it is that word that is then sent to the output system with the subject remaining unaware of any partially excited logogens. This assumption was made by Marslen-Wilson and Welch (1978), who added to the model some assumptions of their own in order to account for their experimental results. They also assumed that the analysis of phonetic input can only become available to other parts of the system by process of how the input affects the logogen system. Finally, Marslen-Wilson and Welch assume that the first syllable of a given word will increase the activation level of a given logogen more than those of the latter syllables, which supported the data found at the time. == Analysis == The logogen model can be used to help linguists explain particular occurrences in the human language. The most-helpful application of the model is to show how one accesses words and their meanings in the lexicon. The word-frequency effect is best explained by the logogen model in that words (or logogens) that have a higher frequency (or are more common) have a lower threshold. This means that they require less perceptual power in the brain to be recognized and decoded from the lexicon and are recognized faster than those words that are less common. Also, with high-frequency words, the recovery from lowering the item's threshold is less fulfilled compared to low-frequency words so less sensory information is needed for that particular item's recognition. There are ways to lower thresholds, such as repetition and semantic priming. Also, each time a word is encountered through these methods, the threshold for that word is temporarily lowered partially because of its recovering ability. This model also conveys that specific concrete words are recalled better because they use images and logogens, whereas abstract words are not as easily recalled well because they only use logogens, hence showing the difference in thresholds between these two types of words. At the time of its conception, Morton's logogen model was one of the most influential models in springing up other parallel word access models and served as the essential basis for these subsequent models. Morton's model also strongly influenced other contemporary theories on lexical access. However, despite the advantages that the logogen theory presents, it also displays some negative facets. First and foremost, the logogen model does not explain all occurrences in language, such as the introduction of new words or non-words into a person's lexicon. Also, because of the distinctive model application, it may vary in its effectiveness in different languages. == Criticisms == While this model does a reasonable job of understanding the underlying semantics of many aspects in psycholinguistics, there are some flaws that have been pointed out in the logogen model. It has been argued that the prior stimulus patterns that have been seen in the logogen theory are not centrally localized in the logogen itself but are actually distributed throughout the different pathways over which the stimulus is being processed. What this directs at is that the notion and proliferation of logogens was due to modality. In essence, the logogen is unnecessary in the idea of attaining the title of being a recognition unit because of the variety of pathways that it is open to, not just logogens. Another criticism has been that this model essentially ignores larger and more critical structures in language and phonetics such as the different syntactic rules or grammatical construction that innately exists in language. Since this model overtly limits itself to the scope of lexical access then this model is seen as biased and misunderstood. To many psychologists, the logogen model does not meet the functional or representational adequacy that a theory should include to sufficiently comprehend language. Also, another criticism is that the logogen theory was supposed to predict that stimulus degradation should affect priming and word frequency in humans. However, many psychologists have conducted studies and researched the model to show that only priming and not word frequency is interacted with stimulus degradation. Priming is supposed to deteriorate a stimulus because it postulates that the semantic characteristics of previously known words are fed back into the detector of a person which in turn raises the threshold of related items. In word frequency, stimulus degradation is supposed to occur because it postulates that familiar words have lower thresholds than their low-frequency counterparts. However, in studies, priming is the only structure that does show observable and notable stimulus decadence. Even though the logogen theory has many unfilled holes, Morton was a revolutionary of his field whose speculation and research has opened up a remarkable era of psycholinguistics. == Other models to consider == cohort model – This model was proposed by Marslen-Wilson and was designed specifically to account for auditory word recognition. It works by breaking the word down and states that when a word is heard all words that begin with the first sound of the target word are activated. This set of words is considered the cohort. Once the first cohort has been activated, the other information, or sounds in the word narrow down the choices. The person recognizes the word when you are left with a single choice; this is considered the "recognition point". checking model – This model was developed by Norris in 1986. In this particular model, he took the approach that any word that partially matches the input is analyzed and checked to see if it fits with the context of the situation. interactive-activation model – This model is considered a connectionist model. Proposed by McClelland and Rumelhart in the 1981 to 1982 period, it is based around nodes, which are visual features, and positions of letters within a given word. They also act as word detectors which have inhibitory and excitatory connections between them. This model starts with first letter and suggests that all the words with that first letter are activated at first and then going through the word one can determine what the word is they are looking at. The main principle is that mental phenomena can be described by interconnected networks of simple units. verification model – The model was developed by Curtis Becker in 1970. The main idea is that a small number of candidates that are activated in parallel are subject to a serial-verification process. This model starts the word-recognition process with a basic representation of the stimulus. Then, sensory trace, consisting of line features is used to activate word detectors. When an acceptable number of detectors are activated these are used to generate a search set. These items are drawn from the lexicon on the basis of similarity to the sensory trace, which help with the identity of the stimulus. Then, in a serial process the candidates are compared to the representation of the sensory-trace input. == Related concepts == word frequency – This is the belief that the speed and accuracy with which a word is recognized is related to how frequently the word occurs in our language. Each logogen has a threshold (for identification) and words with higher frequencies have lower thresholds. Words with higher freq
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Pixel

In digital imaging, a pixel (abbreviated px), pel, or picture element is the smallest addressable physical element of a raster image or the smallest controllable element of a display device or dot matrix printer. Pixels are arranged in a regular, two-dimensional grid, and each pixel serves as a sample of an original image, with a greater number of samples typically providing more accurate representations. Each pixel possesses a specific intensity or color, often composed of three or four component intensities, such as red, green, and blue (RGB), or cyan, magenta, yellow, and black (CMYK). The intensity of each pixel is variable, and in color imaging systems, these components are combined to produce a wide spectrum of colors. The concept of a picture element has existed since the early days of television, appearing as "Bildpunkt" in a 1888 German patent, and the term "pixel" has been used in various U.S. patents since 1911. In most digital display devices, pixels are the smallest element that can be manipulated through software. Each pixel is a sample of an original image; more samples typically provide more accurate representations of the original. The intensity of each pixel is variable. In color imaging systems, a color is typically represented by three or four component intensities such as red, green, and blue, or cyan, magenta, yellow, and black. In some contexts (such as descriptions of camera sensors), pixel refers to a single scalar element of a multi-component representation (called a photosite in the camera sensor context, although sensel 'sensor element' is sometimes used), while in yet other contexts (like MRI) it may refer to a set of component intensities for a spatial position. Software on early consumer computers was necessarily rendered at a low resolution, with large pixels visible to the naked eye; graphics made under these limitations may be called pixel art, especially in reference to video games. Modern computers and displays, however, can easily render orders of magnitude more pixels than was previously possible, necessitating the use of large measurements like the megapixel (one million pixels). == Etymology == The word pixel is a combination of pix (from "pictures", shortened to "pics") and el (for "element"); similar formations with 'el' include the words voxel 'volume pixel', and texel 'texture pixel'. The word pix appeared in Variety magazine headlines in 1932, as an abbreviation for the word pictures, in reference to movies. By 1938, "pix" was being used in reference to still pictures by photojournalists. The word "pixel" was first published in 1965 by Frederic C. Billingsley of JPL, to describe the picture elements of scanned images from space probes to the Moon and Mars. Billingsley had learned the word from Keith E. McFarland, at the Link Division of General Precision in Palo Alto, who in turn said he did not know where it originated. McFarland said simply it was "in use at the time" (c. 1963). The concept of a "picture element" dates to the earliest days of television, for example as "Bildpunkt" (the German word for pixel, literally 'picture point') in the 1888 German patent of Paul Nipkow. According to various etymologies, the earliest publication of the term picture element itself was in Wireless World magazine in 1927, though it had been used earlier in various U.S. patents filed as early as 1911. Some authors explain pixel as picture cell, as early as 1972. In graphics and in image and video processing, pel is often used instead of pixel. For example, IBM used it in their Technical Reference for the original PC. Pixilation, spelled with a second i, is an unrelated filmmaking technique that dates to the beginnings of cinema, in which live actors are posed frame by frame and photographed to create stop-motion animation. An archaic British word meaning "possession by spirits (pixies)", the term has been used to describe the animation process since the early 1950s; various animators, including Norman McLaren and Grant Munro, are credited with popularizing it. == Technical == A pixel is generally thought of as the smallest single component of a digital image. However, the definition is highly context-sensitive. For example, there can be "printed pixels" in a page, or pixels carried by electronic signals, or represented by digital values, or pixels on a display device, or pixels in a digital camera (photosensor elements). This list is not exhaustive and, depending on context, synonyms include pel, sample, byte, bit, dot, and spot. Pixels can be used as a unit of measure such as: 2400 pixels per inch, 640 pixels per line, or spaced 10 pixels apart. The measures "dots per inch" (dpi) and "pixels per inch" (ppi) are sometimes used interchangeably, but have distinct meanings, especially for printer devices, where dpi is a measure of the printer's density of dot (e.g. ink droplet) placement. For example, a high-quality photographic image may be printed with 600 ppi on a 1200 dpi inkjet printer. Even higher dpi numbers, such as the 4800 dpi quoted by printer manufacturers since 2002, do not mean much in terms of achievable resolution. The more pixels used to represent an image, the closer the result can resemble the original. The number of pixels in an image is sometimes called the resolution, though resolution has a more specific definition. Pixel counts can be expressed as a single number, as in a "three-megapixel" digital camera, which has a nominal three million pixels, or as a pair of numbers, as in a "640 by 480 display", which has 640 pixels from side to side and 480 from top to bottom (as in a VGA display) and therefore has a total number of 640 × 480 = 307,200 pixels, or 0.3 megapixels. The pixels, or color samples, that form a digitized image (such as a JPEG file used on a web page) may or may not be in one-to-one correspondence with screen pixels, depending on how a computer displays an image. In computing, an image composed of pixels is known as a bitmapped image or a raster image. The word raster originates from television scanning patterns, and has been widely used to describe similar halftone printing and storage techniques. === Sampling patterns === For convenience, pixels are normally arranged in a regular two-dimensional grid. By using this arrangement, many common operations can be implemented by uniformly applying the same operation to each pixel independently. Other arrangements of pixels are possible, with some sampling patterns even changing the shape (or kernel) of each pixel across the image. For this reason, care must be taken when acquiring an image on one device and displaying it on another, or when converting image data from one pixel format to another. For example: Liquid-crystal displays (LCDs) typically use a staggered grid, where the red, green, and blue components are sampled at slightly different locations. Subpixel rendering is a technology which takes advantage of these differences to improve the rendering of text on LCD screens. The vast majority of color digital cameras use a Bayer filter, resulting in a regular grid of pixels where the color of each pixel depends on its position on the grid. A clipmap uses a hierarchical sampling pattern, where the size of the support of each pixel depends on its location within the hierarchy. Warped grids are used when the underlying geometry is non-planar, such as images of the earth from space. The use of non-uniform grids is an active research area, attempting to bypass the traditional Nyquist limit. Pixels on computer monitors are normally "square" (that is, have equal horizontal and vertical sampling pitch); pixels in other systems are often "rectangular" (that is, have unequal horizontal and vertical sampling pitch – oblong in shape), as are digital video formats with diverse aspect ratios, such as the anamorphic widescreen formats of the Rec. 601 digital video standard. === Resolution of computer monitors === Computer monitors (and TV sets) generally have a fixed native resolution. What it is depends on the monitor, and size. See below for historical exceptions. Computers can use pixels to display an image, often an abstract image that represents a GUI. The resolution of this image is called the display resolution and is determined by the video card of the computer. Flat-panel monitors (and TV sets), e.g. OLED or LCD monitors, or E-ink, also use pixels to display an image, and have a native resolution, and it should (ideally) be matched to the video card resolution. Each pixel is made up of triads, with the number of these triads determining the native resolution. On older, historically available, CRT monitors the resolution was possibly adjustable (still lower than what modern monitor achieve), while on some such monitors (or TV sets) the beam sweep rate was fixed, resulting in a fixed native resolution. Most CRT monitors do not have a fixed beam sweep rate, meaning they do not have a native resolution at all – instead they
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Comparison of video editing software

This is a comparison of non-linear video editing software applications. See also a more complete list of video editing software. == General information == This table gives basic general information about the different editors: === Active === === Discontinued / Inactive === ==== Definition ==== professional: used for full length Hollywood movies; professional (small): mainly used for paid commercials, short films or podcasts/YouTube channels; prosumer: Mainly targeting private use, anything that can do more than just trimming a film; basic: trimming a film; == System requirements == This table lists the operating systems that different editors can run on without emulation, as well as other system requirements. Note that minimum system requirements are listed; some features (like High Definition support) may be unavailable with these specifications. "Unix" includes the similar Linux, BSD and Unix-like operating systems. == High definition/High resolution import == The table below indicates the ability of each program to import various High Definition video or High resolution video formats for editing. == Feature set == == Output options == Please note that recording to Blu-ray does not imply 1080@50p/60p . Most only support up to 1080i 25/30 frames per second recording. Also not all formats can be output.
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Artifact (app)

Artifact was a personalized social news aggregator app that uses recommender systems to suggest articles. Launched in January 2023 by Nokto, Inc., a company founded by co-founders of Instagram Kevin Systrom and Mike Krieger, the app is available for iOS and Android. The app's name is a portmanteau of the words "articles", "artificial intelligence", and "fact". The app shut down in January 2024 as a result of low interest. == History == Nokto, Inc. was established on March 3, 2022, as a foreign stock company in California, with its headquarters in San Francisco. The company's main product, Artifact, is the first new product launched by Krieger and Systrom since their 2018 resignation from Instagram after conflicts with parent company Meta, which acquired Instagram in 2012. Artifact launched on January 31, 2023, after the team had been working on it for over a year, offering the option to sign up for a waiting list for its private beta, which grew to about 160,000 people, and then launching in open beta on February 22, 2023. With a team of seven employees in San Francisco, the app was free throughout its lifetime, with the founders explaining at the time that different business models - such as advertising or subscription fees - could be explored in the future. In January 2024, cofounder Kevin Systrom announced that the app would be shutting down after concluding that "the market opportunity isn’t big enough to warrant continued investment in this way." In April 2024, it was announced Artifact had been acquired by Yahoo, who intended to use the service's technology in an upgraded Yahoo! News app. == Features == Frequently described as "TikTok for text" and a competitor to Twitter, Artifact was a news aggregator that used machine learning to make personalized recommendations based on topics, news sources, and authors that the reader is interested in. In addition to reading articles, the app offered the ability to like articles, leave comments, or listen to an audio version of an article read by AI-generated voices, including a simulation of the voices of Snoop Dogg or Gwyneth Paltrow. AI also would rewrite clickbait headlines that users flagged. Artifact later expanded to a social network where users could post links, images and text to their profile, which could be liked or commented on by other users. Similar to other social news websites like Reddit, reader accounts had profiles with reputation scores.
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Artifact (app)

Artifact was a personalized social news aggregator app that uses recommender systems to suggest articles. Launched in January 2023 by Nokto, Inc., a company founded by co-founders of Instagram Kevin Systrom and Mike Krieger, the app is available for iOS and Android. The app's name is a portmanteau of the words "articles", "artificial intelligence", and "fact". The app shut down in January 2024 as a result of low interest. == History == Nokto, Inc. was established on March 3, 2022, as a foreign stock company in California, with its headquarters in San Francisco. The company's main product, Artifact, is the first new product launched by Krieger and Systrom since their 2018 resignation from Instagram after conflicts with parent company Meta, which acquired Instagram in 2012. Artifact launched on January 31, 2023, after the team had been working on it for over a year, offering the option to sign up for a waiting list for its private beta, which grew to about 160,000 people, and then launching in open beta on February 22, 2023. With a team of seven employees in San Francisco, the app was free throughout its lifetime, with the founders explaining at the time that different business models - such as advertising or subscription fees - could be explored in the future. In January 2024, cofounder Kevin Systrom announced that the app would be shutting down after concluding that "the market opportunity isn’t big enough to warrant continued investment in this way." In April 2024, it was announced Artifact had been acquired by Yahoo, who intended to use the service's technology in an upgraded Yahoo! News app. == Features == Frequently described as "TikTok for text" and a competitor to Twitter, Artifact was a news aggregator that used machine learning to make personalized recommendations based on topics, news sources, and authors that the reader is interested in. In addition to reading articles, the app offered the ability to like articles, leave comments, or listen to an audio version of an article read by AI-generated voices, including a simulation of the voices of Snoop Dogg or Gwyneth Paltrow. AI also would rewrite clickbait headlines that users flagged. Artifact later expanded to a social network where users could post links, images and text to their profile, which could be liked or commented on by other users. Similar to other social news websites like Reddit, reader accounts had profiles with reputation scores.
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Abillion

abillion was a mobile application helping users to find vegan and sustainable products. The platform allowed users to review plant-based, cruelty-free and sustainable products, while donating between 0.10 and $1 to nonprofit organisations for each review written. As of May 2023, the company claimed to have donated over $2.8M to various nonprofit organisations including Sea Shepherd and Mercy for Animals. The main objective of the company was to reach the number of one billion people following a vegan diet and lifestyle by 2030. == History == The American entrepreneur Vikas Garg founded the company in Singapore and the app was officially launched in May 2018. The start-up was first named 'abillionveg' and changed its name in 2020 to shorten it to 'abillion'. In 2019, the company raised $3M in its first round of funding (pre-Series A). In 2021, it raised $10M in its Series A funding. In February 2023, the company announced the launch of a community investment round, using the crowdfunding platform Wefunder, which reached a total of $500 000. In May 2023, it celebrated its 5th anniversary and reaching 1M downloads. In March 2026, the company announced that they would be closing down by the end of the month. == Awards == Using data from the reviews published by its users, abillion was awarding the most liked vegan products and brands. In May 2023, the company published a world Top 10 Best Plant Based Burgers, among the winning brands were Beyond Meat, NotCo and Sojasun.
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