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Non-native speech database

A non-native speech database is a speech database of non-native pronunciations of English. Such databases are used in the development of: multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers, and second language learning systems. == List == The actual table with information about the different databases is shown in Table 2. === Legend === In the table of non-native databases some abbreviations for language names are used. They are listed in Table 1. Table 2 gives the following information about each corpus: The name of the corpus, the institution where the corpus can be obtained, or at least further information should be available, the language which was actually spoken by the speakers, the number of speakers, the native language of the speakers, the total amount of non-native utterances the corpus contains, the duration in hours of the non-native part, the date of the first public reference to this corpus, some free text highlighting special aspects of this database and a reference to another publication. The reference in the last field is in most cases to the paper which is especially devoted to describe this corpus by the original collectors. In some cases it was not possible to identify such a paper. In these cases a paper is referenced which is using this corpus is. Some entries are left blank and others are marked with unknown. The difference here is that blank entries refer to attributes where the value is just not known. Unknown entries, however, indicate that no information about this attribute is available in the database itself. As an example, in the Jupiter weather database no information about the origin of the speakers is given. Therefore this data would be less useful for verifying accent detection or similar issues. Where possible, the name is a standard name of the corpus, for some of the smaller corpora, however, there was no established name and hence an identifier had to be created. In such cases, a combination of the institution and the collector of the database is used. In the case where the databases contain native and non-native speech, only attributes of the non-native part of the corpus are listed. Most of the corpora are collections of read speech. If the corpus instead consists either partly or completely of spontaneous utterances, this is mentioned in the Specials column.
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AirDine

AirDine was a mobile app within the platform economy where individuals acted as both supplier and customer for a supper club. AirDine discontinued their service after 31 October 2017. == Operations == AirDine was an online marketplace for home dining that connected users that liked to cook with users looking for a dining experience. Users were categorized as "Hosts" and "Guests," both of whom needed to register with AirDine. AirDine acted as a two-sided market for home dining that allowed hosts and guests, and did not act as a restaurant or host any dinners itself. AirDine charged a service fee. Security and safety of the host were not vetted by AirDine and were completely left to users based on published reviews. Profiles included user reviews and shared social connections to build trust among users. AirDine also included a private messaging system.
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Data commingling

Data commingling, in computer science, occurs when different items or kinds of data are stored in such a way that they become commonly accessible when they are supposed to remain separated. In cloud computing, this can occur where different customer data sits on the same server. Data that is commingled can present a security vulnerability. Data commingling can also occur due to high speed data transmission mixing. In this situation, data of one security level can inadvertently or purposely be mixed with data of a lower or higher security level on the same transmission portal. Portal vehicles can be wire, fiber optics, microwave or various radio frequency transmission portals. This commingling can cause breaches of security and become a source of legal issues to any entity, corporation or individual. Data commingling can also occur when personal computers and personal software programs are used for business, security, government, etc. uses. In the early formulation stages of entities, non-profit or profit corporations, LLC's, LLP's, etc., the creation and use of stand-alone computers and stand-alone networks, "absolutely unconnected" to involved individuals, is the easiest, and safest way to prevent Data Commingling.
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Z-order

Z-order is an ordering of overlapping two-dimensional objects, such as windows in a stacking window manager, shapes in a vector graphics editor, or objects in a 3D application. One of the features of a typical GUI is that windows may overlap, so that one window hides part or all of another. When two windows overlap, their Z-order determines which one appears on top of the other. == Definition == The term "Z-order" refers to the order of objects along the Z-axis. In coordinate geometry, X typically refers to the horizontal axis (left to right), Y to the vertical axis (up and down), and Z refers to the axis perpendicular to the other two (forward or backward). One can think of the windows in a GUI as a series of planes parallel to the surface of the monitor. The windows are therefore stacked along the Z-axis, and the Z-order information thus specifies the front-to-back ordering of the windows on the screen. An analogy would be some sheets of paper scattered on top of a table, each sheet being a window, the table your computer screen, and the top sheet having the highest Z value. == Use == Typically, users of a GUI can affect the Z-order by selecting a window to be brought to the foreground (that is, "above" or "in front of" all the other windows). Some window managers allow interaction with windows while they are not in the foreground, while others will bring a window to the front whenever it receives input from the user. It is also possible for special windows to be designated "always on top"; these are then fixed to the top of the Z-order so that (with few exceptions) no other window can overlap them. When dealing with visual objects on a computer screen, an object with a Z-order of 1 would be visually "underneath" an object with a Z-order of 2 or greater. This is the same as making "layers" of objects where the Z-order determines what object is on top of another. An HTML page can use CSS to specify the Z-order so that some objects can be layered over others. Z-ordering is also used in 3D applications to determine object visibility based on overlap from other objects. This confers a speed advantage to the user as the computer does not need to render unseen objects. In practice, of course, some objects may be only partially obscured, and this is a complication that must be taken into account. In early real-time 3D graphics, Z-order was applied on a per-polygon basis to avoid using Z-buffer, which was considered expensive at the time. In modern 3D graphics, Z-order is used for order-dependent rendering, for example with semi-transparent objects. It can also be used to reduce the problem of Z-fighting, by either rendering farther objects first and then using weak inequality as the depth test or, conversely, rendering front-to-back and using strict inequality. == z-index == The actual number assigned to a particular place in the Z-order is sometimes known as the z-index. In particular the CSS property that sets the stack order of specific elements is known as the z-index. An element with greater stack order is always in front of another element with lower stack order. Negative values can also be used in the same manner. A negative value will appear behind a positive one. z-index only works on elements that have a position value (e.g. position: relative;) and for many coders, this one of the first things to investigate when debugging why the z-index isn't working. Like all other CSS properties, it can be set with JavaScript, with the following syntax:
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Jaggaer

JAGGAER, formerly SciQuest, is a provider of cloud-based business automation technology for Business Spend Management. Its headquarters is in Durham, North Carolina. == Company history == SciQuest was established in 1995 as a B2B eCommerce exchange.The company went public with an IPO in 1999. In 2001, SciQuest transitioned from a B2B exchange company into eProcurement software and supplier enablement platforms. SciQuest was taken private in 2004 and continued to move into eProcurement, inventory management and accounts payable automation. SciQuest completed an IPO in September 2010, raising approximately $57 million. SciQuest, and its 510 person workforce, was taken private in June 2016 as part of a $509 million acquisition by Accel-KKR, a private equity firm headquartered in Menlo Park, CA. In 2017 SciQuest was rebranded as JAGGAER and announced increased focus on offering a complete, integrated source-to-pay suite. Along with the name change, the company expanded its market focus to manufacturing, healthcare, consumer packaged goods, retail, education, life sciences, logistics and the public sector. JAGGAER acquired the European direct materials procurement specialist Pool4Tool in June 2017 giving it end-to-end direct as well as indirect materials procurement coverage. JAGGAER acquired spend management company BravoSolution in 2017, and entered into a joint venture with United Arab Emirates-based Tejari. In February 2019 JAGGAER launched JAGGAER One, which unifies its full product suite on a single platform. In 2019 the UK-based private equity firm Cinven acquired a majority holding in the company. Jim Bureau was subsequently named JAGGAER's Chief Executive Officer. Bureau left the firm in March 2023, and Andy Hovancik was announced as the company's CEO in June. In 2024, JAGGAER was acquired by Vista Equity Partners, a private equity firm specializing in enterprise software investments. == Current positioning == As of April 2025, JAGGAER positions itself as "an enterprise procurement and supplier collaboration SaaS provider." Its core technology platform, which is called JAGGAER One, serves "direct and indirect procurement with specializations in Higher Education, Discrete and Process Manufacturing, and Public Sector." == Product Categories == The JAGGAER One platform supports the following products: Spend Analytics Category Management Supplier Management Sourcing Contracts eProcurement Invoicing Inventory Management Supply Chain Collaboration Quality Management == Acquisitions == SciQuest acquired the following companies: AECsoft - January 2011. Provider of supplier management and sourcing technology. Upside Software, Inc. - August 2012. Provider of contract lifecycle management (CLM) solutions. Spend Radar, LLC - October 2012, Provider of spend analysis software. CombineNet - September 2013, Provider of advanced sourcing software JAGGAER acquired the following companies: POOL4TOOL - June 2017, Provider of direct sourcing and supply chain management software BravoSolution - December 2017, Provider of global platform spend management solutions
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Data commingling

Data commingling, in computer science, occurs when different items or kinds of data are stored in such a way that they become commonly accessible when they are supposed to remain separated. In cloud computing, this can occur where different customer data sits on the same server. Data that is commingled can present a security vulnerability. Data commingling can also occur due to high speed data transmission mixing. In this situation, data of one security level can inadvertently or purposely be mixed with data of a lower or higher security level on the same transmission portal. Portal vehicles can be wire, fiber optics, microwave or various radio frequency transmission portals. This commingling can cause breaches of security and become a source of legal issues to any entity, corporation or individual. Data commingling can also occur when personal computers and personal software programs are used for business, security, government, etc. uses. In the early formulation stages of entities, non-profit or profit corporations, LLC's, LLP's, etc., the creation and use of stand-alone computers and stand-alone networks, "absolutely unconnected" to involved individuals, is the easiest, and safest way to prevent Data Commingling.
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Data remanence

Data remanence is the residual representation of digital data that remains even after attempts have been made to remove or erase the data. This residue may result from data being left intact by a nominal file deletion operation, by reformatting of storage media that does not remove data previously written to the media, or through physical properties of the storage media that allow previously written data to be recovered. Data remanence may make inadvertent disclosure of sensitive information possible should the storage media be released into an uncontrolled environment (e.g., thrown in refuse containers or lost). Various techniques have been developed to counter data remanence. These techniques are classified as clearing, purging/sanitizing, or destruction. Specific methods include overwriting, degaussing, encryption, and media destruction. Effective application of countermeasures can be complicated by several factors, including media that are inaccessible, media that cannot effectively be erased, advanced storage systems that maintain histories of data throughout the data's life cycle, and persistence of data in memory that is typically considered volatile. Several standards exist for the secure removal of data and the elimination of data remanence. == Causes == Many operating systems, file managers, and other software provide a facility where a file is not immediately deleted when the user requests that action. Instead, the file is moved to a holding area (i.e. the "trash"), making it easy for the user to undo a mistake. Similarly, many software products automatically create backup copies of files that are being edited, to allow the user to restore the original version, or to recover from a possible crash (autosave feature). Even when an explicit deleted file retention facility is not provided or when the user does not use it, operating systems do not actually remove the contents of a file when it is deleted unless they are aware that explicit erasure commands are required, like on a solid-state drive. (In such cases, the operating system will issue the Serial ATA TRIM command or the SCSI UNMAP command to let the drive know to no longer maintain the deleted data.) Instead, they simply remove the file's entry from the file system directory because this requires less work and is therefore faster, and the contents of the file—the actual data—remain on the storage medium. The data will remain there until the operating system reuses the space for new data. In some systems, enough filesystem metadata are also left behind to enable easy undeletion by commonly available utility software. Even when undelete has become impossible, the data, until it has been overwritten, can be read by software that reads disk sectors directly. Computer forensics often employs such software. Likewise, reformatting, repartitioning, or reimaging a system is unlikely to write to every area of the disk, though all will cause the disk to appear empty or, in the case of reimaging, empty except for the files present in the image, to most software. Finally, even when the storage media is overwritten, physical properties of the media may permit recovery of the previous contents. In most cases however, this recovery is not possible by just reading from the storage device in the usual way, but requires using laboratory techniques such as disassembling the device and directly accessing/reading from its components. § Complications below gives further explanations for causes of data remanence. == Countermeasures == There are three levels commonly recognized for eliminating remnant data: === Clearing === Clearing is the removal of sensitive data from storage devices in such a way that there is assurance that the data may not be reconstructed using normal system functions or software file/data recovery utilities. The data may still be recoverable, but not without special laboratory techniques. Clearing is typically an administrative protection against accidental disclosure within an organization. For example, before a hard drive is re-used within an organization, its contents may be cleared to prevent their accidental disclosure to the next user. === Purging === Purging or sanitizing is the physical rewrite of sensitive data from a system or storage device done with the specific intent of rendering the data unrecoverable at a later time. Purging, proportional to the sensitivity of the data, is generally done before releasing media beyond control, such as before discarding old media, or moving media to a computer with different security requirements. === Destruction === The storage media is made unusable for conventional equipment. Effectiveness of destroying the media varies by medium and method. Depending on recording density of the media, and/or the destruction technique, this may leave data recoverable by laboratory methods. Conversely, destruction using appropriate techniques is the most secure method of preventing retrieval. == Specific methods == === Overwriting === A common method used to counter data remanence is to overwrite the storage media with new data. This is often called wiping or shredding a disk or file, by analogy to common methods of destroying print media, although the mechanism bears no similarity to these. Because such a method can often be implemented in software alone, and may be able to selectively target only part of the media, it is a popular, low-cost option for some applications. Overwriting is generally an acceptable method of clearing, as long as the media is writable and not damaged. The simplest overwrite technique writes the same data everywhere—often just a pattern of all zeros. At a minimum, this will prevent the data from being retrieved simply by reading from the media again using standard system functions. The UEFI in modern machines may offer an ATA class disk erase function as well. The ATA-6 standard governs secure erases specifications. Bitlocker is whole disk encryption and illegible without the key. Writing a fresh GPT allows a new file system to be established. Blocks will set empty but LBA read is illegible. New data will be unaffected and work fine. In an attempt to counter more advanced data recovery techniques, specific overwrite patterns and multiple passes have often been prescribed. These may be generic patterns intended to eradicate any trace signatures; an example is the seven-pass pattern 0xF6, 0x00, 0xFF, , 0x00, 0xFF, , sometimes erroneously attributed to US standard DOD 5220.22-M. One challenge with overwriting is that some areas of the disk may be inaccessible, due to media degradation or other errors. Software overwrite may also be problematic in high-security environments, which require stronger controls on data commingling than can be provided by the software in use. The use of advanced storage technologies may also make file-based overwrite ineffective (see the related discussion below under § Complications). There are specialized machines and software that are capable of doing overwriting. The software can sometimes be a standalone operating system specifically designed for data destruction. There are also machines specifically designed to wipe hard drives to the department of defense specifications DOD 5220.22-M. Writing zero to each block on hard disks and SSDs has the advantage of affording the firmware to deploy spare blocks when bad blocks are identified. Bitlocker has the advantage that data is illegible without the key. Seatools and other tools can erase disks with zero which is typical to revive old consumer class disks but they can wipe server disks albeit slowly. Modern 28TB and larger disks have an enormous number of LBA48 blocks. 40TB and 60TB disks will take proportionately longer times to wipe. ==== Feasibility of recovering overwritten data ==== Peter Gutmann investigated data recovery from nominally overwritten media in the mid-1990s. He suggested magnetic force microscopy may be able to recover such data, and developed specific patterns, for specific drive technologies, designed to counter such. These patterns have come to be known as the Gutmann method. Gutmann's belief in the possibility of data recovery is based on many questionable assumptions and factual errors that indicate a low level of understanding of how hard drives work. Daniel Feenberg, an economist at the private National Bureau of Economic Research, claims that the chances of overwritten data being recovered from a modern hard drive amount to "urban legend". He also points to the "18+1⁄2-minute gap" Rose Mary Woods created on a tape of Richard Nixon discussing the Watergate break-in. Erased information in the gap has not been recovered, and Feenberg claims doing so would be an easy task compared to recovery of a modern high density digital signal. As of November 2007, the United States Department of Defense considers overwriting acceptable for clearing magnetic media within the same security area/
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Computer Law & Security Review

The Computer Law & Security Review is an international peer-reviewed journal published by Elsevier. It has been published six times a year since 1985 and is indexed in Scopus and SSCI. It is accessible to a wide range of professional legal and IT practitioners, businesses, academics, researchers, libraries and organisations in both the public and private sectors. The journal regularly covers: CLSR Briefing with special emphasis on UK/US developments European Union update National news from 10 European jurisdictions Pacific rim news column Refereed practitioner and academic papers on topics such as Web 2.0, IT security, Identity management, ID cards, RFID, interference with privacy, Internet law, telecoms regulation, online broadcasting, intellectual property, software law, e-commerce, outsourcing, data protection and freedom of information and many other topics. The Journal's Correspondent Panel includes more than 40 specialists in IT law and security. Each issue contains articles, case law analysis and current news on information and communications technology. Special Features High quality peer reviewed papers from internationally renowned practitioner and academic experts Latest developments reported in situ by more than 20 leading law firms from around the world Highly experienced and respected editor and correspondents panel Online access to all 23 volumes of CLSR with embedded web links to primary sources Contact details of all authors A pool of expertise that can collectively identify the key topics that need to be examined.
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Expectation propagation

Expectation propagation (EP) is a technique in Bayesian machine learning. EP finds approximations to a probability distribution. It uses an iterative approach that uses the factorization structure of the target distribution. It differs from other Bayesian approximation approaches such as variational Bayesian methods. More specifically, suppose we wish to approximate an intractable probability distribution p ( x ) {\displaystyle p(\mathbf {x} )} with a tractable distribution q ( x ) {\displaystyle q(\mathbf {x} )} . Expectation propagation achieves this approximation by minimizing the Kullback–Leibler divergence K L ( p | | q ) {\displaystyle \mathrm {KL} (p||q)} . Variational Bayesian methods minimize K L ( q | | p ) {\displaystyle \mathrm {KL} (q||p)} instead. If q ( x ) {\displaystyle q(\mathbf {x} )} is a Gaussian N ( x | μ , Σ ) {\displaystyle {\mathcal {N}}(\mathbf {x} |\mu ,\Sigma )} , then K L ( p | | q ) {\displaystyle \mathrm {KL} (p||q)} is minimized with μ {\displaystyle \mu } and Σ {\displaystyle \Sigma } being equal to the mean of p ( x ) {\displaystyle p(\mathbf {x} )} and the covariance of p ( x ) {\displaystyle p(\mathbf {x} )} , respectively; this is called moment matching. == Applications == Expectation propagation via moment matching plays a vital role in approximation for indicator functions that appear when deriving the message passing equations for TrueSkill.
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Interlacing (bitmaps)

In computing, interlacing (also known as interleaving) is a method of encoding a bitmap image such that a person who has partially received it sees a degraded copy of the entire image. When communicating over a slow communications link, this is often preferable to seeing a perfectly clear copy of one part of the image, as it helps the viewer decide more quickly whether to abort or continue the transmission. Interlacing is supported by the following formats, where it is optional: GIF interlacing stores the lines in the order 0 , 8 , 16 , … , ( 8 n ) , 4 , 12 , … , ( 8 n + 4 ) , 2 , 6 , 10 , 14 , … , ( 4 n + 2 ) , 1 , 3 , 5 , 7 , 9 , … , ( 2 n + 1 ) . {\displaystyle 0,8,16,\dots ,(8n),\ 4,12,\dots ,(8n+4),\ 2,6,10,14,\dots ,(4n+2),\ 1,3,5,7,9,\dots ,(2n+1).} PNG uses the Adam7 algorithm, which interlaces in both the vertical and horizontal direction. TGA uses two optional interlacing algorithms: Two-way: 0 , 2 , 4 , … , ( 2 n ) , 1 , 3 , … , ( 2 n + 1 ) , {\displaystyle 0,2,4,\dots ,(2n),\ 1,3,\dots ,(2n+1),} And four-way: 0 , 4 , 8 , … , ( 4 n ) , 1 , 5 , … , ( 4 n + 1 ) , 2 , 6 , … , ( 4 n + 2 ) , 3 , 7 , … , ( 4 n + 3 ) . {\displaystyle 0,4,8,\dots ,(4n),\ 1,5,\dots ,(4n+1),\ 2,6,\dots ,\ (4n+2),3,7,\dots ,(4n+3).} JPEG, JPEG 2000, and JPEG XR (actually using a frequency decomposition hierarchy rather than interlacing of pixel values) PGF (also using a frequency decomposition) Interlacing is a form of incremental decoding, because the image can be loaded incrementally. Another form of incremental decoding is progressive scan. In progressive scan the loaded image is decoded line for line, so instead of becoming incrementally clearer it becomes incrementally larger. The main difference between the interlace concept in bitmaps and in video is that even progressive bitmaps can be loaded over multiple frames. For example: Interlaced GIF is a GIF image that seems to arrive on your display like an image coming through a slowly opening Venetian blind. A fuzzy outline of an image is gradually replaced by seven successive waves of bit streams that fill in the missing lines until the image arrives at its full resolution. Interlaced graphics were once widely used in web design and before that in the distribution of graphics files over bulletin board systems and other low-speed communications methods. The practice is much less common today, as common broadband internet connections allow most images to be downloaded to the user's screen nearly instantaneously, and interlacing is usually an inefficient method of encoding images. Interlacing has been criticized because it may not be clear to viewers when the image has finished rendering, unlike non-interlaced rendering, where progress is apparent (remaining data appears as blank). Also, the benefits of interlacing to those on low-speed connections may be outweighed by having to download a larger file, as interlaced images typically do not compress as well.
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RockMyRun

Rock My Run (stylized as RockMyRun; trademarked slogan: "The Best Running Music in the World™") is a mobile running/fitness app founded in 2011 that provides running and workout music in the form of DJ mixes. It is owned by Rock My World, Inc., a health and fitness technology company based in San Diego, California. The app allows users to listen to these professional DJ mixes on their smartphone while running or working out to enhance and motivate their performance. Rock My World, Inc. also developed the app Jolt.ai for the software Slack. == History == During the early stages of the company, Rock My World, Inc. raised more than $2 million in funding generated by the Irvine Company's The Vine SD and from institutional investors including Skullcandy, ZTE and Lighter Capital and were admitted to the Plug and Play Tech Center in Sunnyvale and to the tech incubator EvoNexus in San Diego. In an interview with co-founder and ex-Qualcomm staff Adam Riggs-Zeigen, he said that "from the beginning [their] big goal is to help people live healthier lives." == Features == The RockMyRun app contains thousands of mixes or "stations" produced by its professional DJs intended to increase enjoyment and performance during exercise. DJs who have provided mixes for the app include David Guetta, Zedd, Steve Aoki, Major Lazer and Afrojack. All of the music can be personalized based on the user's steps per minute, heart rate or ideal cadence allowing the user to "always hear the right music at the right time at the right tempo". All RockMyRun mixes are organized into stations to help users discover music that suits their needs. RockMyRun contains mixes of all genres and each station is categorized into their respective genres and displays tags to let users know the type of music contained in the mix. RockMyRun has two membership types; it is free as a standard member, but for uninterrupted listening and additional features, users can upgrade to a paid "Rockstar" membership. Since March 2023, couples can now be on the same RockMyRun playlists and "share" earbuds. This allows people to train together, easier. A group of DJs curate playlists for specific training needs and different energy levels. == Reception == RockMyRun has been featured on television programs such as The Today Show on two occasions and on The Rachael Ray Show, and in positive reviews by many publications and websites including The New York Times on four separate occasions, TIME, The Huffington Post, The Denver Post, Men's Fitness, Real Simple, The Vulcan Post, The L.A. Times, Glamour, Paste magazine, PCMag, Dubai Week, BetaNews, CNET, CNBC, Reuters, Insider, Tom's Guide and Yahoo! Tech. RockMyRun has also been mentioned/recommended in books/publications such as A Practical Guide to Teacher Wellbeing by Elizabeth Holmes and Applying Music in Exercise and Sport by Dr. Costas Karageorghis. Ultimate Ears placed RockMyRun at the top of their list at No. 1 on their "5 Favorite Workout Music Apps". In a positive review by David Strausser for AndroidGuys in 2015, he praised the app in a detailed review, saying "The mixes are incredible and the rates are reasonable. The app is quick, beautiful." In 2015, Jill Duffy of PC Magazine gave a review of the app, pointing out its key features, and stating that the app is great if you enjoy listening to different, or new music, that can match your tempo while running. Also in 2015, Digital Trends listed RockMyRun, as one of the best exercise music apps in the article "No need to make exercise playlists with these music apps". In 2018, Redbull.com recommended RockMyRun in preparation for the Wings for Life World Run in their article "10 essential hacks for running to work to get you in World Run shape". In 2019, The Fashion Spot included RockMyRun in their list of "The Best Workout Apps for People Who Hate to Work Out", saying: "RockMyRun matches music to the tempo of your running pace – the music literally follows your steps/heart rate. The app has thousands of mixes/music options along with tracking capabilities." Also in 2019, MakeUseOf.com included RockMyRun in their list of "The 7 Best Running and Workout Music Apps". In September 2022, VeryWellFit listed RockMyRun as the first of three "Other Playlist Options" in the article "How to Create a Running Playlist, According to Running Coaches". Tech Grapple recommended the app in "The best workout free music apps for iPhone and Android" saying that "RockMyRun is the best application that you can use during workout. It comes with amazing DJs to craft mixes that will keep you moving." == Partners == RockMyRun is partnered with the following brands/companies: C25K Del Taco JLab Audio iFit Active Network, LLC Night Nation Run (the world's first running music festival) Lady Foot Locker Mayweather Boxing + Fitness Mio Global Orangetheory Fitness Red Rock Apps Tapout Fitness
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MyChild App

MyChild App is an Android app that helps parents screen developmental disorders in their children between the age of 1 and 24 months. The app contains information for parents about the different stages of a child's development. == Background == Launched in 2015 on Google PlayStore, the app is a consumer product of the parent company, Time Ahead, Inc. Its office is based in Bhopal, Madhya Pradesh, India. As of August 2016, the app had been downloaded by 11,000+ users in 140+ countries and is a part of fbstart case study. == Funding == In 2015, MyChild App raised a seed round of $100k led by 500 Startups, followed by angel investors Samir Bangara, Anisha Mittal, Pallav Nadhani, Deobrat Singh, Lalit Mangal, Arihant Patni, Amit Gupta, Dr. Ritesh Malik, Saurab Paruthi, and Singapore Angel Network.
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Facial age estimation

Facial age estimation is the use of artificial intelligence to estimate the age of a person based on their facial features. Computer vision techniques are used to analyse the facial features in the images of millions of people whose age is known and then deep learning is used to create an algorithm that tries to predict the age of an unknown person. The key use of the technology is to prevent access to age-restricted goods and services. Examples include restricting children from accessing internet pornography, checking that they meet a mandatory minimum age when registering for an account on social media, or preventing adults from accessing websites, online chat or games designed only for use by children. The technology is distinct from facial recognition systems as the software does not attempt to uniquely identify the individual. Researchers have applied neural networks for age estimation since at least 2010. == Evaluation == An ongoing study by the National Institute of Standards and Technology (NIST) entitled 'Face Analysis Technology Evaluation' seeks to establish the technical performance of prototype age estimation algorithms submitted by academic teams and software vendors including Brno University of Technology, Czech Technical University in Prague, Dermalog, IDEMIA, Incode Technologies Inc, Jumio, Nominder, Rank One Computing, Unissey and Yoti. == Public sector use == The UK government has explored using facial age estimation at the UK border as an alternative to bone X-rays and MRI scans when determining child status of asylum seekers. == Commercial use == Commercial users of facial age estimation include Instagram and OnlyFans. In January 2025, John Lewis & Partners announced that had started using the technology to check the age of people shopping for knives on its website, to comply with UK legislation to limit knife crime. In the UK, several supermarket chains have taken part in Home Office trials of the technology to automate the checking of a customer's age when buying age-restricted goods such as alcohol. UK legislation introduced in January 2025 mandates robust forms of age verification hosting adult content viewable in the UK by July 2025. Allowable methods include facial age estimation. == Criticism == Adam Schwartz, a lawyer for the Electronic Frontier Foundation, criticized the use of facial age estimation software, noting its inaccuracy especially in cases of minorities and women, as was found in NIST's 2024 report. Twenty organisations jointly under European Digital Rights called the practice a "systematic and invasive processing of young people's data" that risks discriminatory profiling.
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Display list

A display list, also called a command list in Direct3D 12 and a command buffer in Vulkan, is a series of graphics commands or instructions that are run when the list is executed. Systems that make use of display list functionality are called retained mode systems, while systems that do not are as opposed to immediate mode systems. In OpenGL, display lists are useful to redraw the same geometry or apply a set of state changes multiple times. This benefit is also used with Direct3D 12's bundle command lists. In Direct3D 12 and Vulkan, display lists are regularly used for per-frame recording and execution. == Origins in vector displays == The vector monitors or calligraphic displays of the 1960s and 1970s used electron beam deflection to draw line segments, points, and sometimes curves directly on a CRT screen. Because the image would immediately fade, it needed to be redrawn many times a second (storage tube CRTs retained the image until blanked, but they were unsuitable for interactive graphics). To refresh the display, a dedicated CPU called a Display Processor or Display Processing Unit (DPU) was used, which had a memory buffer for a "display list", "display file", or "display program" containing line segment coordinates and other information. Advanced Display Processors also supported control flow instructions, which were useful for drawing repetitive graphics such as text, and some could perform coordinate transformations such as 3D projection. == Home computer display list functionality == One of the earliest systems with a true display list was the Atari 8-bit computers. The display list (actually called so in Atari terminology) is a series of instructions for ANTIC, the video co-processor used in these machines. This program, stored in the computer's memory and executed by ANTIC in real-time, can specify blank lines, any of six text modes and eight graphics modes, which sections of the screen can be horizontally or vertically fine-scrolled, and trigger Display List Interrupts (called raster interrupts or HBI on other systems). The Amstrad PCW family contains a Display List function called the 'Roller RAM'. This is a 512-byte RAM area consisting of 256 16-bit pointers in RAM, one for each line of the 720 × 256 pixel display. Each pointer identifies the location of 90 bytes of monochrome pixels that hold the line's 720 pixel states. The 90 bytes of 8 pixel states are spaced at 8-byte intervals, so there are 7 unused bytes between each byte of pixel data. This suits how the text-orientated PCW constructs a typical screen buffer in RAM, where the first character's 8 rows are stored in the first 8 bytes, the second character's rows in the next 8 bytes, and so on. The Roller RAM was implemented to speed up display scrolling as it would have been unacceptably slow for its 3.4 MHz Z80 to move up the 23 KB display buffer 'by hand' i.e. in software. The Roller RAM starting entry used at the beginning of a screen refresh is controlled by a Z80-writable I/O register. Therefore, the screen can be scrolled simply by changing this I/O register. Another system using a Display List-like feature in hardware is the Amiga, which, not coincidentally, was also designed by some of the same people who developed the custom hardware for the Atari 8-bit computers. Once directed to produce a display mode, it would continue to do so automatically for every following scan line. The computer also included a dedicated co-processor, called "Copper", which ran a simple program or 'Copper List' intended for modifying hardware registers in sync with the display. The Copper List instructions could direct the Copper to wait for the display to reach a specific position on the screen, and then change the contents of hardware registers. In effect, it was a processor dedicated to servicing raster interrupts. The Copper was used by Workbench to mix multiple display modes (multiple resolutions and color palettes on the monitor at the same time), and by numerous programs to create rainbow and gradient effects on the screen. The Amiga Copper was also capable of reconfiguring the sprite engine mid-frame, with only one scanline of delay. This allowed the Amiga to draw more than its 8 hardware sprites, so long as the additional sprites did not share scanlines (or the one scanline gap) with more than 7 other sprites. i.e., so long as at least one sprite had finished drawing, another sprite could be added below it on the screen. Additionally, the later 32-bit AGA chipset allowed the drawing of bigger sprites (more pixels per row) while retaining the same multiplexing. The Amiga also had dedicated block-shifter ("blitter") hardware, which could draw larger objects into a framebuffer. This was often used in place of, or in addition to, sprites. In more primitive systems, the results of a display list can be simulated, though at the cost of CPU-intensive writes to certain display modes, color control, or other visual effect registers in the video device, rather than a series of rendering commands executed by the device. Thus, one must create the displayed image using some other rendering process, either before or while the CPU-driven display generation executes. In many cases, the image is also modified or re-rendered between frames. The image is then displayed in various ways, depending on the exact way in which the CPU-driven display code is implemented. Examples of the results possible on these older machines requiring CPU-driven video include effects such as Commodore 64/128's FLI mode, or Rainbow Processing on the ZX Spectrum. == Usage in OpenGL == To delimit a display list, the glNewList and glEndList functions are used, and to execute the list, the glCallList function is used. Almost all rendering commands that occur between the function calls are stored in the display list. Commands that affect the client state are not stored in display lists. Display lists are named with an integer value, and creating a display list with the same name as one already created overrides the first. The glNewList function expects two arguments: an integer representing the name of the list, and an enumeration for the compilation mode. The two modes include GL_COMPILE_AND_EXECUTE, which compiles and immediately executes, and GL_COMPILE, which only compiles the list. Display lists enable the use of the retained mode rendering pattern, which is a system in which graphics commands are recorded (retained) to execute in succession at a later time. This is contrary to immediate mode, where graphics commands are immediately executed on client calls. == Usage in Direct3D 12 == Command lists are created using the ID3D12Device::CreateCommandList function. Command lists may be created in several types: direct, bundle, compute, copy, video decode, video process, and video encoding. Direct command lists specify that a command list the GPU can execute, and doesn't inherit any GPU state. Bundles, are best used for storing and executing small sets of commands any number of times. This is used differently than regular command lists, where commands stored in a command list are typically executed only once. Compute command lists are used for general computations, with a common use being calculating mipmaps. A copy command list is strictly for copying and the video decode and video process command lists are for video decoding and processing respectively. Upon creation, command lists are in the recording state. Command lists may be re-used by calling the ID3D12GraphicsCommandList::Reset function. After recording commands, the command list must be transitioned out of the recording state by calling ID3D12GraphicsCommandList::Close. The command list is then executed by calling ID3D12CommandQueue::ExecuteCommandLists.
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View synthesis

In computer graphics, view synthesis, or novel view synthesis, is a task which consists of generating images of a specific subject or scene from a specific point of view, when the only available information is pictures taken from different points of view. This task was only recently (late 2010s – early 2020s) tackled with significant success, mostly as a result of advances in machine learning. Notable successful methods are Neural radiance fields and 3D Gaussian Splatting. Applications of view synthesis are numerous, one of them being Free view point television. The technique has also been applied to real-estate marketing, where novel views of a listing's interior are generated from a limited set of photographs for use in virtual home staging.
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