AI Detector Zero

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

  • The Future of Work and Death

    The Future of Work and Death

    The Future of Work and Death is a 2016 documentary by Sean Blacknell and Wayne Walsh about the exponential growth of technology. The film showed at several film festivals including Raindance Film Festival, International Film Festival Rotterdam, Academia Film Olomouc and CPH:DOX. In May 2017 it received an official screening at the European Commission. It was distributed by First Run Features and Journeyman Pictures and was released on iTunes, Amazon Prime and On-demand on 9 May 2017. The film was made available on Sundance Now on 27 November 2017. A companion piece to the film, The Cost of Living, a documentary concerning universal basic income in Britain, was released on Amazon Prime on 8 October 2020. == Synopsis == World experts in the fields of futurology, anthropology, neuroscience, and philosophy consider the impact of technological advances on the two 'certainties' of human life; work and death. Charting human developments from Homo habilis, past the Industrial Revolution, to the digital age and beyond, the film looks at the shocking exponential rate at which mankind has managed to create technologies to ease the process of living. As we embark on the next phase of our adaptation, with automation and artificial intelligence signifying the complete move from man to machine, the film asks what the implications are for human fulfilment in an approaching era of job obsolescence and extreme longevity. == Cast == Dudley Sutton – Narrator Aubrey de Grey – Biomedical gerontologist and CSO of the SENS Research Foundation Will Self – Writer, journalist, political commentator and Professor of Contemporary Thought at Brunel University Rudolph E. Tanzi – Professor of Neurology at Harvard University and Director of the Genetics and Aging Research Unit at Massachusetts General Hospital (MGH) Martin Ford – Futurist and author Steve Fuller – Auguste Comte Chair in Social Epistemology at the Department of sociology at University of Warwick Murray Shanahan – Professor of Cognitive Robotics at Imperial College London Gray Scott – Futurist, executive producer of this production Vivek Wadhwa – Entrepreneur, academic and Director of Research at the Center for Entrepreneurship and Research Commercialization at the Pratt School of Engineering, Duke University Zoltan Istvan – Transhumanist and journalist Joanna Cook – Anthropologist, University College London Nicholas Kamara – Physician, Kable Hospital David Pearce – Transhumanist philosopher and co-founder of Humanity+ Peter Cochrane – Futurist and entrepreneur John Harris – Bioethicist, philosopher and Director of the Institute for Science, Ethics and Innovation at the University of Manchester Riva Melissa-Tez – Entrepreneur and transhumanist Ian Pearson – Futurologist Stuart Armstrong – Artificial intelligence researcher at Future of Humanity Institute

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  • Spatiotemporal reservoir resampling

    Spatiotemporal reservoir resampling

    Spatiotemporal reservoir resampling, commonly known as ReSTIR (from "Reservoir-based SpatioTemporal Importance Resampling"), is a collection of computer graphics techniques for reusing samples during rendering. It was developed primarily to allow more realistic lighting in real-time rendering, because relatively few rays can be traced per pixel while maintaining an acceptable frame rate. It can also be used to speed up off-line path tracing. The first ReSTIR paper, published in 2020, provided algorithms for direct lighting, allowing scenes containing thousands of lights to be rendered in real time on a high-end GPU. Researchers later proposed versions for rendering indirect lighting (and more recently, motion blur and depth of field) and built up a framework of mathematical concepts and notation conventions that help analyze such algorithms. A major focus of this work is removing or reducing the bias that could be introduced when samples from other pixels or frames are reused—or selectively allowing some bias in order to speed up rendering and reduce variance (visible as "noise" in the image). Versions for path tracing apply transformations called shift mappings to samples, typically reusing parts of paths closer to the light and modifying the portion closer to the camera. ReSTIR-related papers and talks have been presented every year at the SIGGRAPH conference since 2020. One of the first games to incorporate ReSTIR into its rendering was Cyberpunk 2077. == Overview and motivation == According to Chris Wyman, one of the co-authors of the original paper, although developers commonly thought that bias was acceptable for real-time rendering, end users (e.g. gamers) are well-aware of the artifacts caused by bias and many have a negative opinion of common sample-reuse techniques such as temporal anti-aliasing (TAA), which may cause "ghosting" when the camera moves, and denoising, which causes blurring and other artifacts. ReSTIR techniques can reduce or avoid these types of bias by reusing samples of the set of possible paths taken by light to reach the camera, instead of reusing rendered pixel color values (which are typically the average of multiple samples, discarding information such as the direction of the light). While other techniques reuse samples in a generic post-processing step, ReSTIR passes can test for shadowing, and reused samples are converted into pixel color values by rendering code that takes the characteristics of different materials into account (e.g. by implementing BRDFs). However the output of ReSTIR is noisy, and a denoising pass is typically still used. Stochastic ray tracing techniques such as path tracing need to average multiple samples (produced by tracing individual rays) in order to render a visually acceptable image. When using a simple unbiased renderer based on Monte Carlo integration, halving the deviation of the result (apparent as "noise" in the image) requires multiplying the number of samples by four, meaning that a rapidly increasingly number of samples is needed to improve quality, Standard ways to mitigate this problem include importance sampling (which requires finding improved sampling distributions for specific situations), and quasi-Monte Carlo integration (which usually still requires tracing a large number of rays). ReSTIR offers a solution that multiplies the effective number of samples while tracing a fixed number of additional rays per frame. Temporal reuse multiplies the effective sample count by the number of frames rendered. Spatial reuse multiplies the effective count by the number of neighboring pixels examined. These two types of reuse can be combined, allowing spatial reuse to be applied recursively, which appears to offer an exponentially increasing effective sample count, however this is quickly limited by the size of the neighborhood used for spatial reuse. Spatial reuse is also potentially less effective near shadow and object edges, especially for objects with fine geometric detail, and temporal reuse is limited by movement of the camera and scene elements. == Variations == Many variations of ReSTIR have been proposed that generalize or improve the original technique (which builds on an earlier method called RIS), specialize it for particular types of illumination or other visual effects, or allow incorporation into rendering algorithms other than standard path tracing. Some published versions are listed below. == Algorithms == === Basic algorithm === ReSTIR uses a combination of resampled importance sampling (RIS) and weighted reservoir sampling (WRS) which the authors call streaming RIS. RIS processes samples from an initial probability distribution (e.g. a probability distribution for which a cheap sampling method exists) and generates samples in a new probability distribution (e.g. a sampling distribution that is optimal for rendering but is impractical to draw samples from directly). WRS allows this to be done while storing only a small number of samples in memory, which is especially helpful on a GPU. Information about the samples is stored in a data structure called a reservoir. WRS also allows samples from multiple reservoirs to be combined ("merged") into a single reservoir; this is crucial for sample reuse. Each pixel has a reservoir, typically containing only a single sample when ReSTIR is used for real-time rendering (some implementations use a larger number, e.g. four samples). The reservoir is typically initialized to a sample drawn using a simple method and is then updated by RIS steps and by reservoir merging, so that the pixel value produced by shading using the sample(s) currently in the reservoir, times the weight for the sample, is always an unbiased estimate of the correct pixel value. If appropriate resampling steps are used, the variance of this estimate (or some function of it, typically the luminance of the RGB color value) decreases with each step. A possible sequence of steps performed for each frame, suitable for computing unbiased direct illumination (DI) is: Perform reservoir resampling by drawing multiple light samples and using streaming RIS to choose one, using probabilities based on a target function, e.g. the luminance of the sample's contribution to the pixel. A weight is also computed for the sample. Typically, a single visibility check is performed here, after choosing a sample, setting the weight to 0 if the light is shadowed. Resampling (combined with the visibility check) ensures that the expected value of the weight times the sample brightness is the correct (unbiased) value for the pixel. (temporal reuse) For each pixel, merge the sample(s) from the previous frame into the current reservoir. Multiple importance sampling (MIS) weights are used to avoid bias due to the fact that the samples in the previous frame's reservoirs may have a different target probability distribution if the objects, lights, or camera have moved. (spatial reuse) For each pixel, choose one or more neighboring pixels and merge their samples into the current pixel's reservoir. Multiple importance sampling (MIS) weights are used to avoid bias due to the fact that the samples in each pixel's reservoir have a different target probability distribution. Because computing unbiased MIS weights requires tracing additional rays (along with other work such as evaluating BRDFs), real-time rendering often uses only a single neighboring pixel. Use the sample in each pixel's reservoir, along with its weight, to determine the color of the pixel for the current frame. Alternatively, multiple samples examined during the preceding steps may be averaged and used to shade the pixel instead (decoupled shading and sampling). For direct lighting, the initial samples used in step 1 are typically drawn by importance sampling from the set of lights in a scene. The algorithm above (from the original ReSTIR paper) draws many lower-quality light samples (e.g. 32) using a fast method, without considering visibility, and chooses one using streaming RIS. Visibility is then tested for the final chosen sample. Considering visibility for each sample drawn would require tracing 32 rays, which would make it much more expensive. The intent is to reduce the number of rays traced, relying on the sample reuse in steps 2 and 3 to make up for the loss of quality caused by rejecting many of the rays due to shadowing. A large part of the initial efforts to optimize ReSTIR (to make it run in real-time on available hardware) went into reducing the cost of randomly sampling the lights. Glossy surfaces may require a larger number of samples, and combining light sampling with BRDF sampling (using MIS) may increase quality. Step 2 (temporal reuse) is sometimes skipped for off-line rendering, and the output of multiple repetitions of initial sampling and spatial reuse is averaged instead; this helps avoids artifacts due to correlations. Step 3 (spatial reuse) may be repeated multiple times in a single frame.

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  • VK Video

    VK Video

    VK Video is an internet video hosting service launched by VK (formerly known as Mail.ru Group) in 2021. It is positioned as a Russian alternative to the international platform YouTube. == History == The "VK Video" service began operations on October 15, 2021, following the merger of video platforms belonging to the social networks "VKontakte" and "Odnoklassniki". The launch of "VK Video" was managed by a team of executives led by VKontakte CEO Marina Krasnova, who worked at the company until 2023. Its launch was intended as an alternative to the international platform YouTube, which Russian authorities sought to replace with "domestic analogs. Key differences of the Russian service became the presence of pirated materials. Videos from the American video hosting site were uploaded en masse to "VK Video," which even caused the service to be temporarily blocked by YouTube. From 2022, to attract users, VKontakte's management bet on working with famous bloggers, specifically purchasing the shows "What Happened Next?" (ChBD) and "Vnutri Lapenko". Among the bloggers recruited to promote the service was the popular video blogger Vlad A4. An additional advantage for creators was the availability of monetization, which had been unavailable on YouTube for users from the Russian Federation since 2022. In September 2023, a separate "VK Video" mobile app appeared. In total, by the end of 2023, the monthly audience of "VK Video" reached 67.9 million users (which is almost 30 million less than YouTube). In the summer of 2024, following the blocking of YouTube in Russia, the service's traffic grew sharply: in August, its audience increased by more than two times compared to July. In the same month, "VK Video" took second place in downloads among free apps in the App Store and third in Google Play. In December 2024, the service received its own domain: vkvideo.ru. For the first time, "VK Video" managed to surpass YouTube in monthly audience in Russia in July 2025: the Russian service attracted 76.4 million viewers, whereas YouTube's reach amounted to 74.9 million people. == Platform features == On "VK Video," a view is recorded from the first second, whereas on YouTube it is only from the thirtieth. At the same time, a significant portion of comments are left by bots. For videos from the platform's most popular bloggers, the engagement level (likes to views) does not reach 4%. The "Trends" section most often features videos from large channels where the ratio of likes to views does not exceed 2%. == Management == In April 2025, the post of General Director of "VK Video" was taken by Marianna Maksimovskaya. From June 2022 to July 2024, the development of the platform was led by Fyodor Yezhov, who was primarily responsible for its technical direction. == Awards == In 2023, VK Video was awarded the Runet Prize in the "Science, Technology and Innovation" category.

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  • Sample (graphics)

    Sample (graphics)

    In computer graphics, a sample is an intersection of a channel and a pixel. The diagram below depicts a 24-bit pixel, consisting of 3 samples for Red, Green, and Blue. In this particular diagram, the Red sample occupies 9 bits, the Green sample occupies 7 bits and the Blue sample occupies 8 bits, totaling 24 bits per pixel. Note that the samples do not have to be equal size and not all samples are mandatory in a pixel. Also, a pixel can consist of more than 3 samples (e.g. 4 samples of the RGBA color space). A sample is related to a subpixel on a physical display.

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  • List of security assessment tools

    List of security assessment tools

    This is a list of available software and hardware tools that are designed for or are particularly suited to various kinds of security assessment and security testing. == Operating systems and tool suites == Several operating systems and tool suites provide bundles of tools useful for various types of security assessment. === Operating system distributions === Kali Linux (formerly BackTrack), a penetration-test-focused Linux distribution based on Debian Pentoo, a penetration-test-focused Linux distribution based on Gentoo ParrotOS, a Linux distro focused on penetration testing, forensics, and online anonymity. == Tools ==

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  • Blanking (video)

    Blanking (video)

    In analog video, blanking occurs between horizontal lines and between frames. In raster scan equipment, an image is built up by scanning an electron beam from left to right across a screen to produce a visible trace of one scan line, reducing the brightness of the beam to zero (horizontal blanking), moving it back as fast as possible to the left of the screen at a slightly lower position (the next scan line), restoring the brightness, and continuing until all the lines have been displayed and the beam is at the bottom right of the screen. Its intensity is then reduced to zero again (vertical blanking), and it is rapidly moved to the top left to start again, creating the next frame. In television, in particular, the vertical blanking interval is long to accommodate the slow equipment available at the time the standard was set. Fast modern electronics allows digital information to be encoded into the signal during the vertical blanking interval; it is not displayed on screen as the beam is blanked, but can be processed by appropriate circuitry.

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  • Data item

    Data item

    A data item describes an atomic state of a particular object concerning a specific property at a certain time point. A collection of data items for the same object at the same time forms an object instance (or table row). Any type of complex information can be broken down to elementary data items (atomic state). Data items are identified by object (o), property (p) and time (t), while the value (v) is a function of o, p and t: v = F(o,p,t). Values typically are represented by symbols like numbers, texts, images, sounds or videos. Values are not necessarily atomic. A value's complexity depends on the complexity of the property and time component. When looking at databases or XML files, the object is usually identified by an object name or other type of object identifier, which is part of the "data". Properties are defined as columns (table row), properties (object instance) or tags (XML). Often, time is not explicitly expressed and is an attribute applying to the complete data set. Other data collections provide time on the instance level (time series), column level, or even attribute/property level.

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  • Vigloo

    Vigloo

    Vigloo (Korean: 비글루) is a South Korean microdrama, also known as short-form drama, series streaming platform owned by SpoonLabs, with headquarters in Seoul. It provides content produced in South Korea, Japan, and the United States. Vigloo produced the first AI-created short-form drama in South Korea. == History == Vigloo launched in July 2024. After receiving an equity investment of $86 million (₩120 billion) by South Korean video game company Krafton in September 2024, Vigloo expanded to the U.S. In January 2025, Vigloo unveiled its first in-house produced drama, Xs Who Want to Kill: Adultery Investigation Unit. Vigloo had been testing the use of AI in post-production and visual effects, and in October 2025 released two original dramas produced entirely with AI. It adapted its live action Japanese short-form drama Boyfriend Search Project – Kissing 5 Men into the first short-form animation series made with AI technology in South Korea. Of the top free entertainment iOS apps in South Korea, Vigloo ranks Number 3 as of January 2026. == Service == === Content === Vigloo offers both original and licensed content. It partnered with Passionflix to repackage the latter's original series The Secret Life of Amy Bensen into 35 vertical "bite-sized episodes". The most popular genre is romance, such as romantasy. === Business Model === Vigloo is available around the world, providing subtitles in nine languages, including Korean, English, and Japanese. Fifty percent of Vigloo's revenue comes from the U.S. Vigloo operates on a freemium model, where viewers can try several episodes and then can choose to continue by subscription or in-app purchases. As of September 2025, 70% of Vigloo viewers were over 35 years old. === Microdramas === Emerging during the early COVID period in China, microdramas have grown into a 7-billion-dollar market with dozens of dedicated platforms now operating. Although the format first expanded across Asia, short-form scripted content optimized for mobile viewing is increasingly being produced and watched in markets worldwide. == Series == A Vampire in the Alpha's Den Fight for Love Matrimoney Signed, Sealed, Deceived by My Billionaire Mailboy Spring Break Bucket List Stake to the Heart

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  • Visual temporal attention

    Visual temporal attention

    Visual temporal attention is a special case of visual attention that involves directing attention to specific instant of time. Similar to its spatial counterpart visual spatial attention, these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable explanation of deep learning models. As visual spatial attention mechanism allows human and/or computer vision systems to focus more on semantically more substantial regions in space, visual temporal attention modules enable machine learning algorithms to emphasize more on critical video frames in video analytics tasks, such as human action recognition. In convolutional neural network-based systems, the prioritization introduced by the attention mechanism is regularly implemented as a linear weighting layer with parameters determined by labeled training data. == Application in Action Recognition == Recent video segmentation algorithms often exploits both spatial and temporal attention mechanisms. Research in human action recognition has accelerated significantly since the introduction of powerful tools such as Convolutional Neural Networks (CNNs). However, effective methods for incorporation of temporal information into CNNs are still being actively explored. Motivated by the popular recurrent attention models in natural language processing, the Attention-aware Temporal Weighted CNN (ATW CNN) is proposed in videos, which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is implemented as temporal weighting and it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Experimental results show that the ATW CNN attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments. == Literature == Seibold VC, Balke J and Rolke B (2023): Temporal attention. Front. Cognit. 2:1168320. doi: 10.3389/fcogn.2023.1168320.

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  • Snapshot isolation

    Snapshot isolation

    In databases, and transaction processing (transaction management), snapshot isolation is a guarantee that all reads made in a transaction will see a consistent snapshot of the database (in practice it reads the last committed values that existed at the time it started), and the transaction itself will successfully commit only if no updates it has made conflict with any concurrent updates made since that snapshot. Snapshot isolation has been adopted by several major database management systems, such as InterBase, Firebird, Oracle, MySQL, PostgreSQL, SQL Anywhere, MongoDB and Microsoft SQL Server (2005 and later). The main reason for its adoption is that it allows better performance than serializability, yet still avoids most of the concurrency anomalies that serializability avoids (but not all). In practice snapshot isolation is implemented within multiversion concurrency control (MVCC), where generational values of each data item (versions) are maintained: MVCC is a common way to increase concurrency and performance by generating a new version of a database object each time the object is written, and allowing transactions' read operations of several last relevant versions (of each object). Snapshot isolation has been used to criticize the ANSI SQL-92 standard's definition of isolation levels, as it exhibits none of the "anomalies" that the SQL standard prohibited, yet is not serializable (the anomaly-free isolation level defined by ANSI). In spite of its distinction from serializability, snapshot isolation is sometimes referred to as serializable by Oracle. == Definition == A transaction executing under snapshot isolation appears to operate on a personal snapshot of the database, taken at the start of the transaction. When the transaction concludes, it will successfully commit only if the values updated by the transaction have not been changed externally since the snapshot was taken. Such a write–write conflict will cause the transaction to abort. In a write skew anomaly, two transactions (T1 and T2) concurrently read an overlapping data set (e.g. values V1 and V2), concurrently make disjoint updates (e.g. T1 updates V1, T2 updates V2), and finally concurrently commit, neither having seen the update performed by the other. Were the system serializable, such an anomaly would be impossible, as either T1 or T2 would have to occur "first", and be visible to the other. In contrast, snapshot isolation permits write skew anomalies. As a concrete example, imagine V1 and V2 are two balances held by a single person, Phil. The bank will allow either V1 or V2 to run a deficit, provided the total held in both is never negative (i.e. V1 + V2 ≥ 0). Both balances are currently $100. Phil initiates two transactions concurrently, T1 withdrawing $200 from V1, and T2 withdrawing $200 from V2. If the database guaranteed serializable transactions, the simplest way of coding T1 is to deduct $200 from V1, and then verify that V1 + V2 ≥ 0 still holds, aborting if not. T2 similarly deducts $200 from V2 and then verifies V1 + V2 ≥ 0. Since the transactions must serialize, either T1 happens first, leaving V1 = −$100, V2 = $100, and preventing T2 from succeeding (since V1 + (V2 − $200) is now −$200), or T2 happens first and similarly prevents T1 from committing. If the database is under snapshot isolation(MVCC), however, T1 and T2 operate on private snapshots of the database: each deducts $200 from an account, and then verifies that the new total is zero, using the other account value that held when the snapshot was taken. Since neither update conflicts, both commit successfully, leaving V1 = V2 = −$100, and V1 + V2 = −$200. Some systems built using multiversion concurrency control (MVCC) may support (only) snapshot isolation to allow transactions to proceed without worrying about concurrent operations, and more importantly without needing to re-verify all read operations when the transaction finally commits. This is convenient because MVCC maintains a series of recent history consistent states. The only information that must be stored during the transaction is a list of updates made, which can be scanned for conflicts fairly easily before being committed. However, MVCC systems (such as MarkLogic) will use locks to serialize writes together with MVCC to obtain some of the performance gains and still support the stronger "serializability" level of isolation. == Workarounds == Potential inconsistency problems arising from write skew anomalies can be fixed by adding (otherwise unnecessary) updates to the transactions in order to enforce the serializability property. Materialize the conflict Add a special conflict table, which both transactions update in order to create a direct write–write conflict. Promotion Have one transaction "update" a read-only location (replacing a value with the same value) in order to create a direct write–write conflict (or use an equivalent promotion, e.g. Oracle's SELECT FOR UPDATE). In the example above, we can materialize the conflict by adding a new table which makes the hidden constraint explicit, mapping each person to their total balance. Phil would start off with a total balance of $200, and each transaction would attempt to subtract $200 from this, creating a write–write conflict that would prevent the two from succeeding concurrently. However, this approach violates the normal form. Alternatively, we can promote one of the transaction's reads to a write. For instance, T2 could set V1 = V1, creating an artificial write–write conflict with T1 and, again, preventing the two from succeeding concurrently. This solution may not always be possible. In general, therefore, snapshot isolation puts some of the problem of maintaining non-trivial constraints onto the user, who may not appreciate either the potential pitfalls or the possible solutions. The upside to this transfer is better performance. == Terminology == Snapshot isolation is called "serializable" mode in Oracle and PostgreSQL versions prior to 9.1, which may cause confusion with the "real serializability" mode. There are arguments both for and against this decision; what is clear is that users must be aware of the distinction to avoid possible undesired anomalous behavior in their database system logic. == History == Snapshot isolation arose from work on multiversion concurrency control databases, where multiple versions of the database are maintained concurrently to allow readers to execute without colliding with writers. Such a system allows a natural definition and implementation of such an isolation level. InterBase, later owned by Borland, was acknowledged to provide SI rather than full serializability in version 4, and likely permitted write-skew anomalies since its first release in 1985. Unfortunately, the ANSI SQL-92 standard was written with a lock-based database in mind, and hence is rather vague when applied to MVCC systems. Berenson et al. wrote a paper in 1995 critiquing the SQL standard, and cited snapshot isolation as an example of an isolation level that did not exhibit the standard anomalies described in the ANSI SQL-92 standard, yet still had anomalous behaviour when compared with serializable transactions. In 2008, Cahill et al. showed that write-skew anomalies could be prevented by detecting and aborting "dangerous" triplets of concurrent transactions. This implementation of serializability is well-suited to multiversion concurrency control databases, and has been adopted in PostgreSQL 9.1, where it is known as Serializable Snapshot Isolation (SSI). When used consistently, this eliminates the need for the above workarounds. The downside over snapshot isolation is an increase in aborted transactions. This can perform better or worse than snapshot isolation with the above workarounds, depending on workload.

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  • MY F.C.

    MY F.C.

    MY F.C. is a freemium app designed to organise and administer football teams. It is developed by MY F.C. Limited, a private company headquartered in Auckland, New Zealand. The app allows users to build a team by adding players and from there they can create trainings and matches, keep up with relevant news in the curated newsfeed, record statistics both individually and team based, follow the games live in the match-centre. The app also features integrated lineup builder with custom team kits. == History == Founders Sam Jenkins, Mike Simpson and Sam Jasper started MY F.C. in 2015 to help them "run their football lives". The app was launched on Android and iOS on 14 February 2017. == Accolades == MY F.C. won the first place prize at Bank of New Zealand Start-up Alley 2017 competition that aims to discover New Zealand start-ups who are doing innovative work and ready to establish themselves as long-term, sustainable businesses. The prize package included $15,000 and a trip to San Francisco.

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  • Data event

    Data event

    A data event is a relevant state transition defined in an event schema. Typically, event schemata are described by pre- and post condition for a single or a set of data items. In contrast to ECA (Event condition action), which considers an event to be a signal, the data event not only refers to the change (signal), but describes specific state transitions, which are referred to in ECA as conditions. Considering data events as relevant data item state transitions allows defining complex event-reaction schemata for a database. Defining data event schemata for relational databases is limited to attribute and instance events. Object-oriented databases also support collection properties, which allows defining changes in collections as data events, too.

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  • Chasys Photo

    Chasys Photo

    Chasys Photo (previously called Chasys Draw Artist, then Chasys Draw IES) is a suite of applications including a layer-based raster graphics editor with adjustment layers, linked layers, timeline and frame-based animation, icon editing, image stacking and comprehensive plug-in support (Chasys Draw IES Artist), a fast multi-threaded image file converter (Chasys Draw IES Converter) and a fast image viewer (Chasys Draw IES Viewer), with RAW image support in all components. It supports the native file formats of several competitors including Adobe Photoshop, Affinity Photo, Corel Photo-Paint, GIMP, Krita, Paint.NET and PaintShop Pro, and the whole suite is designed to make effective use of multi-core processors, touch-screens and pen-input devices. The software is developed by John Paul Chacha in Nairobi, Kenya. Chasys Draw IES is currently released as freeware, and is available for computers running Microsoft Windows operating systems. It is available in three distributions: the standard distro, a portable version and a Microsoft Store version. The suite is coded in a blend of C, C++ and assembly language. It runs on x86 processors and supports the MMX, SSE, SSE2, S-SSE3, and SSE4.1 instruction sets. == History == Chasys Draw is a project that was started in November 2001 by John Paul Chacha, mostly as a hobby than anything else. The original Chasys Draw was a rather simple bitmap editor done in Visual Basic, a lot like MS Paint save for its ability to do gradients. This application underwent many changes, eventually leading up to Chasys Draw 5. This was the first version to have its own native format, referred to simply as CD5. Major updates to the graphics code in May 2002 resulted in Chasys Draw DTFx (Direct Tool eFfects). The new graphics code being referred to here was actually a miniature bitmap abstraction engine that allowed for fast per-pixel operations and direct image buffer access (much as the DIB engine does for GDI). The engine was named JpDRAW. This version was also done in VB, but was much faster than all the previous versions. The new graphics code allowed for more tools to be implemented than was ever possible before. Later on in 2002, the developer decided to completely abandon VB as a programming platform and moved all the code to C/C++. The move to C/C++ allowed the development of a full-fledged graphics engine which was named JpDRAW2. Chasys was renamed to Chasys Draw Artist, and the CD5 image format was also updated to reflect the new features. By coincidence, the module that implemented the file format was the fifth module to be added, so the format was called Chasys Draw module 5, retaining the .cd5 file extension. First public release In April 2004, Chasys Draw Artist was released to the public via the internet for the first time (version 1.27). The release was done via betanews). In 2005, Chasys Draw underwent major user interface changes as well as internal changes. By December of that year, the project had reached version 1.63. This was the first version to introduce advanced features such as anti-aliasing. It was also the first version with full support for alpha channels. The CD5 image format was also upgraded to version 2, adding advanced compression, full alpha channels, encryption and metadata. Version 1.63 was the first version to win an IEEE (Kenya chapter) award in ICT. The "chazy-glass" interface, from which the all later versions' user interfaces borrowed, was introduced in version 1.80. Chasys Draw Artist adopted photo editing features in version 2.01. Comprehensive tutorials were added and many features were re-designed to make them easier to use. Multi-threading was introduced to accelerate some tasks, such as the improved auto-save engine. Utilities such as a converter and browser were added. Version 2.43 of Chasys Draw Artist was quietly released to the public in late 2007 without any announcements. It featured many fixes to the formal version 2.42, as well as many new features. The quiet release was due to a decision to re-build Chasys Draw Artist from scratch, while still continuing support for the old architecture. An experimental version 2.45 was released only to beta-testers for the purpose of testing new technologies that would be included in the new architecture and was officially withdrawn in May 2008. During the time when the versions 2.43~2.45 were being released, work was underway to create a new layer-based Chasys Draw, which was released as Chasys Draw IES (Image Editing Suite), with the initial version number 2.50. A new multi-layer tag-based image format was created to support layering and blending modes; this was named CD5 v3. The next version introduced animation and multi-resolution support as editing modes, and the next one brought in an unlimited undo engine, new plug-ins and several internal fixes. Further development led to the introduction of super-resolution and image stacking, support for video and video capture, Anti-aliasing, metadata save and restore, a "Pen and Path" tool, physical measurement specification, and a video sequence composer engine. The user interface was enhanced with adaptive scrolling and the auto-save engine was optimized. Some memory management was added for machines with low RAM. By version 2.60, Chasys Draw IES was capable of loading Photoshop's PSD files, as well as load and save JPEG 2000. This version also had shell integration with thumbnails and application-level support for multi-monitor display setups. Metadata was extended to support save, restore and scaling for text formatting and path data. There was also a new palette with exchangeable swatches, loadable from all kinds of palette files. A slicing tool for web and user interface design was also included. A C++ code module output for inline image generation was added, as was a constrained recolor brush. The concept of a "fully anti-aliased work-flow" was introduced in version 2.62, in which all drawing and selection tools were anti-aliased by default. Support for Photoshop plug-ins using Adobe's 8bf format was added in version 2.66, allowing users to utilize thousands of free plug-ins available online. Equivalents for the Pantone palettes (PMS 100 to 814-2x) were added, and the "Just-in-Time" memory compressor significantly reduced the editor's memory requirements. First freeware release Chasys Draw IES went freeware on 6 June 2009. With the coming of the freeware IES, two blending modes (Hue and Chroma) were added. Textures were improved to allow multiple layer-based textures. The TextArt G3 engine was enhanced with LINK metadata, and alpha shift was improved. IES 2.72 added the Luma Wand tool, fixed PNG and TIFF transparency issues, and fixed Smart-Paste transparency. IES 2.74 introduced alpha protection, and 2.75 followed with a new adjustments engine that faced out many effects implemented by the effects engine. The adjustments engine was designed to appeal to experienced image editors. IES 2.76 introduced a new transform engine and the Resizer for IES plug-in supporting multi-core and 18 scaling methods, including customizable windowed Sinc interpolation. IES 2.77 added Greyscale with Tint adjustment, separated the Lock and Click-Thru layer properties, extended the Cloning Brush with three options (this, below and composite) and also extended the Color Picker with multiple point sampling. IES 3.01 brought a new look and many breakthrough tools to the suite. It was geared toward touch and was fully compatible with Windows 7. The toolbox was reorganized, with some tools being grouped and new ones added. Some message boxes were replaced with a new popup system, and the working of the workspace was changed to use a back-blitter, which enabled the addition of new blending modes, Screen and Mask. The printing interface was modified and given accurate proofing. Alpha Function Adjustment was added and a new Anti-Quantization Engine included for all adjustments to remove the need for 16 bits per channel editing. An internal clipboard was created to cater for copying images that are too large for the Windows clipboard, and translucency full-page gradients added. Some new tutorials were added and keyboard shortcuts made configurable. IES 3.05 brought the power of custom full-page gradients to the suite, supporting .ggr, .grd and .gra gradients. New gradient styles were included, as was support for Adobe color tables (.act), palette previewing, point color editing and a highly improved TextArt engine. Digital lightroom IES 3.11 was introduced on 14 December 2009. It was done on a new development base and added a new application, raw-Input. This was a RAW image format processor based on dcraw. This application allowed the use of Chasys Draw IES in processing digital negatives, which are popular with professional photographers. Chasys Draw IES 3.24 was released with a re-designed user interface, powered by a higher performance graphics core and better memory management. A history palette w

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  • Identity column

    Identity column

    An identity column is a column (also known as a field) in a database table that is made up of values generated by the database. This is much like an AutoNumber field in Microsoft Access or a sequence in Oracle. Because the concept is so important in database science, many RDBMS systems implement some type of generated key, although each has its own terminology. Today a popular technique for generating identity is to generate a random UUID. An identity column differs from a primary key in that its values are managed by the server and usually cannot be modified. In many cases an identity column is used as a primary key; however, this is not always the case. It is a common misconception that an identity column will enforce uniqueness; however, this is not the case. If you want to enforce uniqueness on the column you must include the appropriate constraint too. In Microsoft SQL Server you have options for both the seed (starting value) and the increment. By default the seed and increment are both 1. == Code samples == or In PostgreSQL == Related functions == It is often useful or necessary to know what identity value was generated by an INSERT command. Microsoft SQL Server provides several functions to do this: @@IDENTITY provides the last value generated on the current connection in the current scope, while IDENT_CURRENT(tablename) provides the last value generated, regardless of the connection or scope it was created on. Example:

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  • Comparison of color models in computer graphics

    Comparison of color models in computer graphics

    This article provides introductory information about the RGB, HSV, and HSL color models from a computer graphics (web pages, images) perspective. An introduction to colors is also provided to support the main discussion. == Basics of color == === Primary colors and hue === First, "color" refers to the human brain's subjective interpretation of combinations of a narrow band of wavelengths of light. For this reason, the definition of "color" is not based on a strict set of physical phenomena. Therefore, even basic concepts like "primary colors" are not clearly defined. For example, traditional "Painter's Colors" use red, blue, and yellow as the primary colors, "Printer's Colors" use cyan, yellow, and magenta, and "Light Colors" use red, green, and blue. "Light colors", more formally known as additive colors, are formed by combining red, green, and blue light. This article refers to additive colors and refers to red, green, and blue as the primary colors. Hue is a term describing a pure color, that is, a color not modified by tinting or shading (see below). In additive colors, hues are formed by combining two primary colors. When two primary colors are combined in equal intensities, the result is a "secondary color". === Color wheel === A color wheel is a tool that provides a visual representation of the relationships between all possible hues. The primary colors are arranged around a circle at equal (120 degree) intervals. (Warning: Color wheels frequently depict "Painter's Colors" primary colors, which leads to a different set of hues than additive colors.) The illustration shows a simple color wheel based on the additive colors. Note that the position (top, right) of the starting color, typically red, is arbitrary, as is the order of green and blue (clockwise, counter-clockwise). The illustration also shows the secondary colors, yellow, cyan, and magenta, located halfway between (60 degrees) the primary colors. == Complementary color == The complement of a hue is the hue that is opposite it (180 degrees) on the color wheel. Using additive colors, mixing a hue and its complement in equal amounts produces white. === Tints and shades === The following discussion uses an illustration involving three projectors pointing to the same spot on a screen. Each projector is capable of generating one hue. The "intensities" of each projector are "matched" and can be equally adjusted from zero to full. (Note: "Intensity" is used here in the same sense as the RGB color model. The subject of matching, or "gamma correction", is beyond the level of this article.) A shade is produced by "dimming" a maximum chroma color. Painters refer to this as "adding black". In our illustration, one projector is set to full intensity, a second is set to some intensity between zero and full, and third is set to zero. "Dimming" is accomplished by decreasing each projector's intensity setting to the same fraction of its start setting. In the shade example, with any fully shaded hue, that all three projectors are set to zero intensity, resulting in black. A tint is produced by "lightening" a maximum chroma color. Painters refer to this as "adding white". In our illustration, one projector is set to full intensity, a second is set to some intensity between zero and full, and third is set to zero. "Lightening" is accomplished by increasing each projector's intensity setting by the same fraction from its start setting to full. In the tinting example, note that the third projector is now contributing. When the hue is fully lightened, all three projectors are each at full intensity, and the result is white. Note an attribute of the total intensity in the additive model. If full intensity for one projector is 1, then a primary color has a combined intensity of 1. A secondary color has a total intensity of 2. White has a total intensity of 3. Tinting, or "adding white", increases the total intensity of the hue. While this is simply a fact, the HSL model will take this fact into account in its design. === Tones === Tone is a general term, typically used by painters, to refer to the effects of reducing the "colorfulness" of a maximum chroma color; painters refer to it as "adding gray". Note that gray is not a color or even a single concept but refers to all the range of values between black and white where all three primary colors are equally represented. The general term is provided as more specific terms have conflicting definitions in different color models. Thus, shading takes a hue toward black, tinting takes a hue towards white, and tones cover the range between. == Choosing a color model == No one color model is necessarily "better" than another. Typically, the choice of a color model is dictated by external factors, such as a graphics tool or the need to specify colors according to the CSS2 or CSS3 standard. The following discussion only describes how the models function, centered on the concepts of hue, shade, tint, and tone. === RGB === The RGB model's approach to colors is important because: It directly reflects the physical properties of "Truecolor" displays As of 2011, most graphic cards define pixel values in terms of the colors red, green, and blue. The typical range of intensity values for each color, 0–255, is based on taking a binary number with 32 bits and breaking it up into four bytes of 8 bits each. 8 bits can hold a value from 0 to 255. The fourth byte is used to specify the "alpha", or the opacity, of the color. Opacity comes into play when layers with different colors are stacked. If the color in the top layer is less than fully opaque (alpha < 255), the color from underlying layers "shows through". In the RGB model, hues are represented by specifying one color as full intensity (255), a second color with a variable intensity, and the third color with no intensity (0). The following provides some examples using red as the full-intensity and green as the partial-intensity colors; blue is always zero: Shades are created by multiplying the intensity of each primary color by 1 minus the shade factor, in the range 0 to 1. A shade factor of 0 does nothing to the hue, a shade factor of 1 produces black: new intensity = current intensity (1 – shade factor) The following provides examples using orange: Tints are created by modifying each primary color as follows: the intensity is increased so that the difference between the intensity and full intensity (255) is decreased by the tint factor, in the range 0 to 1. A tint factor of 0 does nothing, a tint factor of 1 produces white: new intensity = current intensity + (255 – current intensity) tint factor The following provides examples using orange: Tones are created by applying both a shade and a tint. The order in which the two operations are performed does not matter, with the following restriction: when a tint operation is performed on a shade, the intensity of the dominant color becomes the "full intensity"; that is, the intensity value of the dominant color must be used in place of 255. The following provides examples using orange: === HSV === The HSV, or HSB, model describes colors in terms of hue, saturation, and value (brightness). Note that the range of values for each attribute is arbitrarily defined by various tools or standards. Be sure to determine the value ranges before attempting to interpret a value. Hue corresponds directly to the concept of hue in the Color Basics section. The advantages of using hue are The angular relationship between tones around the color circle is easily identified Shades, tints, and tones can be generated easily without affecting the hue Saturation corresponds directly to the concept of tint in the Color Basics section, except that full saturation produces no tint, while zero saturation produces white, a shade of gray, or black. Value corresponds directly to the concept of intensity in the Color Basics section. Pure colors are produced by specifying a hue with full saturation and value Shades are produced by specifying a hue with full saturation and less than full value Tints are produced by specifying a hue with less than full saturation and full value Tones are produced by specifying a hue and both less than full saturation and value White is produced by specifying zero saturation and full value, regardless of hue Black is produced by specifying zero value, regardless of hue or saturation Shades of gray are produced by specifying zero saturation and between zero and full value The advantage of HSV is that each of its attributes corresponds directly to the basic color concepts, which makes it conceptually simple. The perceived disadvantage of HSV is that the saturation attribute corresponds to tinting, so desaturated colors have increasing total intensity. For this reason, the CSS3 standard plans to support RGB and HSL but not HSV. === HSL === The HSL model describes colors in terms of hue, saturation, and lightness (also called luminance). (Note: the definition of sa

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