AI Content On Linkedin

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  BeyondCorp

  BeyondCorp is an implementation of zero-trust computer security concepts creating a zero trust network. It is created by Google. == Background == It was created in response to the 2009 Operation Aurora. An open source implementation inspired by Google's research paper on an access proxy is known as "transcend". Google documented its Zero Trust journey from 2014 to 2018 through a series of articles in the journal ;login:. Google called their ZT network "BeyondCorp". Google implemented a Zero Trust architecture on a large scale, and relied on user and device credentials, regardless of location. Data was encrypted and protected from managed devices. Unmanaged devices, such as BYOD, were not given access to the BeyondCorp resources. == Design and technology == BeyondCorp utilized a zero trust security model, which is a relatively new security model that it assumes that all devices and users are potentially compromised. This is in contrast to traditional security models, which rely on firewalls and other perimeter defenses to protect sensitive data. === Trust === The corporate network grants no inherent trust, and all internal apps are accessed via the BeyondCorp system, regardless of whether the user is in a Google office or working remotely. BeyondCorp is related to Zero Trust architecture as it implements a true Zero Trust network, where all access is granted on identity, device, and authentication, based on robust underlying device and identity data sources. BeyondCorp works by using a number of security policies including authentication, authorization, and access control to ensure that only authorized users can access corporate resources. Authentication verifies the identity of the user, authorization determines whether the user has permission to access the requested resource, and access control policies restrict what the user can do with the resource. ==== Trust Inferrer ==== One of the main components in BeyondCorp's implementation is the Trust Inferrer. The Trust Inferrer is a security component (typically software) that looks at information about a user's device, like a computer or phone, to decide how much it can be trusted to access certain resources like important company documents. The Trust Inferrer checks things like the security of the device, whether it has the right software installed, and if it belongs to an authorized user. Based on all this information, the Trust Inferrer decides what the device can access and what it can't. === Security mechanisms === Unlike traditional VPNs, BeyondCorp's access policies are based on information about a device, its state, and its associated user. BeyondCorp considers both internal networks and external networks to be completely untrusted, and gates access to applications by dynamically asserting and enforcing levels, or “tiers,” of access. === Device Inventory Database === BeyondCorp utilized a Device Inventory Database and Device Identity that uniquely identifies a device through a digital certificate. Any changes to the device are recorded in the Device Inventory Database. The certificate is used to uniquely identify a device; however, additional information is required to grant access privileges to a resource. === Access Control Engine === Another important component of BeyondCorp's implementation is the Access Control Engine. Think of this as the brain of the Zero Trust architecture. The Access Control Engine is like a traffic cop standing at an intersection. Its job is to make sure that only authorized devices and users are allowed to access specific resources (like files or applications) on the network. It checks the access policy (the rules that say who can access what), the device's state (like whether it has the right software updates or security settings), and the resources being requested. Then it makes a decision on whether to grant or deny access based on all of this information. It helps ensure that only the right people and devices are allowed access to the network, which helps keep things secure. The Access Control Engine utilizes the output from the Trust Inferrer and other data that is fed into its system. == Usage == One of the first things Google did to implement a Zero Trust architecture was to capture and analyze network traffic. The purpose of analyzing the traffic was to build a baseline of what typical network traffic looked like. In doing so, BeyondCorp also discovered unusual, unexpected, and unauthorized traffic. This was very useful because it gave the BeyondCorp engineers critical information that assisted them in reengineering the system in a secure manner. Some of the benefits BeyondCorp realized by adopting a Zero Trust architecture include the ability to allow their employees to work securely from any location. It reduces the risk of data breaches since data and applications are protected and users and devices are constantly being verified. The Zero Trust architecture is scalable and can be adapted to the changing needs of the businesses and their users. Especially relevant in today's work-from-home era, BeyondCorp allows employees to access enterprise resources securely from any location, without the need for traditional VPNs.

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  Simple interactive object extraction

  Simple interactive object extraction (SIOX) is an algorithm for extracting foreground objects from color images and videos with very little user interaction. It has been implemented as "foreground selection" tool in the GIMP (since version 2.3.3), as part of the tracer tool in Inkscape (since 0.44pre3), and as function in ImageJ and Fiji (plug-in). Experimental implementations were also reported for Blender and Krita. Although the algorithm was originally designed for videos, virtually all implementations use SIOX primarily for still image segmentation. In fact, it is often said to be the current de facto standard for this task in the open-source world. Initially, a free hand selection tool is used to specify the region of interest. It must contain all foreground objects to extract and as few background as possible. The pixels outside the region of interest form the sure background while the inner region define a superset of the foreground, i.e. the unknown region. A so-called foreground brush is then used to mark representative foreground regions. The algorithm outputs a selection mask. The selection can be refined by either adding further foreground markings or by adding background markings using the background brush. Technically, the algorithm performs the following steps: Create a set of representative colors for sure foreground and sure background, the so-called color signatures. Assign all image points to foreground or background by a weighted nearest neighbor search in the color signatures. Apply some standard image processing operations like erode, dilate, and blur to remove artifacts. Find the connected foreground components that are either large enough or marked by the user. For video segmentation the sure background and sure foreground regions are learned from motion statistics. SIOX also features tools that allow sub-pixel accurate refinement of edges and high texture areas, the so-called "detail refinement brushes". As with all segmentation algorithms, there are always pictures where the algorithm does not yield perfect results. The most critical drawback of SIOX is the color dependence. Although many photos are well-separable by color, the algorithm cannot deal with camouflage. If the foreground and background share many identical shades of similar colors, the algorithm might give a result with parts missing or incorrectly classified foreground. SIOX performs about equally well on different benchmarks compared to graph-based segmentation methods, such as Grabcut. SIOX is, however, more noise robust and can therefore also be used for the segmentation of videos. Graph-based segmentation methods search for a minimum cut and therefore tend to not perform optimally with complex structures. The algorithm has initially been developed at the department of computer science at Freie Universitaet Berlin. The main developer, Gerald Friedland, is now faculty at the EECS department of the University of California at Berkeley and also a Principal Data Scientist at Lawrence Livermore National Lab. He continues to support the development through mentoring, e.g. in the Google Summer of Code.

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  Internet Security Awareness Training

  Internet Security Awareness Training (ISAT) is the training given to members of an organization regarding the protection of various information assets of that organization. ISAT is a subset of general security awareness training (SAT). Even small and medium enterprises are generally recommended to provide such training, but organizations that need to comply with government regulations (e.g., the Gramm–Leach–Bliley Act, the Payment Card Industry Data Security Standard, Health Insurance Portability and Accountability Act, Sarbanes–Oxley Act) normally require formal ISAT for annually for all employees. Often such training is provided in the form of online courses. ISAT, also referred to as Security Education, Training, and Awareness (SETA), organizations train and create awareness of information security management within their environment. It is beneficial to organizations when employees are well trained and feel empowered to take important actions to protect themselves and organizational data. The SETA program target must be based on user roles within organizations and for positions that expose the organizations to increased risk levels, specialized courses must be required. == Coverage == There are general topics to cover for the training, but it is necessary for each organization to have a coverage strategy based on its needs, as this will ensure the training is practical and captures critical topics relevant to the organization. As the threat landscape changes very frequently, organizations should continuously review their training programs to ensure relevance with current trends. Topics covered in ISAT include: Appropriate methods for protecting sensitive information on personal computer systems, including password policy Various computer security concerns, including spam, malware, phishing, social engineering, etc. Consequences of failure to properly protect information, including potential job loss, economic consequences to the firm, damage to individuals whose private records are divulged, and possible civil and criminal law penalties. Being Internet Security Aware means you understand that there are people actively trying to steal data that is stored within your organization's computers. (This often focuses on user names and passwords, so that criminal elements can ultimately get access to bank accounts and other high-value IT assets.) That is why it is important to protect the assets of the organization and stop that from happening. The general scope should include topics such as password security, Email phishing, Social engineering, Mobile device security, Sensitive data security, and Business communications. In contrast, those requiring specialized knowledge are usually required to take technical and in-depth training courses. Suppose an organization determines that it is best to use one of the available training tools on the market, it must ensure it sets objectives that the training can meet, including confirming the training will provide employees with the knowledge to understand risks and the behaviors needed in managing them, actions to take to prevent or detect security incidents, using language easily understandable by the trainees, and ensuring the pricing is reasonable. Organizations are recommended to base ISAT training content on employee roles and their culture; the policy should guide that training for all employees and gave the following as examples of sources of reference materials: National Institute of Standards and Technology (NIST) Special Publication 800-50, Building an Information Technology Security Awareness and Training Program International Standards Organization (ISO) 27002:2013, Information technology—Security techniques—Code of practice for information security controls International Standards Organization (ISO) 27001:2013, Information technology — Security techniques — Information security management systems COBIT 5 Appendix F.2, Detailed Guidance: Services, Infrastructure and Applications Enabler, Security Awareness The training must focus on current threats specific to an organization and the impacts if that materializes as a result of user actions. Including practical examples and ways of dealing with scenarios help users know the appropriate measures to take. It is a good practice to periodically train customers of specific organizations on threats they face from people with malicious intentions. Coverage strategy for SAT should be driven by an organization's policy. It can help truly determine the level of depth of the training and where it should be conducted at a global level or business unit level, or a combination of both. A policy also empowers a responsible party within the organization to run the training. == Importance == Studies show that well-structured security awareness training can significantly reduce the likelihood of cyber incidents caused by human error. According to the Ponemon Institute, organizations that implement regular security training experience up to 70% fewer successful phishing attacks. Additionally, a 2023 Verizon Data Breach Investigations Report found that 74% of breaches involve the human element, highlighting the need for continuous education. Employees are key in whether organizations are breached or not; there must be a policy on creating awareness and training them on emerging threats and actions to take in safeguarding sensitive information and reporting any observed unusual activity within the corporate environment. Research has shown that SAT has helped reduce cyber-attacks within organizations, especially when it comes to phishing, as trainees learned to identify these attack modes and give them the self-assurance to take action appropriately. There is an increase in phishing attacks, and it has become increasingly important for people to understand how to these attacks work, and the actions required to prevent these and SAT has shown a significant impact on the number of successful phishing attacks against organizations. == Compliance Requirements == Various regulations and laws mandate SAT for organizations in specific industries, including the Gramm–Leach–Bliley Act (GLBA) for the financial services, the Federal Information Security Modernization Act of 2014 for federal agencies, and the European Union's General Data Protection Regulation (GDPR). === Federal Information Security Modernization Act === Employees and contractors in federal agencies are required to receive Security Awareness Training annually, and the program needs to address job-related information security risks linked that provide them with the knowledge to lessen security risks. === Health Insurance Portability and Accountability Act === The Health Insurance Portability and Accountability Act has the Security Rule, and Privacy Rule requiring the creation of a security awareness training program and ensuring employees are trained accordingly. === Payment Card Industry Data Security Standard === The Payment Card Industry Security Standards Council, the governing council for stakeholders in the payment industry, formed by American Express, Discover, JCB International, MasterCard, and Visa that developed the DSS as a requirement for the payment industry. Requirement 12.6 requires member organizations to institute a formal security awareness program. There is a published guide for organizations to adhere to when setting up the program. === US States Training Regulations === Some States mandate Security Awareness Training whiles other do not but simply recommend voluntary training. Among states that require the training for its employees include: Colorado (The Colorado Information Security Act, Colorado Revised Statutes 24-37.5-401 et seq.) Connecticut (13 FAM 301.1-1 Cyber Security Awareness Training (PS800)) Florida (Florida Statutes Chapter 282) Georgia (Executive Order GA E.O.182 mandated training within 90 days of issue) Illinois (Cook County) Indiana (IN H 1240) Louisiana (Louisiana Division of Administration, Office of Technology Services p. 52: LA H 633) Maryland (20-07 IT Security Policy) Montana (Mandatory cyber training for executive branch state employees) Nebraska Nevada (agency-by-agency state employee requirement - State Security Standard 123 – IT Security) New Hampshire New Jersey ( NJ A 1654) North Carolina Ohio (IT-15 - Security Awareness and Training) Pennsylvania Texas Utah Vermont Virginia West Virginia (WV Code Section 5A-6-4a) == Training Techniques == Below are some common training techniques, even though some can be blended depending on the operating environment: Interactive video training – This technique allows users to be trained using two-way interactive audio and video instruction. Web-based training – This method allows employees or users to take the training independently and usually has a testing component to determine if learning has taken place. If not, users can be allowed to retake the course and test to ensure there is a complete understanding

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  Trustworthy computing

  The term trustworthy computing (TwC) has been applied to computing systems that are inherently secure, available, and reliable. It is particularly associated with the Microsoft initiative of the same name, launched in 2002. == History == Until 1995, there were restrictions on commercial traffic over the Internet. On, May 26, 1995, Bill Gates sent the "Internet Tidal Wave" memorandum to Microsoft executives assigning "...the Internet this highest level of importance..." but Microsoft's Windows 95 was released without a web browser as Microsoft had not yet developed one. The success of the web had caught them by surprise but by mid 1995, they were testing their own web server, and on August 24, 1995, launched a major online service, The Microsoft Network (MSN). The National Research Council recognized that the rise of the Internet simultaneously increased societal reliance on computer systems while increasing the vulnerability of such systems to failure and produced an important report in 1999, "Trust in Cyberspace". This report reviews the cost of un-trustworthy systems and identifies actions required for improvement. == Microsoft and Trustworthy Computing == Bill Gates launched Microsoft's "Trustworthy Computing" initiative with a January 15, 2002 memo, referencing an internal whitepaper by Microsoft CTO and Senior Vice President Craig Mundie. The move was reportedly prompted by the fact that they "...had been under fire from some of its larger customers–government agencies, financial companies and others–about the security problems in Windows, issues that were being brought front and center by a series of self-replicating worms and embarrassing attacks." such as Code Red, Nimda, Klez and Slammer. Four areas were identified as the initiative's key areas: Security, Privacy, Reliability, and Business Integrity, and despite some initial scepticism, at its 10-year anniversary it was generally accepted as having "...made a positive impact on the industry...". The Trustworthy Computing campaign was the main reason why Easter eggs disappeared from Windows, Office and other Microsoft products.

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  StyleGAN

  The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture introduced by Nvidia researchers in December 2018, and made source available in February 2019. StyleGAN depends on Nvidia's CUDA software, GPUs, and Google's TensorFlow, or Meta AI's PyTorch, which supersedes TensorFlow as the official implementation library in later StyleGAN versions. The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. Nvidia introduced StyleGAN3, described as an "alias-free" version, on June 23, 2021, and made source available on October 12, 2021. == History == A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces. StyleGAN was able to run on Nvidia's commodity GPU processors. In February 2019, Uber engineer Phillip Wang used the software to create the website This Person Does Not Exist, which displayed a new face on each web page reload. Wang himself has expressed amazement, given that humans are evolved to specifically understand human faces, that nevertheless StyleGAN can competitively "pick apart all the relevant features (of human faces) and recompose them in a way that's coherent." In September 2019, a website called Generated Photos published 100,000 images as a collection of stock photos. The collection was made using a private dataset shot in a controlled environment with similar light and angles. Similarly, two faculty at the University of Washington's Information School used StyleGAN to create Which Face is Real?, which challenged visitors to differentiate between a fake and a real face side by side. The faculty stated the intention was to "educate the public" about the existence of this technology so they could be wary of it, "just like eventually most people were made aware that you can Photoshop an image". The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. In 2021, a third version was released, improving consistency between fine and coarse details in the generator. Dubbed "alias-free", this version was implemented with PyTorch. === Illicit use === In December 2019, Facebook took down a network of accounts with false identities, and mentioned that some of them had used profile pictures created with machine learning techniques. == Architecture == === Progressive GAN === Progressive GAN is a method for training GAN for large-scale image generation stably, by growing a GAN generator from small to large scale in a pyramidal fashion. Like SinGAN, it decomposes the generator as G = G 1 ∘ G 2 ∘ ⋯ ∘ G N {\displaystyle G=G_{1}\circ G_{2}\circ \cdots \circ G_{N}} , and the discriminator as D = D N ∘ D N − 1 ∘ ⋯ ∘ D 1 {\displaystyle D=D_{N}\circ D_{N-1}\circ \cdots \circ D_{1}} . During training, at first only G N , D N {\displaystyle G_{N},D_{N}} are used in a GAN game to generate 4x4 images. Then G N − 1 , D N − 1 {\displaystyle G_{N-1},D_{N-1}} are added to reach the second stage of GAN game, to generate 8x8 images, and so on, until we reach a GAN game to generate 1024x1024 images. To avoid discontinuity between stages of the GAN game, each new layer is "blended in" (Figure 2 of the paper). For example, this is how the second stage GAN game starts: Just before, the GAN game consists of the pair G N , D N {\displaystyle G_{N},D_{N}} generating and discriminating 4x4 images. Just after, the GAN game consists of the pair ( ( 1 − α ) + α ⋅ G N − 1 ) ∘ u ∘ G N , D N ∘ d ∘ ( ( 1 − α ) + α ⋅ D N − 1 ) {\displaystyle ((1-\alpha )+\alpha \cdot G_{N-1})\circ u\circ G_{N},D_{N}\circ d\circ ((1-\alpha )+\alpha \cdot D_{N-1})} generating and discriminating 8x8 images. Here, the functions u , d {\displaystyle u,d} are image up- and down-sampling functions, and α {\displaystyle \alpha } is a blend-in factor (much like an alpha in image composing) that smoothly glides from 0 to 1. === StyleGAN === StyleGAN is designed as a combination of Progressive GAN with neural style transfer. The key architectural choice of StyleGAN-1 is a progressive growth mechanism, similar to Progressive GAN. Each generated image starts as a constant 4 × 4 × 512 {\displaystyle 4\times 4\times 512} array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance). At training time, usually only one style latent vector is used per image generated, but sometimes two ("mixing regularization") in order to encourage each style block to independently perform its stylization without expecting help from other style blocks (since they might receive an entirely different style latent vector). After training, multiple style latent vectors can be fed into each style block. Those fed to the lower layers control the large-scale styles, and those fed to the higher layers control the fine-detail styles. Style-mixing between two images x , x ′ {\displaystyle x,x'} can be performed as well. First, run a gradient descent to find z , z ′ {\displaystyle z,z'} such that G ( z ) ≈ x , G ( z ′ ) ≈ x ′ {\displaystyle G(z)\approx x,G(z')\approx x'} . This is called "projecting an image back to style latent space". Then, z {\displaystyle z} can be fed to the lower style blocks, and z ′ {\displaystyle z'} to the higher style blocks, to generate a composite image that has the large-scale style of x {\displaystyle x} , and the fine-detail style of x ′ {\displaystyle x'} . Multiple images can also be composed this way. === StyleGAN2 === StyleGAN2 improves upon StyleGAN in two ways. One, it applies the style latent vector to transform the convolution layer's weights instead, thus solving the "blob" problem. The "blob" problem roughly speaking is because using the style latent vector to normalize the generated image destroys useful information. Consequently, the generator learned to create a "distraction" by a large blob, which absorbs most of the effect of normalization (somewhat similar to using flares to distract a heat-seeking missile). Two, it uses residual connections, which helps it avoid the phenomenon where certain features are stuck at intervals of pixels. For example, the seam between two teeth may be stuck at pixels divisible by 32, because the generator learned to generate teeth during stage N-5, and consequently could only generate primitive teeth at that stage, before scaling up 5 times (thus intervals of 32). This was updated by the StyleGAN2-ADA ("ADA" stands for "adaptive"), which uses invertible data augmentation. It also tunes the amount of data augmentation applied by starting at zero, and gradually increasing it until an "overfitting heuristic" reaches a target level, thus the name "adaptive". === StyleGAN3 === StyleGAN3 improves upon StyleGAN2 by solving the "texture sticking" problem, which can be seen in the official videos. They analyzed the problem by the Nyquist–Shannon sampling theorem, and argued that the layers in the generator learned to exploit the high-frequency signal in the pixels they operate upon. To solve this, they proposed imposing strict lowpass filters between each generator's layers, so that the generator is forced to operate on the pixels in a way faithful to the continuous signals they represent, rather than operate on them as merely discrete signals. They further imposed rotational and translational invariance by using more signal filters. The resulting StyleGAN-3 is able to generate images that rotate and translate smoothly, and without texture sticking.

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  Digistar

  Digistar is the first computer graphics-based planetarium projection and content system. It was designed by Evans & Sutherland and released in 1983. The technology originally focused on accurate and high quality display of stars, including for the first time showing stars from points of view other than Earth's surface, travelling through the stars, and accurately showing celestial bodies from different times in the past and future. Beginning with the Digistar 3 the system now projects full-dome video. == Projector == Unlike modern full-dome systems, which use LCD, DLP, SXRD, or laser projection technology, the Digistar projection system was designed for projecting bright pinpoints of light representing stars. This was accomplished using a calligraphic display, a form of vector graphics, rather than raster graphics. The heart of the Digistar projector is a large cathode-ray tube (CRT). A phosphor plate is mounted atop the tube, and light is then dispersed by a large lens with a 160 degree field of view to cover the planetarium dome. The original lens bore the inscription: "August 1979 mfg. by Lincoln Optical Corp., L.A., CA for Evans and Sutherland Computer Corp., SLC, UT, Digital planetarium CRT projection lens, 43mm, f2.8, 160 degree field of view". The coordinates of the stars and wire-frame models to be displayed by the projector were stored in computer RAM in a display list. The display would read each set of coordinates in turn and drive the CRT's electron beam directly to those coordinates. If the electron beam was enabled while being moved a line would be painted on the phosphor plate. Otherwise, the electron beam would be enabled once at its destination and a star would be painted. Once all coordinates in the display list had been processed, the display would repeat from the top of the display list. Thus, the shorter the display list the more frequently the electron beam would refresh the charge on a given point on the phosphor plate, making the projection of the points brighter. In this way, the stars projected by Digistar were substantially brighter than could be achieved using a raster display, which has to touch every point on the phosphor plate before repeating. Likewise, the calligraphic technology allowed Digistar to have a darker black-level than full-dome projectors, since the portions of the phosphor plate representing dark sky were never hit by the electron beam. As it is only one tube, with no pixelated color filter screen, the Digistar projector is monochromatic. The Digistar projects a bright, phosphorescent green, though many (including both visitors and planetarians) report they cannot distinguish between this green and white. Additionally, unlike a raster display, the calligraphic display is not discretized into pixels, so the displayed stars were a more realistic single spot of light, without the blocky or ropy artifacts that are hard to avoid with raster graphics. Due to the use of vector graphics, as opposed to raster imaging, the Digistar does not have the resolution issues that many full-dome systems have. Thanks to this, and the brightness of the CRT, only one projector is needed to project on the entire dome, whereas most full-dome systems require up to six raster projectors, depending on dome size. The projector in the original Digistar was housed in a square pyramid-shaped sheathing. When powered on, the four sides at the tip of the pyramid would recede into the housing, exposing the lens and appearing as a cut-off pyramid. As Digistar II was being developed, many planetaria were sold Digistar LEA projectors. The LEA, called Digistar 1.5 by many users, was effectively a prototype of the D2 projector, compatible with Digistar and upgradable to Digistar II. There are no significant differences in performance between the LEA and the true D2. == History == Digistar was the brainchild of Stephen McAllister and Brent Watson, both of whom were long-time amateur astronomers and computer graphics engineers. In 1977, E&S had been consulting with Johnson Space Center regarding training simulators for astronauts. McAllister had been writing proof-of-concept software for this consultation and in summer 1977 entered the data for 400 bright stars and wrote the software to display them. Steve and Brent both originally saw the system's purpose as celestial navigation training. Brent, who had until recently worked at Hansen planetarium, asked his planetarium coworkers what they thought of a potential digital planetarium system, and then Steve and Brent both targeted the system toward planetaria. The primary goal of the planetarium system was to use computer graphics to overcome the limitation of traditional star ball technology that only allowed display of star fields from the point of view of Earth's surface. By using computer graphics the stars could be displayed from viewpoints in space, including simulating the appearance of space flight. Likewise, planets and moons within the Solar System could be displayed accurately for any time in history, from any point of view. The system used the location of real stars from the Yale Bright Star Catalogue, as well as random stars. A laboratory prototype of Digistar was used to generate the star fields and tactical displays in the 1982 science fiction film Star Trek II: The Wrath of Khan. Filming was done directly from the Digistar display in the lab. ILM projected the effort would take two weeks, but in fact it took from late November 1981 until mid-February 1982. The last shot recorded was what became the first entirely computer generated feature film sequence. It was the opening scene of the film, a rotating forward translation through a star field that lasted 3.5 minutes. It was recorded in one take, at a rate of one frame every 3.5 seconds, taking four hours for the shoot. The Digistar team members are credited in the film. After prototyping in labs at Evans and Sutherland the team repeatedly used Salt Lake City's Hansen planetarium to beta test the system at the planetarium at night. The Digistar team performed one week of shows at the planetarium as a fund raiser to benefit the planetarium. The company also later gave the planetarium an improved prototype Digistar to replace "Jake", the planetarium's aging Spitz planetarium projector. The first customer installation was to the newly constructed Universe Planetarium at the Science Museum of Virginia in 1983, the largest planetarium dome in the world at the time, for $595,000. By September 1986 there were four installed Digistars. Even at this point the long-term success of the product was very much in doubt, but as of 2019 Digistar has an installed base of over 550 planetaria. === Versions === Digistar (1983) Digistar II (1995) Digistar 3 (2002) Digistar 4 (2010?) Digistar 5 (2012) Digistar 6 (2016) Digistar 7 (2021) == Hardware == Digistar was driven by a VAX-11/780 minicomputer, with custom graphics hardware related to the E&S Picture System 2. Later versions of Digistar 1 used a DEC MicroVAX 2, driving a custom version of a PS/300. The original Digistar and Digistar 2 had a physical control panel that was used for running the star shows. This control panel was approximately 3' x 4' and contained a keyboard, a 6 DOF joystick, and a large array of back-lit buttons. One button that was used for moving the viewpoint forward in space was labeled "Boldly Go". Later iterations of Digistar replaced the physical control panel with a common graphical user interface. Digistar 3 was the first Digistar system to offer full-dome video in 2002, using six projectors. Digistar 4 was able to cover the dome using only two projectors. == System limitations == Though technologically advanced in its day, and the closest system to true full-dome video at the time of its release, the original Digistar and Digistar 2 are limited to only projecting dots and lines—meaning only wireframe models can be projected. To compensate for this, the projector is capable of defocusing specific models, blurring lines and dots together. An example of this is in the Digistar 2's built-in Milky Way model. The model is a circle of parallel lines that, when defocused, appear as the continuous band of the Milky Way across the sky. On more complex models, especially three-dimensional ones, brightness and details may be lost in this process, so it is not useful in all situations. The Digistar and Digistar 2 also suffer focus limitations. Because they use a single lens to cover the entire dome, it is difficult to gain perfect focus across the dome. Coupled with this, stars greater than a certain brightness are "multihit" points, meaning the projector draws two dots at the given position to accommodate the brightness of the star. Errors in the projector can lead the second dot to be slightly out-of-place with the first one. These two issues together, along with other issues that can occur within the projector's focus system, give the stars a blobby look. Some p

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  Texture artist

  A texture artist is an individual who develops textures for digital media, usually for video games, movies, web sites and television shows or things like 3D posters. These textures can be in the form of 2D or (rarely) 3D art that may be overlaid onto a polygon mesh to create a realistic 3D model. Texture artists often take advantage of web sites for the purposes of marketing their art and self-promotion of their skills with the goal of gaining employment from a professional game studio or to join a team working on a "mod" (modification) of an existing game in hopes of establishing industry or trade credentials.

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  Foreign key

  A foreign key is a set of attributes in a table that refers to the primary key of another table, linking these two tables. In the context of relational databases, a foreign key is subject to an inclusion dependency constraint that the tuples consisting of the foreign key attributes in one relation, R, must also exist in some other (not necessarily distinct) relation, S; furthermore that those attributes must also be a candidate key in S. In other words, a foreign key is a set of attributes that references a candidate key. For example, a table called TEAM may have an attribute, MEMBER_NAME, which is a foreign key referencing a candidate key, PERSON_NAME, in the PERSON table. Since MEMBER_NAME is a foreign key, any value existing as the name of a member in TEAM must also exist as a person's name in the PERSON table; in other words, every member of a TEAM is also a PERSON. == Summary == The table containing the foreign key is called the child table, and the table containing the candidate key is called the referenced or parent table. In database relational modeling and implementation, a candidate key is a set of zero or more attributes, the values of which are guaranteed to be unique for each tuple (row) in a relation. The value or combination of values of candidate key attributes for any tuple cannot be duplicated for any other tuple in that relation. Since the purpose of the foreign key is to identify a particular row of referenced table, it is generally required that the foreign key is equal to the candidate key in some row of the primary table, or else have no value (the NULL value.). This rule is called a referential integrity constraint between the two tables. Because violations of these constraints can be the source of many database problems, most database management systems provide mechanisms to ensure that every non-null foreign key corresponds to a row of the referenced table. For example, consider a database with two tables: a CUSTOMER table that includes all customer data and an ORDER table that includes all customer orders. Suppose the business requires that each order must refer to a single customer. To reflect this in the database, a foreign key column is added to the ORDER table (e.g., CUSTOMERID), which references the primary key of CUSTOMER (e.g. ID). Because the primary key of a table must be unique, and because CUSTOMERID only contains values from that primary key field, we may assume that, when it has a value, CUSTOMERID will identify the particular customer which placed the order. However, this can no longer be assumed if the ORDER table is not kept up to date when rows of the CUSTOMER table are deleted or the ID column altered, and working with these tables may become more difficult. Many real world databases work around this problem by 'inactivating' rather than physically deleting master table foreign keys, or by complex update programs that modify all references to a foreign key when a change is needed. Foreign keys play an essential role in database design. One important part of database design is making sure that relationships between real-world entities are reflected in the database by references, using foreign keys to refer from one table to another. Another important part of database design is database normalization, in which tables are broken apart and foreign keys make it possible for them to be reconstructed. Multiple rows in the referencing (or child) table may refer to the same row in the referenced (or parent) table. In this case, the relationship between the two tables is called a one to many relationship between the referencing table and the referenced table. In addition, the child and parent table may, in fact, be the same table, i.e. the foreign key refers back to the same table. Such a foreign key is known in SQL:2003 as a self-referencing or recursive foreign key. In database management systems, this is often accomplished by linking a first and second reference to the same table. A table may have multiple foreign keys, and each foreign key can have a different parent table. Each foreign key is enforced independently by the database system. Therefore, cascading relationships between tables can be established using foreign keys. A foreign key is defined as an attribute or set of attributes in a relation whose values match a primary key in another relation. The syntax to add such a constraint to an existing table is defined in SQL:2003 as shown below. Omitting the column list in the REFERENCES clause implies that the foreign key shall reference the primary key of the referenced table. Likewise, foreign keys can be defined as part of the CREATE TABLE SQL statement. If the foreign key is a single column only, the column can be marked as such using the following syntax: Foreign keys can be defined with a stored procedure statement. child_table: the name of the table or view that contains the foreign key to be defined. parent_table: the name of the table or view that has the primary key to which the foreign key applies. The primary key must already be defined. col3 and col4: the name of the columns that make up the foreign key. The foreign key must have at least one column and at most eight columns. == Referential actions == Because the database management system enforces referential constraints, it must ensure data integrity if rows in a referenced table are to be deleted (or updated). If dependent rows in referencing tables still exist, those references have to be considered. SQL:2003 specifies 5 different referential actions that shall take place in such occurrences: CASCADE RESTRICT NO ACTION SET NULL SET DEFAULT === CASCADE === Whenever rows in the parent (referenced) table are deleted (or updated), the respective rows of the child (referencing) table with a matching foreign key column will be deleted (or updated) as well. This is called a cascade delete (or update). === RESTRICT === A value cannot be updated or deleted when a row exists in a referencing or child table that references the value in the referenced table. Similarly, a row cannot be deleted as long as there is a reference to it from a referencing or child table. To understand RESTRICT (and CASCADE) better, it may be helpful to notice the following difference, which might not be immediately clear. The referential action CASCADE modifies the "behavior" of the (child) table itself where the word CASCADE is used. For example, ON DELETE CASCADE effectively says "When the referenced row is deleted from the other table (master table), then delete also from me". However, the referential action RESTRICT modifies the "behavior" of the master table, not the child table, although the word RESTRICT appears in the child table and not in the master table! So, ON DELETE RESTRICT effectively says: "When someone tries to delete the row from the other table (master table), prevent deletion from that other table (and of course, also don't delete from me, but that's not the main point here)." RESTRICT is not supported by Microsoft SQL 2012 and earlier. === NO ACTION === NO ACTION and RESTRICT are very much alike. The main difference between NO ACTION and RESTRICT is that with NO ACTION the referential integrity check is done after trying to alter the table. RESTRICT does the check before trying to execute the UPDATE or DELETE statement. Both referential actions act the same if the referential integrity check fails: the UPDATE or DELETE statement will result in an error. In other words, when an UPDATE or DELETE statement is executed on the referenced table using the referential action NO ACTION, the DBMS verifies at the end of the statement execution that none of the referential relationships are violated. This is different from RESTRICT, which assumes at the outset that the operation will violate the constraint. Using NO ACTION, the triggers or the semantics of the statement itself may yield an end state in which no foreign key relationships are violated by the time the constraint is finally checked, thus allowing the statement to complete successfully. === SET NULL, SET DEFAULT === In general, the action taken by the DBMS for SET NULL or SET DEFAULT is the same for both ON DELETE or ON UPDATE: the value of the affected referencing attributes is changed to NULL for SET NULL, and to the specified default value for SET DEFAULT. === Triggers === Referential actions are generally implemented as implied triggers (i.e. triggers with system-generated names, often hidden.) As such, they are subject to the same limitations as user-defined triggers, and their order of execution relative to other triggers may need to be considered; in some cases it may become necessary to replace the referential action with its equivalent user-defined trigger to ensure proper execution order, or to work around mutating-table limitations. Another important limitation appears with transaction isolation: your changes to a row may not be able to fully cascade because the row is ref

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  Superquadrics

  In mathematics, the superquadrics or super-quadrics (also superquadratics) are a family of geometric shapes defined by formulas that resemble those of ellipsoids and other quadrics, except that the squaring operations are replaced by arbitrary powers. They can be seen as the three-dimensional relatives of the superellipses. The term may refer to the solid object or to its surface, depending on the context. The equations below specify the surface; the solid is specified by replacing the equality signs by less-than-or-equal signs. The superquadrics include many shapes that resemble cubes, octahedra, cylinders, lozenges and spindles, with rounded or sharp corners. Because of their flexibility and relative simplicity, they are popular geometric modeling tools, especially in computer graphics. It becomes an important geometric primitive widely used in computer vision, robotics, and physical simulation. Some authors, such as Alan Barr, define "superquadrics" as including both the superellipsoids and the supertoroids. In modern computer vision literatures, superquadrics and superellipsoids are used interchangeably, since superellipsoids are the most representative and widely utilized shape among all the superquadrics. Comprehensive coverage of geometrical properties of superquadrics and methods of their recovery from range images and point clouds are covered in several computer vision literatures. == Formulas == === Implicit equation === The surface of the basic superquadric is given by | x | r + | y | s + | z | t = 1 {\displaystyle \left|x\right|^{r}+\left|y\right|^{s}+\left|z\right|^{t}=1} where r, s, and t are positive real numbers that determine the main features of the superquadric. Namely: less than 1: a pointy octahedron modified to have concave faces and sharp edges. exactly 1: a regular octahedron. between 1 and 2: an octahedron modified to have convex faces, blunt edges and blunt corners. exactly 2: a sphere greater than 2: a cube modified to have rounded edges and corners. infinite (in the limit): a cube Each exponent can be varied independently to obtain combined shapes. For example, if r=s=2, and t=4, one obtains a solid of revolution which resembles an ellipsoid with round cross-section but flattened ends. This formula is a special case of the superellipsoid's formula if (and only if) r = s. If any exponent is allowed to be negative, the shape extends to infinity. Such shapes are sometimes called super-hyperboloids. The basic shape above spans from -1 to +1 along each coordinate axis. The general superquadric is the result of scaling this basic shape by different amounts A, B, C along each axis. Its general equation is | x A | r + | y B | s + | z C | t = 1. {\displaystyle \left|{\frac {x}{A}}\right|^{r}+\left|{\frac {y}{B}}\right|^{s}+\left|{\frac {z}{C}}\right|^{t}=1.} === Parametric description === Parametric equations in terms of surface parameters u and v (equivalent to longitude and latitude if m equals 2) are x ( u , v ) = A g ( v , 2 r ) g ( u , 2 r ) y ( u , v ) = B g ( v , 2 s ) f ( u , 2 s ) z ( u , v ) = C f ( v , 2 t ) − π 2 ≤ v ≤ π 2 , − π ≤ u < π , {\displaystyle {\begin{aligned}x(u,v)&{}=Ag\left(v,{\frac {2}{r}}\right)g\left(u,{\frac {2}{r}}\right)\\y(u,v)&{}=Bg\left(v,{\frac {2}{s}}\right)f\left(u,{\frac {2}{s}}\right)\\z(u,v)&{}=Cf\left(v,{\frac {2}{t}}\right)\\&-{\frac {\pi }{2}}\leq v\leq {\frac {\pi }{2}},\quad -\pi \leq u<\pi ,\end{aligned}}} where the auxiliary functions are f ( ω , m ) = sgn ⁡ ( sin ⁡ ω ) | sin ⁡ ω | m g ( ω , m ) = sgn ⁡ ( cos ⁡ ω ) | cos ⁡ ω | m {\displaystyle {\begin{aligned}f(\omega ,m)&{}=\operatorname {sgn}(\sin \omega )\left|\sin \omega \right|^{m}\\g(\omega ,m)&{}=\operatorname {sgn}(\cos \omega )\left|\cos \omega \right|^{m}\end{aligned}}} and the sign function sgn(x) is sgn ⁡ ( x ) = { − 1 , x < 0 0 , x = 0 + 1 , x > 0. {\displaystyle \operatorname {sgn}(x)={\begin{cases}-1,&x<0\\0,&x=0\\+1,&x>0.\end{cases}}} === Spherical product === Barr introduces the spherical product which given two plane curves produces a 3D surface. If f ( μ ) = ( f 1 ( μ ) f 2 ( μ ) ) , g ( ν ) = ( g 1 ( ν ) g 2 ( ν ) ) {\displaystyle f(\mu )={\begin{pmatrix}f_{1}(\mu )\\f_{2}(\mu )\end{pmatrix}},\quad g(\nu )={\begin{pmatrix}g_{1}(\nu )\\g_{2}(\nu )\end{pmatrix}}} are two plane curves then the spherical product is h ( μ , ν ) = f ( μ ) ⊗ g ( ν ) = ( f 1 ( μ ) g 1 ( ν ) f 1 ( μ ) g 2 ( ν ) f 2 ( μ ) ) {\displaystyle h(\mu ,\nu )=f(\mu )\otimes g(\nu )={\begin{pmatrix}f_{1}(\mu )\ g_{1}(\nu )\\f_{1}(\mu )\ g_{2}(\nu )\\f_{2}(\mu )\end{pmatrix}}} This is similar to the typical parametric equation of a sphere: x = x 0 + r sin ⁡ θ cos ⁡ φ y = y 0 + r sin ⁡ θ sin ⁡ φ ( 0 ≤ θ ≤ π , 0 ≤ φ < 2 π ) z = z 0 + r cos ⁡ θ {\displaystyle {\begin{aligned}x&=x_{0}+r\sin \theta \;\cos \varphi \\y&=y_{0}+r\sin \theta \;\sin \varphi \qquad (0\leq \theta \leq \pi ,\;0\leq \varphi <2\pi )\\z&=z_{0}+r\cos \theta \end{aligned}}} which give rise to the name spherical product. Barr uses the spherical product to define quadric surfaces, like ellipsoids, and hyperboloids as well as the torus, superellipsoid, superquadric hyperboloids of one and two sheets, and supertoroids. == Plotting code == The following GNU Octave code generates a mesh approximation of a superquadric:

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  Linux color management

  Linux color management has the same goal as the color management systems (CMS) for other operating systems, which is to achieve the best possible color reproduction throughout an imaging workflow from its source (camera, video, scanner, etc.), through imaging software (Digikam, darktable, RawTherapee, GIMP, Krita, Scribus, etc.), and finally onto an output medium (monitor, video projector, printer, etc.). In particular, color management attempts to enable color consistency across media and throughout a color-managed workflow. Linux color management relies on the use of accurate ICC (International Color Consortium) and DCP (DNG Color Profile) profiles describing the behavior of input and output devices, and color-managed applications that are aware of these profiles. These applications perform gamut conversions between device profiles and color spaces. Gamut conversions, based on accurate device profiles, are the essence of color management. Historically, color management was not an initial design consideration of the X Window System on which much of Linux graphics support rests, and thus color-managed workflows have been somewhat more challenging to implement on Linux than on other OS's such as Microsoft Windows or macOS. This situation is now being progressively remedied, and color management under Linux, while functional, has not yet acquired mature status. Although it is now possible to obtain a consistent color-managed workflow under Linux, certain problems still remain: The absence of a central user control panel for color settings. Some hardware devices for color calibration lack Linux drivers, firmware or accessory data. Since ICC color profiles are written to an open specification, they are compatible across operating systems. Hence, a profile produced on one OS should work on any other OS given the availability of the necessary software to read it and perform the gamut conversions. This can be used as a workaround for the lack of support for certain spectrophotometers or colorimeters under Linux: one can simply produce a profile on a different OS and then use it in a Linux workflow. Additionally, certain hardware, such as most printers and certain monitors, can be calibrated under another OS and then used in a fully color-managed workflow on Linux. The popular Ubuntu Linux distribution added initial color management in the 11.10 release (the "Oneiric Ocelot" release). == Requirements for a color-managed workflow == Accurate device profiles obtained with source or output characterization software. Correctly loaded video card lookup tables (LUTs) (or monitor profiles that do not require LUT adjustments). Color-managed applications that are configured to use a correct monitor profile and input/output profiles, with support for control over the rendering intent and black point compensation. Calibration and profiling requires: for input devices (scanner, camera, etc.) a color target which the profiling software will compare to the manufacturer-provided color values of the target. or for output devices (monitor, printer, etc.) a reading with a specific device (spectrophotometer, colorimeter or spectrocolorimeter) of the color patch values and comparing the measured values against the values originally sent for output. === Monitor calibration and profiling === One of the critical elements in any color-managed workflow is the monitor, because, at one step or another, handling and making color adaptation through imaging software is required for most images, thus the ability of the monitor to present accurate colors is crucial. Monitor color management consists of calibration and profiling. The first step, calibration, is done by adjusting the monitor controls and the output of the graphics card (via calibration curves) to match user-definable characteristics, such as brightness, white point and gamma. The calibration settings are stored in a .cal file. The second step, profiling (characterization), involves measuring the calibrated display's response and recording it in a color profile. The profile is stored in an .icc file ("ICC file"). For convenience, the calibration settings are usually stored together with the profile in the ICC file. Note that .icm files are identical to .icc files - the difference is only in the name. Seeing correct colors requires using a monitor profile-aware application, together with the same calibration used when profiling the monitor. Calibration alone does not yield accurate colors. If a monitor was calibrated before it was profiled, the profile will only yield correct colors when used on the monitor with the same calibration (the same monitor control adjustments and the same calibration curves loaded into the video card's lookup table). macOS has built-in support for loading calibration curves and installing a system-wide color profile. Windows 7 onward allows loading calibration curves, though this functionality must be enabled manually. Linux and older versions of Windows require using a standalone LUT loader. === Device profiles === ICC profiles are cross-platform and can thus be created on other operating systems and used under Linux. Monitor profiles, however, require some additional attention. Since a monitor profile depends both on the monitor itself and on the video card, a monitor profile should only be used with the same monitor and video card with which it was created. The monitor settings should not be adjusted after creating the profile. In addition, since most calibration software use LUT adjustments during calibration, the corresponding LUTs must be loaded every time the display server (X11, Wayland) is started (e.g. with every graphical login). In the unlikely case of a colorimeter being unsupported by Linux, a profile created under Windows or macOS can be used under Linux. === Display-channel lookup tables === There are two approaches to loading display channel LUTs: Create a profile that does not modify video card LUTs and thus does not require LUTs be loaded later on. Ideally, this approach would rely on DDC-capable monitors—the internal monitor settings of which are set via calibration software. Unfortunately, monitors capable of making these adjustments through DDC are not common and are generally expensive. There is only one calibration software on Linux that can interact with a DDC monitor. For mainstream monitors, a couple of options exist: BasICColor software, which works with most colorimeters on the market, allows one to adjust display output via the monitor interface, and then to choose a "Profile, do not calibrate" option. By doing this, one can create a profile that does not require video card LUT adjustments. For EyeOne devices, EyeOne Match allows the user to calibrate to "Native" gamma and white point targets, which results in the LUT adjustment curves displayed after the calibration as a simple, linear 1:1 mapping (a straight line from corner to corner). Both BasICColor and EyeOne Match do not presently run under Linux but they are capable of creating a profile that does not require LUT adjustments. Use an LUT loader to actually load the LUT adjustments contained within the profile prepared during calibration. According to the documentation, these loaders do not modify the video card LUT by itself, but achieve the same type of adjustment by modifying the X server gamma ramp. Loaders are available for Linux distributions that use X.org or XFree86—the two most popular X servers on Linux. Other X servers are not guaranteed to work with the currently available loaders. There are two LUT loaders available for Linux: Xcalib is one such loader, and although it is a command-line utility, it is quite easy to use. dispwin is a part of Argyll CMS. If, for any reason, the LUT cannot be loaded, it is still recommended to go through the initial stages of calibration where a user is asked by calibration software to make some manual adjustments to the monitor, as this will often improve display linearity and also provide information on its color temperature. This is especially recommended for CRT monitors. === Color-managed applications === In ICC-aware applications, it is important to make sure the correct profiles are assigned to devices, mainly to the monitor and the printer. Some Linux applications can auto-detect the monitor profile, while others requires that it is specified manually. Although there is no designated place to store device profiles on Linux, /usr/share/color/icc/ has become the de facto standard. Most applications running under WINE have not been fully tested for color accuracy. While 8-bpp programs can have some color resolution difficulties due to depth conversion errors, colors in higher-depth applications should be accurate, as long as those programs perform their gamut conversions based on the same monitor profile as that used for loading the LUT, granted that the corresponding LUT adjustments are loaded. == List of color-managed applications == darktabl

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  Screen space directional occlusion

  Screen space directional occlusion (SSDO) is a computer graphics technique enhancing screen space ambient occlusion (SSAO) by taking direction into account to sample the ambient light (both the light coming directly at an object, as well as the light reflected off of the object directly behind it), to better approximate global illumination. SSDO was introduced by Tobias Ritschel, Thorsten Grosch, and Hans-Peter Seidel in their 2009 ACM Symposium on Interactive 3D Graphics and Games paper Approximating dynamic global illumination in image space, which describes it as extending SSAO to directional occlusion with one diffuse indirect bounce of light; later literature notes that SSDO still suffers from common screen-space artifacts such as noise and banding. == Method == The original SSDO paper describes a two-pass screen-space approach, with one pass for direct lighting and a second pass for indirect bounces. Later literature describes SSDO as assuming a general shadowing direction that allows color bleeding and a single light bounce.

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  Attack path management

  Attack path management is a cybersecurity technique that involves the continuous discovery, mapping, and risk assessment of identity-based attack paths. Attack path management is distinct from other computer security mitigation strategies in that it does not rely on finding individual attack paths through vulnerabilities, exploits, or offensive testing. Rather, attack path management techniques analyze all attack paths present in an environment based on active identity management policies, authentication configurations, and active authenticated "sessions" between objects. == Overview == Attack path management relies on concepts such as mapping and removing attack paths, identifying attack path choke points, and remediation of attack paths. Identity-based attacks are present in most publicly disclosed breaches, whether through social engineering to gain initial access to Active Directories or lateral movement for privilege escalation. Attackers require privileges to attack an environment’s most sensitive segments. Attack path management often involves removing out-of-date privileges and privilege assignments given to overly large groups. In attack path management, attack graphs are used to represent how a network of machines’ security is vulnerable to attack. The nodes in an attack graph represent principals and other objects such as machines, accounts, and security groups. The edges in an attack graph represent the links and relationships between nodes. Some nodes are easy to penetrate due to short paths from regular users to domain admins, resulting in focal points of concentrated network traffic, which are known as attack path choke points. Attack graphs are often analyzed using algorithms and visualization. Attack path management also identifies tier 0 assets, which are considered the most vulnerable because they have direct or indirect control of an Active Directory or Microsoft Entra ID environment.

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  SMBGhost

  SMBGhost (or SMBleedingGhost or CoronaBlue) is a type of security vulnerability, with wormlike features, that affects Windows 10 computers and was first reported publicly on 10 March 2020. == Security vulnerability == A proof of concept (PoC) exploit code was published 1 June 2020 on GitHub by a security researcher. The code could possibly spread to millions of unpatched computers, resulting in as much as tens of billions of dollars in losses. Microsoft recommends all users of Windows 10 versions 1903 and 1909 and Windows Server versions 1903 and 1909 to install patches, and states, "We recommend customers install updates as soon as possible as publicly disclosed vulnerabilities have the potential to be leveraged by bad actors ... An update for this vulnerability was released in March [2020], and customers who have installed the updates, or have automatic updates enabled, are already protected." Workarounds, according to Microsoft, such as disabling SMB compression and blocking port 445, may help but may not be sufficient. According to the advisory division of Homeland Security, "Malicious cyber actors are targeting unpatched systems with the new [threat], ... [and] strongly recommends using a firewall to block server message block ports from the internet and to apply patches to critical- and high-severity vulnerabilities as soon as possible."

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  Load file

  A load file in the litigation community is commonly referred to as the file used to import data (coded, captured or extracted data from ESI processing) into a database; or the file used to link images. These load files carry commands, commanding the software to carry out certain functions with the data found in them. Load files are usually ASCII text files that have delimited fields of information. Such load files may have data about documents to be imported into a document management software such as Concordance or Summation. Or they may have the path or directory where images may reside so that the software can link such images to their corresponding records. Some database programs take one load file for importing images and another for importing data while others take only one load file for both pieces of information. OCR or Search-able Text which is considered "data" is also imported into most database programs via the same load files. Though some people prefer to load the OCR into their databases by running a separate command to search and find the desired text. Commonly used databases and their corresponding file extensions are: Summation (DII , CSV), Concordance (OPT, DAT), Sanction (SDT), IPRO (LFP), Ringtail (MDB) and DB/TextWorks (TXT).

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  Kuaishou

  Kuaishou Technology is a Chinese publicly traded partly state-owned holding company based in Haidian District, Beijing, that was founded in 2011 by Hua Su (Chinese: 宿华) and Cheng Yixiao (Chinese: 程一笑). The company, listed on the Hong Kong Stock Exchange, is known for developing a mobile app for sharing users' short videos, a social network, and video special effects editor. The app is known as Kwai in many countries outside of China. It is also known as Snack Video in India, Pakistan and Indonesia. == Ownership and governance == Kuaishou's overseas team is led by the former CEO of the application 99, and staff from Google, Facebook, Netflix, and TikTok were recruited to lead the company's international expansion. The China Internet Investment Fund, a state-owned enterprise controlled by the Cyberspace Administration of China, holds a golden share ownership stake in Kuaishou. == History == Kuaishou is China's first short video platform that was developed in 2011 by engineer Hua Su and Cheng Yixiao. Prior to co-founding Kuaishou, Su Hua had worked for both Google and Baidu as a software engineer. The company is headquartered in Haidian District, Beijing. Kuaishou's predecessor "GIF Kuaishou" was founded in March 2011. GIF Kuaishou was a mobile app with which users could make and share GIF pictures. In 2013, Kuaishou became a short-video social platform. By 2013, the app had reached 100 million daily users. By 2019, it had exceeded 200 million active daily users. In March 2017, Kuaishou closed a US$350 million investment round that was led by Tencent. In January 2018, Forbes estimated the company's valuation to be US$18 billion. In April 2018, Kuaishou's app was briefly banned from Chinese app stores after China Central Television (CCTV) reported on the platform popularizing videos of teenage mothers. In 2019, the company announced a partnership with the People's Daily, an official newspaper of the Central Committee of the Chinese Communist Party, to help it experiment with the use of artificial intelligence in news. In June 2020, following the start of the 2020–2021 China–India skirmishes, the Government of India banned Kwai along with 58 other apps, citing "data and privacy issues". In January 2021, Kuaishou announced it was planning an initial public offering (IPO) to raise approximately US$5 billion. Kuaishou's stock completed its first day of trading at $300 Hong Kong dollars (HKD) (US$38.70), more than doubling its initial offer price, and causing its market value to rise to over $1 trillion HKD (US$159 billion). In February 2021, Kuaishou made a debut on the Hong Kong Stock Exchange, with its shares soaring by 194% at the opening. The company subsequently encountered major setbacks as a result of heightened regulatory restrictions on Chinese internet firms, which contributed to its share price falling by nearly 80% from its post-IPO peak. By December 2021, Kuaishou announced a major reorganization, including the layoff of 30% of its staff, primarily targeting mid-level employees earning an annual salary of $157,000 or more. This restructuring aimed to cut costs and mitigate financial losses. In October 2022, state-owned Beijing Radio and Television Station took a minority ownership stake in Kuaishou. In April 2024, a Financial Times article citing current and former Kuaishou employees stated that the company has been running an ageist redundancy programme known internally as "Limestone", culling workers in their mid-30s. In June 2024, Kuaishou and the Sichuan international communication center launched a branch center in São Paulo, Brazil. In June 2024, Kuaishou released its diffusion transformer text-to-video model, Kling, which they claimed could generate two minutes of video at 30 frames per second and in 1080p resolution. The model has been compared to that of OpenAI's Sora text-to-video model. It is accessible to the public on Kuaishou's video editing app KwaiCut via signing up for a waitlist with a Chinese phone number. In December 2025, Kuaishou came under a cyberattack which led to a temporary influx of violent and pornographic content. == Popularity == As of 2019, it had a worldwide user base of over 200 million, leading the "Most Downloaded" lists of the Google Play and Apple App Store in eight countries, such as Brazil, where it was introduced in 2019. Its main short-video platform competitor was Douyin, which is known as TikTok outside China. Compared to Douyin, Kuaishou is more popular with older users living outside China's Tier 1 cities. Its initial popularity came from videos of Chinese rural life. The app is particularly well known for its "rustic" aesthetic and is popular among rural people. Kuaishou also relied more on e-commerce revenue than on advertising revenue compared to its main competitor. == Reception == Kwai (as the app is called outside of China) was banned in India in 2020 along with other short video apps like TikTok. Kuaishou then released the clone SnackVideo, which was subsequently also banned. The app is one of the most popular social media platforms in Brazil, where Kuaishou partnered with creators to make telenovela style content, and appeals to football fans by working with football teams CR Flamengo and Santos FC and sponsoring the tournament Copa América. Kwai was notable in Brazil for spreading information (and misinformation) about the COVID-19 vaccine and political misinformation. === Manjiao Wenhua === "Manjiao wenhua" (慢脚文化) is a sarcasm term on Chinese internet on the unethical or illegal contents on Kuaishou. State broadcaster China Central Television (CCTV) reported that many contents are about child pregnancy. "Dating, pregnancy, bearing a child...these are strictly prohibited in the real time by a minor, but these contents can easily shown to audiences here." In addition, many students from primary or secondary schools make a pose of smoking. Wang Zhenhui (王贞会) from CUPSL stated that these kinds of bad values will give negative effects to the minors.

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