AI Generator Xi Pics

AI Generator Xi Pics — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Autoscaling

Autoscaling, (also written as auto scaling, auto-scaling, or known as automatic scaling), is a method used in cloud computing that dynamically adjusts the amount of computational resources in a server farm - typically measured by the number of active servers - automatically based on the load on the farm. For example, the number of servers running behind a web application may be increased or decreased automatically based on the number of active users on the site. Since such metrics may change dramatically throughout the course of the day, and servers are a limited resource that cost money to run even while idle, there is often an incentive to run "just enough" servers to support the current load while still being able to support sudden and large spikes in activity. Autoscaling is helpful for such needs, as it can reduce the number of active servers when activity is low, and launch new servers when activity is high. Autoscaling is closely related to, and builds upon, the idea of load balancing. == Advantages == Autoscaling offers the following advantages: For companies running their own web server infrastructure, autoscaling typically means allowing some servers to go to sleep during times of low load, saving on electricity costs (as well as water costs if water is being used to cool the machines). For companies using infrastructure hosted in the cloud, autoscaling can mean lower bills, because most cloud providers charge based on total usage rather than maximum capacity. Even for companies that cannot reduce the total compute capacity they run or pay for at any given time, autoscaling can help by allowing the company to run less time-sensitive workloads on machines that get freed up by autoscaling during times of low traffic. Autoscaling solutions, such as the one offered by Amazon Web Services, can also take care of replacing unhealthy instances and therefore protecting somewhat against hardware, network, and application failures. Autoscaling can offer greater uptime and more availability in cases where production workloads are variable and unpredictable. Autoscaling differs from having a fixed daily, weekly, or yearly cycle of server use in that it is responsive to actual usage patterns, and thus reduces the potential downside of having too few or too many servers for the traffic load. For instance, if traffic is usually lower at midnight, then a static scaling solution might schedule some servers to sleep at night, but this might result in downtime on a night where people happen to use the Internet more (for instance, due to a viral news event). Autoscaling, on the other hand, can handle unexpected traffic spikes better. == Terminology == In the list below, we use the terminology used by Amazon Web Services (AWS). However, alternative names are noted and terminology that is specific to the names of Amazon services is not used for the names. == Practice == === Amazon Web Services (AWS) === Amazon Web Services launched the Amazon Elastic Compute Cloud (EC2) service in August 2006, that allowed developers to programmatically create and terminate instances (machines). At the time of initial launch, AWS did not offer autoscaling, but the ability to programmatically create and terminate instances gave developers the flexibility to write their own code for autoscaling. Third-party autoscaling software for AWS began appearing around April 2008. These included tools by Scalr and RightScale. RightScale was used by Animoto, which was able to handle Facebook traffic by adopting autoscaling. On May 18, 2009, Amazon launched its own autoscaling feature along with Elastic Load Balancing, as part of Amazon Elastic Compute Cloud. Autoscaling is now an integral component of Amazon's EC2 offering. Autoscaling on Amazon Web Services is done through a web browser or the command line tool. In May 2016 Autoscaling was also offered in AWS ECS Service. On-demand video provider Netflix documented their use of autoscaling with Amazon Web Services to meet their highly variable consumer needs. They found that aggressive scaling up and delayed and cautious scaling down served their goals of uptime and responsiveness best. In an article for TechCrunch, Zev Laderman, the co-founder and CEO of Newvem, a service that helps optimize AWS cloud infrastructure, recommended that startups use autoscaling in order to keep their Amazon Web Services costs low. Various best practice guides for AWS use suggest using its autoscaling feature even in cases where the load is not variable. That is because autoscaling offers two other advantages: automatic replacement of any instances that become unhealthy for any reason (such as hardware failure, network failure, or application error), and automatic replacement of spot instances that get interrupted for price or capacity reasons, making it more feasible to use spot instances for production purposes. Netflix's internal best practices require every instance to be in an autoscaling group, and its conformity monkey terminates any instance not in an autoscaling group in order to enforce this best practice. === Microsoft's Windows Azure === On June 27, 2013, Microsoft announced that it was adding autoscaling support to its Windows Azure cloud computing platform. Documentation for the feature is available on the Microsoft Developer Network. === Oracle Cloud === Oracle Cloud Platform allows server instances to automatically scale a cluster in or out by defining an auto-scaling rule. These rules are based on CPU and/or memory utilization and determine when to add or remove nodes. === Google Cloud Platform === On November 17, 2014, the Google Compute Engine announced a public beta of its autoscaling feature for use in Google Cloud Platform applications. As of March 2015, the autoscaling tool is still in Beta. === Facebook === In a blog post in August 2014, a Facebook engineer disclosed that the company had started using autoscaling to bring down its energy costs. The blog post reported a 27% decline in energy use for low traffic hours (around midnight) and a 10-15% decline in energy use over the typical 24-hour cycle. === Kubernetes Horizontal Pod Autoscaler === Kubernetes Horizontal Pod Autoscaler automatically scales the number of pods in a replication controller, deployment or replicaset based on observed CPU utilization (or, with beta support, on some other, application-provided metrics) == Alternative autoscaling decision approaches == Autoscaling by default uses reactive decision approach for dealing with traffic scaling: scaling only happens in response to real-time changes in metrics. In some cases, particularly when the changes occur very quickly, this reactive approach to scaling is insufficient. Two other kinds of autoscaling decision approaches are described below. === Scheduled autoscaling approach === This is an approach to autoscaling where changes are made to the minimum size, maximum size, or desired capacity of the autoscaling group at specific times of day. Scheduled scaling is useful, for instance, if there is a known traffic load increase or decrease at specific times of the day, but the change is too sudden for reactive approach based autoscaling to respond fast enough. AWS autoscaling groups support scheduled scaling. === Predictive autoscaling === This approach to autoscaling uses predictive analytics. The idea is to combine recent usage trends with historical usage data as well as other kinds of data to predict usage in the future, and autoscale based on these predictions. For parts of their infrastructure and specific workloads, Netflix found that Scryer, their predictive analytics engine, gave better results than Amazon's reactive autoscaling approach. In particular, it was better for: Identifying huge spikes in demand in the near future and getting capacity ready a little in advance Dealing with large-scale outages, such as failure of entire availability zones and regions Dealing with variable traffic patterns, providing more flexibility on the rate of scaling out or in based on the typical level and rate of change in demand at various times of day On November 20, 2018, AWS announced that predictive scaling would be available as part of its autoscaling offering.
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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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Soterml

SoTerML (Soil and Terrain Markup Language) is a XML-based markup language for storing and exchanging soil and terrain related data. SoTerML development is being done within The e-SoTer Platform. GEOSS plans a global Earth Observation System and, within this framework, the e-SOTER project addresses the felt need for a global soil and terrain database. The Centre for Geospatial Science (Currently Nottingham Geospatial Institute) at the University of Nottingham has initiated the development since January 2009. Further development and maintenance is currently handled in National Soil Resources Institute (NSRI) at Cranfield University, UK. The role of CGS is within the development of the e-SOTER dissemination platform, which is based on INSPIRE principles. The SoTerML development included: 1. Development of a data dictionary for nomenclatures and various data sources (data and metadata). 2. Development of an exchange format/procedures from the World Reference Base 2006.
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NAPLPS

NAPLPS (North American Presentation Layer Protocol Syntax) is a graphics language for use originally with videotex and teletext services. NAPLPS was developed from the Telidon system developed in Canada, with a small number of additions from AT&T Corporation. The basics of NAPLPS were later used as the basis for several other microcomputer-based graphics systems. == History == The Canadian Communications Research Centre (CRC), based in Ottawa, had been working on various graphics systems since the late 1960s, much of it led by Herb Bown. Through the 1970s they turned their attention to building out a system of "picture description instructions", which encoded graphics commands as a text stream. Graphics were encoded as a series of instructions (graphics primitives) each represented by a single ASCII character. Graphic coordinates were encoded in multiple 6-bit strings of XY coordinate data, flagged to place them in the printable ASCII range so that they could be transmitted with conventional text transmission techniques. ASCII SI/SO characters were used to differentiate the text from graphic portions of a transmitted "page". These instructions were decoded by separate programs to produce graphics output, on a plotter for instance. Other work produced a fully interactive version. In 1975, the CRC gave a contract to Norpak to develop an interactive graphics terminal that could decode the instructions and display them on a color display. During this period, a number of companies were developing the first teletext systems, notably the BBC's Ceefax system. Ceefax encoded character data into the lines in the vertical blanking interval of normal television signals where they could not be seen on-screen, and then used a buffer and decoder in the user's television to convert these into "pages" of text on the display. The Independent Broadcasting Authority quickly introduced their own ORACLE system, and the two organizations subsequently agreed to use a single standard, the "Broadcast Teletext Specification". This later became World System Teletext. At about the same time, other organizations were developing videotex systems, similar to teletext except they used modems to transmit their data instead of television signals. This was potentially slower and used up a telephone line, but had the major advantage of allowing the user to transmit data back to the sender. The UK's General Post Office developed a system using the Ceefax/ORACLE standard, launching it as Prestel, while France prepared the first steps for its ultimately very successful Minitel system, using a rival display standard called Antiope. By 1977, the Norpak system was running, and from this work the CRC decided to create their own teletext/videotext system. Unlike the systems being rolled out in Europe, the CRC decided from the start that the system should be able to run on any combination of communications links. For instance, it could use the vertical blanking interval to send data to the user, and a modem to return selections to the servers. It could be used in a one-way or two-way system. In teletext mode, character codes were sent to users' televisions by encoding them as dot patterns in the vertical blanking interval of the video signal. Various technical "tweaks" and details of the NTSC signals used by North American televisions allowed the downstream videotex channel to increase to 600 bit/s, about twice that used in the European systems. In videotext mode, Bell 202 modems were typical, offering a 1,200 bit/s download rate. A set top box attached to the TV decoded these signals back into text and graphics pages, which the user could select among. The system was publicly launched as Telidon on August 15, 1978. Compared to the European standards, the CRC system was faster, bi-directional, and offered real graphics as opposed to simple character graphics. The downside of the system was that it required much more advanced decoders, typically featuring Zilog Z80 or Motorola 6809 processors with RGB and/or RF output. The Innovation, Science and Economic Development Canada (then Department of Communications) launched a four-year plan to fund public roll-outs of the technology in an effort to spur the development of a commercial Telidon system. AT&T Corporation was so impressed by Telidon that they decided to join the project. They added a number of useful extensions, notably the ability to define original graphics commands (macro) and character sets (DRCS). They also tabled algorithms for proportionally spaced text, which greatly improved the quality of the displayed pages. A joint CSA/ANSI working group (X3L2.1) revised the specifications, which were submitted for standardization. In 1983, they became CSA T500 and ANSI X3.110, or NAPLPS. The data encoding system was also standardized as the NABTS (North American Broadcast Teletext Specification) protocol. Business models for Telidon services were poorly developed. Unlike the UK, where teletext was supported by one of only two large companies whose whole revenue model was based on a read-only medium (television), in North America Telidon was being offered by companies who worked on a subscriber basis. == One-way systems == Telidon-based teletext was tested in a few North American trials in the early 1980s — CBC IRIS, TVOntario, MTS-sponsored Project IDA, to name a few. NAPLPS was also part of the NABTS teletext standard, for the encoding and display of teletext pages. In the late 1980s and early 1990s, affiliates of the regional sports network group SportsChannel ran a service called Sports Plus Network, which ran sports news and scores while SportsChannel was not otherwise on the air. The screens, which frequently featured team logos or likenesses of players in addition to text, were drawn entirely with NAPLPS graphics and resembled the loading of Prodigy pages over a modem, though slightly faster. == Two-way systems == Various two-way systems using NAPLPS appeared in North America in the early 1980s. The biggest North American examples were Knight Ridder's Viewtron (based in Miami) and the Los Angeles Times' Gateway service (based in Orange County). Both used the Sceptre NAPLPS terminal from AT&T. The Sceptre contained a slow modem that connected over the consumer's telephone line to host computers. The Sceptre was expensive whether purchased or rented. Despite huge investments by their parent companies, neither Viewtron nor Gateway lasted into the second half of the decade. Another system, Keyfax, was developed by Keycom Electronic Publishing, a joint venture of Honeywell, Centel (since acquired by Sprint) and Field Enterprises, then-owner of the Chicago Sun-Times newspaper. Keyfax had originally been a WST teletext service, broadcast overnights on Field's Chicago television station WFLD-32 and through the VBI of both WFLD and national superstation WTBS; the decision was made to convert Keyfax into a subscription service, using a proprietary NAPLPS terminal device in a last-ditch effort to save the service. It did not work and Keyfax had ceased operations by the end of 1986. Other early-1980s NAPLPS technology was deployed in Canada, both as a way for rural Canadians to get news and weather information and as the platform for touchscreen information kiosks. In Vancouver these were featured at Expo 86. The kiosks became ubiquitous in Toronto under the name Teleguide, and were deployed in many shopping centres and at major tourist attractions. The latter city was the North American nexus of NAPLPS and the home of Norpak, the most successful of NAPLPS-oriented developers. Norpak created and sold hardware and software for NAPLPS development and display. TVOntario also developed NAPLPS content creation software. London, Ontario - based Cableshare used NAPLPS as the basis of touch-screen information kiosks for shopping malls, the flagship of which was deployed at Toronto's Eaton Centre. The system relied on an 8085-based microcomputer which drove several NAPLPS terminals fitted with touch screens, all communicating via Datapac to a back end database. The system offered news, weather and sports information along with shopping mall guides and coupons. Cableshare also developed and sold a leading NAPLPS page creation utility called the "Picture Painter." In the late 1980s, Tribune Media Services (TMS) and the Associated Press operated a cable television channel called AP News Plus that provided NAPLPS-based news screens to cable television subscribers in many U.S. cities. The news pages were created and edited by TMS staffers working on an Atex editing system in Orlando, Florida, and sent by satellite to NAPLPS decoder devices located at the local cable television companies. Among the firms providing technology to TMS and the Associated Press for the AP News Plus channel was Minneapolis-based Electronic Publishers Inc. (1985–1988). In 1981, two amateur radio operators (VE3FTT and VE3GQW) received special permission from the Canad
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Astrostatistics

Astrostatistics is a discipline which spans astrophysics, statistical analysis and data mining. It is used to process the vast amount of data produced by automated scanning of the cosmos, to characterize complex datasets, and to link astronomical data to astrophysical theory. Many branches of statistics are involved in astronomical analysis including nonparametrics, multivariate regression and multivariate classification, time series analysis, and especially Bayesian inference. The field is closely related to astroinformatics.
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Scrolling

In computer displays, filmmaking, television production, video games and other kinetic displays, scrolling is sliding text, images or video across a monitor or display, vertically or horizontally. "Scrolling," as such, does not change the layout of the text or pictures but moves (pans or tilts) the user's view across what is apparently a larger image that is not wholly seen. A common television and movie special effect is to scroll credits, while leaving the background stationary. Scrolling may take place completely without user intervention (as in film credits) or, on an interactive device, be triggered by touchscreen or a keypress and continue without further intervention until a further user action, or be entirely controlled by input devices. Scrolling may take place in discrete increments (perhaps one or a few lines of text at a time), or continuously (smooth scrolling). Frame rate is the speed at which an entire image is redisplayed. It is related to scrolling in that changes to text and image position can only happen as often as the image can be redisplayed. When frame rate is a limiting factor, one smooth scrolling technique is to blur images during movement that would otherwise appear to "jump". == Computing == === Implementation === Scrolling is often carried out on a computer by the CPU (software scrolling) or by a graphics processor. Some systems feature hardware scrolling, where an image may be offset as it is displayed, without any frame buffer manipulation (see also hardware windowing). This was especially common in 8 and 16bit video game consoles. === UI paradigms === In a WIMP-style graphical user interface (GUI), user-controlled scrolling is carried out by manipulating a scrollbar with a mouse, or using keyboard shortcuts, often the arrow keys. Scrolling is often supported by text user interfaces and command line interfaces. Older computer terminals changed the entire contents of the display one screenful ("page") at a time; this paging mode requires fewer resources than scrolling. Scrolling displays often also support page mode. Typically certain keys or key combinations page up or down; on PC-compatible keyboards the page up and page down keys or the space bar are used; earlier computers often used control key combinations. Some computer mice have a scroll wheel, which scrolls the display, often vertically, when rolled; others have scroll balls or tilt wheels which allow both vertical and horizontal scrolling. Some software supports other ways of scrolling. Adobe Reader has a mode identified by a small hand icon ("hand tool") on the document, which can then be dragged by clicking on it and moving the mouse as if sliding a large sheet of paper. When this feature is implemented on a touchscreen it is called kinetic scrolling. Touch-screens often use inertial scrolling, in which the scrolling motion of an object continues in a decaying fashion after release of the touch, simulating the appearance of an object with inertia. An early implementation of such behavior was in the "Star7" PDA of Sun Microsystems ca. 1991–1992. Scrolling can be controlled in other software-dependent ways by a PC mouse. Some scroll wheels can be pressed down, functioning like a button. Depending on the software, this allows both horizontal and vertical scrolling by dragging in the direction desired; when the mouse is moved to the original position, scrolling stops. A few scroll wheels can also be tilted, scrolling horizontally in one direction until released. On touchscreen devices, scrolling is a multi-touch gesture, done by swiping a finger on the screen vertically in the direction opposite to where the user wants to scroll to. If any content is too wide to fit on a display, horizontal scrolling is required to view all of it. In applications such as graphics and spreadsheets there is often more content than can fit either the width or the height of the screen at a comfortable scale, and scrolling in both directions is necessary. === Infinite scrolling === In contrast to material divided into discrete pages, the web design approach of infinite scrolling dynamically adds new material to the user display, leading to a continuous, apparently bottomless or endless scrolling experience. === Text === In languages written horizontally, such as most Western languages, text documents longer than will fit on the screen are often displayed wrapped and sized to fit the screen width, and scrolled vertically to bring desired content into view. It is possible to display lines too long to fit the display without wrapping, scrolling horizontally to view each entire line. However, this requires inconvenient constant line-by-line scrolling, while vertical scrolling is only needed after reading a full screenful. Software such as word processors and web browsers normally uses word-wrapping to display as many words in a single line as will fit the width of the screen or window or, for text organised in columns, each column. === Demos === Scrolling texts, also referred to as scrolltexts or scrollers, played an important part in the birth of the computer demo culture. The software crackers often used their deep knowledge of computer platforms to transform the information that accompanied their releases into crack intros. The sole role of these intros was to scroll the text on the screen in an impressive way. == Film and television == Scrolling is commonly used to display the credits at the end of films and television programs. Scrolling is often used in the form of a news ticker towards the bottom of the picture for content such as television news, scrolling sideways across the screen, delivering short-form content. In the dynamic layout of kinetic typography, scrolling typography can scroll across the flat screen, or can appear to recede or advance. An iconic example is the Star Wars opening crawl inspired by the Flash Gordon serials. == Video games == In computer and video games, scrolling of a playing field allows the player to control an object in a large contiguous area. Early examples of this method include Taito's 1974 vertical-scrolling racing video game Speed Race, Sega's 1976 forward-scrolling racing games Moto-Cross (Fonz) and Road Race, and Super Bug. Previously the flip-screen method was used to indicate moving backgrounds. The Namco Galaxian arcade system board introduced with Galaxian in 1979 pioneered a sprite system that animated pre-loaded sprites over a scrolling background, which became the basis for Nintendo's Radar Scope and Donkey Kong arcade hardware and home consoles such as the Nintendo Entertainment System. Parallax scrolling, which was first featured in Moon Patrol, involves several semi-transparent layers (called playfields), which scroll on top of each other at varying rates in order to give an early pseudo-3D illusion of depth. Belt scrolling is a method used in side-scrolling beat 'em up games with a downward camera angle where players can move up and down in addition to left and right. == Studies == A 1993 article by George Fitzmaurice studied spatially aware palmtop computers. These devices had a 3D sensor, and moving the device caused the contents to move as if the contents were fixed in place. This interaction could be referred to as “moving to scroll.” Also, if the user moved the device away from their body, they would zoom in; conversely, the device would zoom out if the user pulled the device closer to them. Smartphone cameras and “optical flow” image analysis utilize this technique nowadays. A 1996 research paper by Jun Rekimoto analyzed tilting operations as scrolling techniques on small screen interfaces. Users could not only tilt to scroll, but also tilt to select menu items. These techniques proved especially useful for field workers, since they only needed to hold and control the device with one hand. A study from 2013 by Selina Sharmin, Oleg Špakov, and Kari-Jouko Räihä explored the action of reading text on a screen while the text auto-scrolls based on the user's eye tracking patterns. The control group simply read text on a screen and manually scrolled. The study found that participants preferred to read primarily at the top of the screen, so the screen scrolled down whenever participants’ eyes began to look toward the bottom of the screen. This auto-scrolling caused no statistically significant difference in reading speed or performance. An undated study occurring during or after 2010 by Dede Frederick, James Mohler, Mihaela Vorvoreanu, and Ronald Glotzbach noted that parallax scrolling "may cause certain people to experience nausea."
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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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Log shipping

Log shipping is the process of automating the backup of transaction log files on a primary (production) database server, and then restoring them onto a standby server. This technique is supported by Microsoft SQL Server, 4D Server, MySQL, and PostgreSQL. Similar to replication, the primary purpose of log shipping is to increase database availability by maintaining a backup server that can replace a production server quickly. Other databases such as Adaptive Server Enterprise and Oracle Database support the technique but require the Database Administrator to write code or scripts to perform the work. Although the actual failover mechanism in log shipping is manual, this implementation is often chosen due to its low cost in human and server resources, and ease of implementation. In comparison, SQL server clusters enable automatic failover, but at the expense of much higher storage costs. Compared to database replication, log shipping does not provide as much in terms of reporting capabilities, but backs up system tables along with data tables, and locks the standby server from users' modifications. A replicated server can be modified (e.g. views) and is therefore unsuitable for failover purposes.
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Vujak

VuJak is an early video sampler, a VJ remix and mashup tool created in 1992 by Brian Kane, Lisa Eisenpresser, and Jay Haynes. The original name of the project was Mideo, but it was later changed to VuJak. VuJak was based on MIDI control of video in real-time. It was created with MAX from Opcode Systems, and utilized the newly released QuickTime 1.0 movie object. The first working version of the program was built on a Mac IIfx with 8 megs of ram, and could jump in real-time across a 160 x 120 pixel QuickTime movie via a midi keyboard. Later versions could manipulate full screen video, included the first real-time video scratch feature, had looping, vari-speed, and random play features, and allowed for recording and editing of video sequences within the application. VuJak also had networking capabilities which allowed artists to "jam" in real time across standard phone lines. The first public exhibition of VuJak was at the Digital Hollywood conference in Beverly Hills in 1993, where it was promoted by Timothy Leary. VuJak was featured in Mondo 2000, CBS Evening News, Wired Magazine, Electronic Musician, Billboard Magazine, The Hollywood Reporter, and it was used to create promotional videos for MTV. In 1994, VuJak was a featured interactive exhibition at the Exploratorium in San Francisco. Development of VuJak ceased in 1995.
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Trigger list

Trigger list in its most general meaning refers to a list whose items are used to initiate ("trigger") certain actions. == United States: Private financial information == In the United States, when a person applies for a mortgage loan, the lender makes a credit inquiry about the potential borrower from the national credit bureaus, Equifax, Experian and TransUnion. Unless the borrower is opted out, the credit bureaus put the applicants onto a "trigger list" of "leads" about persons who are interested in new loans. These lists are sold to numerous lenders all over the United States, and soon after the application the applicant starts receiving offers from all parts of the country. The trigger lists contain a significant amount of personal financial information. Among the buyers of trigger lists are "lead generators" which resell filtered information to borrowers, e.g., of people who live in a certain area and have a certain credit score. While the Federal Trade Commission considers the market of "trigger lists" to be a legal business, many people and organizations (such as the National Association of Mortgage Brokers) consider this a serious breach of privacy and lobby for putting this practice under regulatory controls. As of now, American consumers may opt-out from "trigger lists" by calling 1-888-5-OPTOUT (1-888-567-8688). == Nuclear non-proliferation == The Zangger Committee and the Nuclear Suppliers Group maintain lists of items that may contribute to nuclear proliferation; The nuclear non-proliferation treaty forbids its members to export such items to non-treaty members. these items are said to trigger the countries' responsibilities under the NPT, hence the name.
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Geometric primitive

In vector computer graphics, CAD systems, and geographic information systems, a geometric primitive (or prim) is the simplest (i.e. 'atomic' or irreducible) geometric shape that the system can handle (draw, store). Sometimes the subroutines that draw the corresponding objects are called "geometric primitives" as well. The most "primitive" primitives are point and straight line segments, which were all that early vector graphics systems had. In constructive solid geometry, primitives are simple geometric shapes such as a cube, cylinder, sphere, cone, pyramid, torus. Modern 2D computer graphics systems may operate with primitives which are curves (segments of straight lines, circles and more complicated curves), as well as shapes (boxes, arbitrary polygons, circles). A common set of two-dimensional primitives includes lines, points, and polygons, although some people prefer to consider triangles primitives, because every polygon can be constructed from triangles (polygon triangulation). All other graphic elements are built up from these primitives. In three dimensions, triangles or polygons positioned in three-dimensional space can be used as primitives to model more complex 3D forms. In some cases, curves (such as Bézier curves, circles, etc.) may be considered primitives; in other cases, curves are complex forms created from many straight, primitive shapes. == Common primitives == The set of geometric primitives is based on the dimension of the region being represented: Point (0-dimensional), a single location with no height, width, or depth. Line or curve (1-dimensional), having length but no width, although a linear feature may curve through a higher-dimensional space. Planar surface or curved surface (2-dimensional), having length and width. Volumetric region or solid (3-dimensional), having length, width, and depth. In GIS, the terrain surface is often spoken of colloquially as "2 1/2 dimensional," because only the upper surface needs to be represented. Thus, elevation can be conceptualized as a scalar field property or function of two-dimensional space, affording it a number of data modeling efficiencies over true 3-dimensional objects. A shape of any of these dimensions greater than zero consists of an infinite number of distinct points. Because digital systems are finite, only a sample set of the points in a shape can be stored. Thus, vector data structures typically represent geometric primitives using a strategic sample, organized in structures that facilitate the software interpolating the remainder of the shape at the time of analysis or display, using the algorithms of Computational geometry. A Point is a single coordinate in a Cartesian coordinate system. Some data models allow for Multipoint features consisting of several disconnected points. A Polygonal chain or Polyline is an ordered list of points (termed vertices in this context). The software is expected to interpolate the intervening shape of the line between adjacent points in the list as a parametric curve, most commonly a straight line, but other types of curves are frequently available, including circular arcs, cubic splines, and Bézier curves. Some of these curves require additional points to be defined that are not on the line itself, but are used for parametric control. A Polygon is a polyline that closes at its endpoints, representing the boundary of a two-dimensional region. The software is expected to use this boundary to partition 2-dimensional space into an interior and exterior. Some data models allow for a single feature to consist of multiple polylines, which could collectively connect to form a single closed boundary, could represent a set of disjoint regions (e.g., the state of Hawaii), or could represent a region with holes (e.g., a lake with an island). A Parametric shape is a standardized two-dimensional or three-dimensional shape defined by a minimal set of parameters, such as an ellipse defined by two points at its foci, or three points at its center, vertex, and co-vertex. A Polyhedron or Polygon mesh is a set of polygon faces in three-dimensional space that are connected at their edges to completely enclose a volumetric region. In some applications, closure may not be required or may be implied, such as modeling terrain. The software is expected to use this surface to partition 3-dimensional space into an interior and exterior. A triangle mesh is a subtype of polyhedron in which all faces must be triangles, the only polygon that will always be planar, including the Triangulated irregular network (TIN) commonly used in GIS. A parametric mesh represents a three-dimensional surface by a connected set of parametric functions, similar to a spline or Bézier curve in two dimensions. The most common structure is the Non-uniform rational B-spline (NURBS), supported by most CAD and animation software. == Application in GIS == A wide variety of vector data structures and formats have been developed during the history of Geographic information systems, but they share a fundamental basis of storing a core set of geometric primitives to represent the location and extent of geographic phenomena. Locations of points are almost always measured within a standard Earth-based coordinate system, whether the spherical Geographic coordinate system (latitude/longitude), or a planar coordinate system, such as the Universal Transverse Mercator. They also share the need to store a set of attributes of each geographic feature alongside its shape; traditionally, this has been accomplished using the data models, data formats, and even software of relational databases. Early vector formats, such as POLYVRT, the ARC/INFO Coverage, and the Esri shapefile support a basic set of geometric primitives: points, polylines, and polygons, only in two dimensional space and the latter two with only straight line interpolation. TIN data structures for representing terrain surfaces as triangle meshes were also added. Since the mid 1990s, new formats have been developed that extend the range of available primitives, generally standardized by the Open Geospatial Consortium's Simple Features specification. Common geometric primitive extensions include: three-dimensional coordinates for points, lines, and polygons; a fourth "dimension" to represent a measured attribute or time; curved segments in lines and polygons; text annotation as a form of geometry; and polygon meshes for three-dimensional objects. Frequently, a representation of the shape of a real-world phenomenon may have a different (usually lower) dimension than the phenomenon being represented. For example, a city (a two-dimensional region) may be represented as a point, or a road (a three-dimensional volume of material) may be represented as a line. This dimensional generalization correlates with tendencies in spatial cognition. For example, asking the distance between two cities presumes a conceptual model of the cities as points, while giving directions involving travel "up," "down," or "along" a road imply a one-dimensional conceptual model. This is frequently done for purposes of data efficiency, visual simplicity, or cognitive efficiency, and is acceptable if the distinction between the representation and the represented is understood, but can cause confusion if information users assume that the digital shape is a perfect representation of reality (i.e., believing that roads really are lines). == In 3D modelling == In CAD software or 3D modelling, the interface may present the user with the ability to create primitives which may be further modified by edits. For example, in the practice of box modelling the user will start with a cuboid, then use extrusion and other operations to create the model. In this use the primitive is just a convenient starting point, rather than the fundamental unit of modelling. A 3D package may also include a list of extended primitives which are more complex shapes that come with the package. For example, a teapot is listed as a primitive in 3D Studio Max. == In graphics hardware == Various graphics accelerators exist with hardware acceleration for rendering specific primitives such as lines or triangles, frequently with texture mapping and shaders. Modern 3D accelerators typically accept sequences of triangles as triangle strips.
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Swizzling (computer graphics)

In computer graphics, swizzles are a class of operations that transform vectors by rearranging components. Swizzles can also project from a vector of one dimensionality to a vector of another dimensionality, such as taking a three-dimensional vector and creating a two-dimensional or five-dimensional vector using components from the original vector. For example, if A = {1,2,3,4}, where the components are x, y, z, and w respectively, one could compute B = A.wwxy, whereupon B would equal {4,4,1,2}. Additionally, one could create a two-dimensional vector with A.wx or a five-dimensional vector with A.xyzwx. Combining vectors and swizzling can be employed in various ways. This is common in GPGPU applications. In terms of linear algebra, this is equivalent to multiplying by a matrix whose rows are standard basis vectors. If A = ( 1 , 2 , 3 , 4 ) T {\displaystyle A=(1,2,3,4)^{T}} , then swizzling A {\displaystyle A} as above looks like A . w w x y = [ 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 ] [ 1 2 3 4 ] = [ 4 4 1 2 ] . {\displaystyle A.\!wwxy={\begin{bmatrix}0&0&0&1\\0&0&0&1\\1&0&0&0\\0&1&0&0\end{bmatrix}}{\begin{bmatrix}1\\2\\3\\4\end{bmatrix}}={\begin{bmatrix}4\\4\\1\\2\end{bmatrix}}.}
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Autoscaling

Autoscaling, (also written as auto scaling, auto-scaling, or known as automatic scaling), is a method used in cloud computing that dynamically adjusts the amount of computational resources in a server farm - typically measured by the number of active servers - automatically based on the load on the farm. For example, the number of servers running behind a web application may be increased or decreased automatically based on the number of active users on the site. Since such metrics may change dramatically throughout the course of the day, and servers are a limited resource that cost money to run even while idle, there is often an incentive to run "just enough" servers to support the current load while still being able to support sudden and large spikes in activity. Autoscaling is helpful for such needs, as it can reduce the number of active servers when activity is low, and launch new servers when activity is high. Autoscaling is closely related to, and builds upon, the idea of load balancing. == Advantages == Autoscaling offers the following advantages: For companies running their own web server infrastructure, autoscaling typically means allowing some servers to go to sleep during times of low load, saving on electricity costs (as well as water costs if water is being used to cool the machines). For companies using infrastructure hosted in the cloud, autoscaling can mean lower bills, because most cloud providers charge based on total usage rather than maximum capacity. Even for companies that cannot reduce the total compute capacity they run or pay for at any given time, autoscaling can help by allowing the company to run less time-sensitive workloads on machines that get freed up by autoscaling during times of low traffic. Autoscaling solutions, such as the one offered by Amazon Web Services, can also take care of replacing unhealthy instances and therefore protecting somewhat against hardware, network, and application failures. Autoscaling can offer greater uptime and more availability in cases where production workloads are variable and unpredictable. Autoscaling differs from having a fixed daily, weekly, or yearly cycle of server use in that it is responsive to actual usage patterns, and thus reduces the potential downside of having too few or too many servers for the traffic load. For instance, if traffic is usually lower at midnight, then a static scaling solution might schedule some servers to sleep at night, but this might result in downtime on a night where people happen to use the Internet more (for instance, due to a viral news event). Autoscaling, on the other hand, can handle unexpected traffic spikes better. == Terminology == In the list below, we use the terminology used by Amazon Web Services (AWS). However, alternative names are noted and terminology that is specific to the names of Amazon services is not used for the names. == Practice == === Amazon Web Services (AWS) === Amazon Web Services launched the Amazon Elastic Compute Cloud (EC2) service in August 2006, that allowed developers to programmatically create and terminate instances (machines). At the time of initial launch, AWS did not offer autoscaling, but the ability to programmatically create and terminate instances gave developers the flexibility to write their own code for autoscaling. Third-party autoscaling software for AWS began appearing around April 2008. These included tools by Scalr and RightScale. RightScale was used by Animoto, which was able to handle Facebook traffic by adopting autoscaling. On May 18, 2009, Amazon launched its own autoscaling feature along with Elastic Load Balancing, as part of Amazon Elastic Compute Cloud. Autoscaling is now an integral component of Amazon's EC2 offering. Autoscaling on Amazon Web Services is done through a web browser or the command line tool. In May 2016 Autoscaling was also offered in AWS ECS Service. On-demand video provider Netflix documented their use of autoscaling with Amazon Web Services to meet their highly variable consumer needs. They found that aggressive scaling up and delayed and cautious scaling down served their goals of uptime and responsiveness best. In an article for TechCrunch, Zev Laderman, the co-founder and CEO of Newvem, a service that helps optimize AWS cloud infrastructure, recommended that startups use autoscaling in order to keep their Amazon Web Services costs low. Various best practice guides for AWS use suggest using its autoscaling feature even in cases where the load is not variable. That is because autoscaling offers two other advantages: automatic replacement of any instances that become unhealthy for any reason (such as hardware failure, network failure, or application error), and automatic replacement of spot instances that get interrupted for price or capacity reasons, making it more feasible to use spot instances for production purposes. Netflix's internal best practices require every instance to be in an autoscaling group, and its conformity monkey terminates any instance not in an autoscaling group in order to enforce this best practice. === Microsoft's Windows Azure === On June 27, 2013, Microsoft announced that it was adding autoscaling support to its Windows Azure cloud computing platform. Documentation for the feature is available on the Microsoft Developer Network. === Oracle Cloud === Oracle Cloud Platform allows server instances to automatically scale a cluster in or out by defining an auto-scaling rule. These rules are based on CPU and/or memory utilization and determine when to add or remove nodes. === Google Cloud Platform === On November 17, 2014, the Google Compute Engine announced a public beta of its autoscaling feature for use in Google Cloud Platform applications. As of March 2015, the autoscaling tool is still in Beta. === Facebook === In a blog post in August 2014, a Facebook engineer disclosed that the company had started using autoscaling to bring down its energy costs. The blog post reported a 27% decline in energy use for low traffic hours (around midnight) and a 10-15% decline in energy use over the typical 24-hour cycle. === Kubernetes Horizontal Pod Autoscaler === Kubernetes Horizontal Pod Autoscaler automatically scales the number of pods in a replication controller, deployment or replicaset based on observed CPU utilization (or, with beta support, on some other, application-provided metrics) == Alternative autoscaling decision approaches == Autoscaling by default uses reactive decision approach for dealing with traffic scaling: scaling only happens in response to real-time changes in metrics. In some cases, particularly when the changes occur very quickly, this reactive approach to scaling is insufficient. Two other kinds of autoscaling decision approaches are described below. === Scheduled autoscaling approach === This is an approach to autoscaling where changes are made to the minimum size, maximum size, or desired capacity of the autoscaling group at specific times of day. Scheduled scaling is useful, for instance, if there is a known traffic load increase or decrease at specific times of the day, but the change is too sudden for reactive approach based autoscaling to respond fast enough. AWS autoscaling groups support scheduled scaling. === Predictive autoscaling === This approach to autoscaling uses predictive analytics. The idea is to combine recent usage trends with historical usage data as well as other kinds of data to predict usage in the future, and autoscale based on these predictions. For parts of their infrastructure and specific workloads, Netflix found that Scryer, their predictive analytics engine, gave better results than Amazon's reactive autoscaling approach. In particular, it was better for: Identifying huge spikes in demand in the near future and getting capacity ready a little in advance Dealing with large-scale outages, such as failure of entire availability zones and regions Dealing with variable traffic patterns, providing more flexibility on the rate of scaling out or in based on the typical level and rate of change in demand at various times of day On November 20, 2018, AWS announced that predictive scaling would be available as part of its autoscaling offering.
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Network eavesdropping

Network eavesdropping, also known as eavesdropping attack, sniffing attack, or snooping attack, is a method that retrieves user information through the internet. This attack happens on electronic devices like computers and smartphones. This network attack typically happens under the usage of unsecured networks, such as public wifi connections or shared electronic devices. Eavesdropping attacks through the network is considered one of the most urgent threats in industries that rely on collecting and storing data. Internet users use eavesdropping via the Internet to improve information security. A typical network eavesdropper may be called a Black-hat hacker and is considered a low-level hacker as it is simple to network eavesdrop successfully. The threat of network eavesdroppers is a growing concern. Research and discussions are brought up in the public's eye, for instance, types of eavesdropping, open-source tools, and commercial tools to prevent eavesdropping. Models against network eavesdropping attempts are built and developed as privacy is increasingly valued. Sections on cases of successful network eavesdropping attempts and its laws and policies in the National Security Agency are mentioned. Some laws include the Electronic Communications Privacy Act and the Foreign Intelligence Surveillance Act. == Types of attacks == Types of network eavesdropping include intervening in the process of decryption of messages on communication systems, attempting to access documents stored in a network system, and listening on electronic devices. Types include electronic performance monitoring and control systems, keystroke logging, man-in-the-middle attacks, observing exit nodes on a network, and Skype & Type. === Electronic performance monitoring and control systems (EPMCSs) === Electronic performance monitoring and control systems are used by employees or companies and organizations to collect, store, analyze, and report actions or performances of employers when they are working. The beginning of this system is used to increase the efficiency of workers, but instances of unintentional eavesdropping can occur, for example, when employees' casual phone calls or conversations would be recorded. === Keystroke logging === Keystroke logging is a program that can oversee the writing process of the user. It can be used to analyze the user's typing activities, as keystroke logging provides detailed information on activities like typing speed, pausing, deletion of texts, and more behaviors. By monitoring the activities and sounds of the keyboard strikes, the message typed by the user can be translated. Although keystroke logging systems do not explain reasons for pauses or deletion of texts, it allows attackers to analyze text information. Keystroke logging can also be used with eye-tracking devices which monitor the movements of the user's eyes to determine patterns of the user's typing actions which can be used to explain the reasons for pauses or deletion of texts. === Man-in-the-middle attack (MitM) === A Man-in-the-middle attack is an active eavesdropping method that intrudes on the network system. It can retrieve and alter the information sent between two parties without anyone noticing. The attacker hijacks the communication systems and gains control over the transport of data, but cannot insert voice messages that sound or act like the actual users. Attackers also create independent communications through the system with the users acting as if the conversation between users is private. The "man-in-the-middle" can also be referred to as lurkers in a social context. A lurker is a person who rarely or never posts anything online, but the person stays online and observes other users' actions. Lurking can be valuable as it lets people gain knowledge from other users. However, like eavesdropping, lurking into other users' private information violates privacy and social norms. === Observing exit nodes === Distributed networks including communication networks are usually designed so that nodes can enter and exit the network freely. However, this poses a danger in which attacks can easily access the system and may cause serious consequences, for example, leakage of the user's phone number or credit card number. In many anonymous network pathways, the last node before exiting the network may contain actual information sent by users. Tor exit nodes are an example. Tor is an anonymous communication system that allows users to hide their IP addresses. It also has layers of encryption that protect information sent between users from eavesdropping attempts trying to observe the network traffic. However, Tor exit nodes are used to eavesdrop at the end of the network traffic. The last node in the network path flowing through the traffic, for instance, Tor exit nodes, can acquire original information or messages that were transmitted between different users. === Skype & Type (S&T) === Skype & Type (S&T) is a new keyboard acoustic eavesdropping attack that takes advantage of Voice-over IP (VoIP). S&T is practical and can be used in many applications in the real world, as it does not require attackers to be close to the victim and it can work with only some leaked keystrokes instead of every keystroke. With some knowledge of the victim's typing patterns, attackers can gain a 91.7% accuracy typed by the victim. Different recording devices including laptop microphones, smartphones, and headset microphones can be used for attackers to eavesdrop on the victim's style and speed of typing. It is especially dangerous when attackers know what language the victim is typing in. == Tools to prevent eavesdropping attacks == Computer programs where the source code of the system is shared with the public for free or for commercial use can be used to prevent network eavesdropping. They are often modified to cater to different network systems, and the tools are specific in what task it performs. In this case, Advanced Encryption Standard-256, Bro, Chaosreader, CommView, Firewalls, Security Agencies, Snort, Tcptrace, and Wireshark are tools that address network security and network eavesdropping. === Advanced encryption standard-256 (AES-256) === It is a cipher block chaining (CBC) mode for ciphered messages and hash-based message codes. The AES-256 contains 256 keys for identifying the actual user, and it represents the standard used for securing many layers on the internet. AES-256 is used by Zoom Phone apps that help encrypt chat messages sent by Zoom users. If this feature is used in the app, users will only see encrypted chats when they use the app, and notifications of an encrypted chat will be sent with no content involved. === Bro === Bro is a system that detects network attackers and abnormal traffic on the internet. It emerged at the University of California, Berkeley that detects invading network systems. The system does not apply to the detection of eavesdropping by default, but can be modified to an offline analyzing tool for eavesdropping attacks. Bro runs under Digital Unix, FreeBSD, IRIX, SunOS, and Solaris operating systems, with the implementation of approximately 22,000 lines of C++ and 1,900 lines of Bro. It is still in the process of development for real-world applications. === Chaosreader === Chaosreader is a simplified version of many open-source eavesdropping tools. It creates HTML pages on the content of when a network intrusion is detected. No actions are taken when an attack occurs and only information such as time, network location on which system or wall the user is trying to attack will be recorded. === CommView === CommView is specific to Windows systems which limits real-world applications because of its specific system usage. It captures network traffic and eavesdropping attempts by using packet analyzing and decoding. === Firewalls === Firewall technology filters network traffic and blocks malicious users from attacking the network system. It prevents users from intruding into private networks. Having a firewall in the entrance to a network system requires user authentications before allowing actions performed by users. There are different types of firewall technologies that can be applied to different types of networks. === Security agencies === A Secure Node Identification Agent is a mobile agent used to distinguish secure neighbor nodes and informs the Node Monitoring System (NMOA). The NMOA stays within nodes and monitors the energy exerted, and receives information about nodes including node ID, location, signal strength, hop counts, and more. It detects nodes nearby that are moving out of range by comparing signal strengths. The NMOA signals the Secure Node Identification Agent (SNIA) and updates each other on neighboring node information. The Node BlackBoard is a knowledge base that reads and updates the agents, acting as the brain of the security system. The Node Key Management agent is created when an encryption key is inserted to th
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2018 Google data breach

The 2018 Google data breach was a major data privacy scandal in which the Google+ API exposed the private data of over five hundred thousand users. Google+ managers first noticed harvesting of personal data in March 2018, during a review following the Facebook–Cambridge Analytica data scandal. The bug, despite having been fixed immediately, exposed the private data of approximately 500,000 Google+ users to the public. Google did not reveal the leak to the network's users. In November 2018, another data breach occurred following an update to the Google+ API. Although Google found no evidence of failure, approximately 52.5 million personal profiles were potentially exposed. In August 2019, Google declared a shutdown of Google+ due to low use and technological challenges. == Overview of Google+ == Google+ was launched in June 2011 as an invite-only social network, but was opened for public access later in the year. It was managed by Vic Gundotra. Similar to Facebook, Google+ also included key features Circles, Hangouts and Sparks. Circles let users personalize their social groups by sorting friends into different categories. Once allowed into a Circle, users could regulate information in their individual spaces. Hangouts included video chatting and instant messaging between users. Sparks allowed Google to track users' past searches to find news and content related to their interests. Google+ was linked to other Google services, such as YouTube, Google Drive and Gmail, giving it access to roughly 2 billion user accounts. However, less than 400 million consumers actively used Google+, with 90% of those users using it for less than five seconds. == The breaches == In March 2018, Google developers found a data breach within the Google+ People API in which external apps acquired access to Profile fields that were not marked as public. According to The Wall Street Journal, Google didn’t disclose the breach when it was first discovered in March to avoid regulatory scrutiny and reputational damage. 500,000 Google+ accounts were included in the breach, which allowed 438 external apps unauthorized access to private users' names, emails, addresses, occupations, genders and ages. This information was available between 2015 and 2018. Google found no evidence of any user's personal information being misused, nor that any third-party app developers were aware of the leak. In November 2018, a software update created another data breach within the Google+ API. The bug impacted 52.5 million users, where, similarly to the March breach, unauthorized apps were able to access Google+ profiles, including users' names, email addresses, occupations and ages. Apps could not access financial information, national identification, numbers, or passwords. Blog posts, messages and phone numbers also remained inaccessible if marked as private. Unlike the previous breach, access was only available for six days before Google+ learned of the breach. Once more, Google+ found no evidence of data being misused by third-party developers. == Responses == In October 2018, the Wall Street Journal published an article outlining the initial breach and Google's decision to not disclose it to users. At the time, there was no federal law that required Google to inform their consumers of data breaches. Google+ originally did not disclose the breach out of fears of being compared to Facebook's recent data leak and subsequent loss of consumer confidence. In response to the Wall Street Journal article, Google announced the shutdown of Google+ in August 2019. After the second data leak, the date was moved to April 2019. In response to the data breach, enterprise consumers were notified of the bug's impact and given instructions on how to save, download and delete their data prior to the Google+ shut down. Google's Privacy and Data Protection Office found no misuse of user data. Prior to the Google+ shutdown, Google set a 10-month period in which users could download and migrate their data. After the 10-month period, user content was deleted. On 4 February 2019, consumers were no longer able to create new Google+ profiles. Google shut down Google+ APIs on 7 March 2019 to ensure that developers did not continue to rely on the APIs prior to the Google+ shutdown. Google is the principal entity of its parent company, Alphabet Inc. After the data breach, Alphabet Inc. share prices fell by 1% to $1,157.06 on 9 October 2018 after an earlier drop of $1,135.40 that morning, the lowest price since 5 July 2018. After the publication of The Wall Street Journal article, share prices dropped as low as 2.1% in two days on 10 October 2018. Share prices steadily increased from this point and met the 8 October 2018 share price on 5 February 2019. Google planned to rebuild Google+ as a corporate enterprise network. Google Play will now assess which apps can ask for permission to access the user's SMS data. Only the default app for telephone distribution is able to make requests. Prior to the data breaches, apps were able to request access to all of a consumer's data simultaneously. Now, each app must request permission for each aspect of a consumer's profile.
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