AI For Kids Dubai

AI For Kids Dubai — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

YNAB

You Need a Budget (YNAB) (pronounced ) is an online personal budgeting program based on the envelope system developed by a privately owned American company of the same name. It is available via any web browser or a mobile app. == History == The program was initially developed as standalone software in 2004 by Jesse Mecham, while he was in college pursuing his master's degree in accounting, after he and his wife faced financial difficulty and decided to improve their budgeting. It evolved from a spreadsheet that he created for the budgeting process. The acronym stands for "you need a budget." In 2015 they changed their licensing model to software as a service. In 2020, YNAB had 115 employees, all working remotely. == Overview == The service encourages users to follow four principles or "rules": Give every dollar a job: Each dollar in a budget is allocated to a specific purpose. This concept is also called zero-based budgeting. Embrace true expenses: All expenses are planned for, so that there are no surprises. Roll with the punches: Being flexible when there is overspending. Age your money: Keeping money in your budget without immediately spending it. Users can either import transactions automatically from their financial institutions or input them manually. The software also displays financial reports to keep users informed about their finances at a glance. == Awards and recognition == YNAB has been named one of the best budgeting apps by U.S. News & World Report, Kiplinger's Personal Finance, CNN, HuffPost, CNBC, and hundreds of other financial reporting outlets. The Wall Street Journal – Best budgeting app for hands-on budgeters. Forbes – Best Budgeting Apps Money – Best budgeting app for college students. Lifehacker – Most popular personal finance software. Wirecutter – "Great pick for hard-core budgeters". Investopedia – Best overall budgeting app.
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Blue check

A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official
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Digital history

Digital history is the use of digital media to further historical analysis, presentation, and research. It is a branch of the digital humanities and an extension of quantitative history, cliometrics, and computing. Digital history is commonly known as digital public history, concerned primarily with engaging online audiences with historical content, or digital research methods, that further academic research. Digital history outputs include: digital archives, online presentations, data and information visualizations, interactive maps, timelines, audio files, and virtual worlds. These outputs are designed to enhance accessibility to users, facilitating engagement with historical content. Recent digital history projects focus on creativity, collaboration, and technical innovation, text mining, corpus linguistics, network analysis, 3D modeling, and big data analysis. By utilizing these resources, the user can rapidly develop new analyses that can link to, extend, and bring to life existing histories. == History == Rooted in earlier social science history work, particularly around the history of enslavement in the United States, early digital history in the 1960s and 70s focused on using computers to conduct quantitative analyses, primarily of demographic and social history data - censuses, election returns, city directories, and other tabular or countable data. - with the aim of producing defensible research findings These early computers could be programmed to conduct statistical analyses of these records, creating tallies, or seeking trends across records. This research into historical demography was rooted in the rise of social history as a field of historical interest. The historians involved in this work sought to quantify past societies, to come to new conclusions about communities and population. Computers proved capable tools for that type of work. By the late 1970s younger historians turned to cultural studies, most of these studies involved online databases that were checked by Professionals in Great Britain about once a year. The outpouring of quantitative studies by established scholars continued. Since then, quantitative history and cliometrics have been used primarily by historically minded economists and political scientists. In the late 1980s quantifiers founded the Association for History and Computing. This movement provided some of the impetus for the rise of digital history in the 1990s. The more recent roots of digital history were in software rather than online networks. In 1982, the Library of Congress embarked on its Optical Disk Pilot Project, which placed text and images from its collection on to laserdiscs and CD-ROMs. The library started offering online exhibits in 1992 when it launched Selected Civil War Photographs. In 1993, Roy Rosenzweig, along with Steve Brier and Josh Brown, produced their award-winning CD-ROM Who Built America? From the Centennial Exposition of 1876 to the Great War of 1914, designed for Apple, Inc. that integrated images, text, film and sound clips, displayed in a visual interface that supported a text narrative. Among the earliest online digital history projects were The Heritage Project of the University of Kansas, and medieval historian Dr. Lynn Nelson's World History Index and History Central Catalogue. Another was The Valley of the Shadow, conceived in 1991 by current University of Richmond professor of humanities and president emeritus, Edward L. Ayers, who was then at the University of Virginia. The Institute for Advanced Technology in the Humanities (IATH) at the University of Virginia adopted the Valley Project and partnered with IBM to collect and transcribe historical sources into digital files. The project collected data related to Augusta County in Virginia and Franklin County in Pennsylvania during the American Civil War. In 1996, William G. Thomas III joined Ayers on the Valley Project. Together, they produced an online article entitled "The Differences Slavery Made: A Close Analysis of Two American Communities," which also appeared in The American Historical Review in 2003. A CD-ROM also accompanied the Valley Project, published by W. W. Norton and Company in 2000. Rosenzweig, who died October 11, 2007, founded the Center for History and New Media (CHNM) at George Mason University in 1994. Today, CHNM boasts several digital tools available to historians, such as Zotero, Omeka or Tropy. In 1997, Ayers and Thomas used the term "digital history" when they proposed and founded the Virginia Center for Digital History (VCDH) at the University of Virginia, the earliest center devoted exclusively to history. Several other institutions promoting digital history include the Center for Humane Arts, Letters, and Social Sciences Online (MATRIX) at Michigan State University, Maryland's Institute for Technology in the Humanities, and the Center for Digital Research in the Humanities at the University of Nebraska. In 2004, Emory University launched Southern Spaces, a "peer-reviewed Internet journal and scholarly forum" examining the history of the South. == Applications == There are many potential benefits to the use of digital history when combined with traditional historical methods. Some of these applications include: Combining traditional historical methods and new research methods in order to come to new conclusions. Using different tools to extract and analyse larger amounts of data that would not be manageable otherwise. Create models and maps of data extracted to create a visualisation of the data. Data extracted and analysed can be placed alongside existing historiography to increase combined historical knowledge. By adding new research methods to existing historical method, historians can benefit greatly from the ability to work with larger amounts of data and develop new interpretations from this. == Notable Projects == The collaborative nature of most digital history endeavors has meant that the discipline has developed primarily at institutions with the resources to sponsor content research and technical innovation. Two of the first centers, George Mason University's Center for History and New Media and the Virginia Center for Digital History at the University of Virginia have been among the leaders in the development of digital history projects and the education of digital historians. Some of the noteworthy projects emerging from these pioneering centers are The Geography of Slavery, The Texas Slavery Project, and The Countryside Transformed at VCDH and Liberty, Equality, Fraternity: Exploring the French Revolution and The Lost Museum at the CHNM. In each of these projects, mediated archives holding multiple types of sources are combined with digital tools to analyze and illuminate an historical question to a varying degree; this integration of content and tools with analysis is one of the hallmarks of digital history—projects move beyond archives or collections and into scholarly analysis and the use of digital tools to develop that analysis. The differences between the ways projects incorporate these integrations are a measure of the development of the field and point to the ongoing debates over what digital history can and should be. While many of the projects at VCDH, CHNM, and other university's centers have been geared towards academics and post-secondary education, the University of Victoria (British Columbia), in conjunction with the Université de Sherbrooke and the Ontario Institute for Studies in Education at the University of Toronto, has created as series of projects for all ages, "Great Unsolved Mysteries in Canadian History." Laden with instructional aids, this site asks teachers to introduce students to historical research methods to help them develop analytical skills and a sense of the complexities of their national history. Issues of race, religion, and gender are addressed in carefully constructed modules that cover incidents in Canadian history from Viking exploration through the 1920s. One of the original co-creators of the project, John Lutz has also developed Victoria's Victoria with the University of Victoria and Malaspina University-College. In addition to Ayers, Thomas, Lutz, and Rosenzweig, numerous other individual scholars work with digital history techniques and have made and/or continue to make important contributions to the field. Robert Darnton's 2000 article, "An Early Information Society: News and the Media in Eighteenth-Century Paris" was supplemented with electronic resources and is an early model of the discussions around digital history and its future in the humanities. One of the first major digital projects to be reviewed by the American Historical Review (AHR) was Philip Ethington's "Los Angeles and the Problem of Urban Historical Knowledge"—a multimedia exploration of changes to Los Angeles' physical profile over the course of several decades. In this essay, he also expresses his beliefs that historians have major power in
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Modulation error ratio

The modulation error ratio (MER) is a measure used to quantify the performance of a digital radio (or digital TV) transmitter or receiver in a communications system using digital modulation (such as QAM). A signal sent by an ideal transmitter or received by a receiver would have all constellation points precisely at the ideal locations, however various imperfections in the implementation (such as noise, low image rejection ratio, phase noise, carrier suppression, distortion, etc.) or signal path cause the actual constellation points to deviate from the ideal locations. Transmitter MER can be measured by specialized equipment, which demodulates the received signal in a similar way to how a real radio demodulator does it. Demodulated and detected signal can be used as a reasonably reliable estimate for the ideal transmitted signal in MER calculation. == Definition == An error vector is a vector in the I-Q plane between the ideal constellation point and the point received by the receiver. The Euclidean distance between the two points is its magnitude. The modulation error ratio is equal to the ratio of the root mean square (RMS) power (in Watts) of the reference vector to the power (in Watts) of the error. It is defined in dB as: M E R ( d B ) = 10 log 10 ⁡ ( P s i g n a l P e r r o r ) {\displaystyle \mathrm {MER(dB)} =10\log _{10}\left({P_{\mathrm {signal} } \over P_{\mathrm {error} }}\right)} where Perror is the RMS power of the error vector, and Psignal is the RMS power of ideal transmitted signal. MER is defined as a percentage in a compatible (but reciprocal) way: M E R ( % ) = P e r r o r P s i g n a l × 100 % {\displaystyle \mathrm {MER(\%)} ={\sqrt {P_{\mathrm {error} } \over P_{\mathrm {signal} }}}\times 100\%} with the same definitions. MER is closely related to error vector magnitude (EVM), but MER is calculated from the average power of the signal. MER is also closely related to signal-to-noise ratio. MER includes all imperfections including deterministic amplitude imbalance, quadrature error and distortion, while noise is random by nature.
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LakeFS

lakeFS is an open-source data version control system for managing data stored in object storage. It provides Git-like operations such as branching, committing, merging, and reverting for large-scale data stored in systems including Amazon S3, Azure Blob Storage, and Google Cloud Storage, as well as other S3-compatible object storage platforms. lakeFS is used in data engineering and machine learning workflows to manage changes to data, support reproducibility, and enable data governance across data lakes. The software is available as an open-source project, as well as in enterprise and managed service offerings, including lakeFS Cloud. == History == lakeFS was created in 2020 by Einat Orr and Oz Katz at Treeverse. Its first public release, version 0.8.1, appeared in August 2020 and introduced Git-style operations with support for Amazon S3. In 2021, Treeverse raised $23 million in a Series A funding round led by Dell Technologies Capital, Norwest Venture Partners, and Zeev Ventures. The same year, lakeFS was included in InfoWorld’s Best of Open Source Software (Bossie) awards. In June 2022, Treeverse introduced lakeFS Cloud, a managed service providing hosted lakeFS deployments for cloud-based data lakes. Version 1.0 was released in October 2023, adding integrations with platforms such as Databricks and Apache Iceberg, as well as support for orchestration tools including Apache Airflow. Public case studies and conference materials have described usage of lakeFS by organizations such as Microsoft, Volvo, and NASA. In July 2025, Treeverse announced an additional $20 million in growth funding to support further development of lakeFS. In November 2025, Treeverse announced the acquisition of the open-source data version control project DVC. == Software == === Overview === lakeFS provides Git-like operations such as branching, committing, merging, and reverting for datasets stored in object storage. These operations are used to manage changes to data, test modifications in isolation, reproduce specific data states, and recover from errors or unintended updates. === Architecture === lakeFS operates as a metadata layer on top of object storage systems such as Amazon S3, Azure Blob Storage, and Google Cloud Storage. It stores repository metadata describing commits, branches, and tags, enabling versioned views of data without copying underlying objects. The system provides access through multiple interfaces, including a web user interface, command-line tools, a REST API, and software development kits. It is designed to integrate with existing data engineering and machine learning workflows, and can be deployed either in self-hosted environments or as a managed service. === Functions === lakeFS provides version control functionality for data stored in object storage–based data lakes. Core features include: Atomic commits and version tracking for datasets, supporting reproducibility and auditability. Branching and merging mechanisms that allow isolated development and testing without duplicating data. Configurable hooks that can validate data or trigger external processes during commit and merge operations. The ability to revert repositories to earlier states to recover from data errors or failed changes. Recording of commit history and associated metadata for lineage tracking. Support for managing data across multiple object storage systems, including Amazon S3, Azure Blob Storage, Google Cloud Storage, and MinIO. Use of fixed data versions to reproduce experiments and machine learning model training. === Integrations === Coverage of lakeFS has described integrations with platforms such as Databricks and Apache Iceberg, as well as support for environments including Red Hat OpenShift. Additional materials describe its use with Trino, including validation of data changes prior to merging in versioned data workflows, as well as compatibility with orchestration tools such as Apache Airflow.
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Flat-panel display

A flat-panel display (FPD) is an electronic display used to display visual content such as text or images. It is present in consumer, medical, transportation, and industrial equipment. Flat-panel displays are thin, lightweight, provide better linearity and are capable of higher resolution and contrast than typical consumer-grade TVs from earlier eras. They are usually less than 10 centimetres (3.9 in) thick. While the highest resolution for consumer-grade CRT televisions is 1080i, many interactive flat panels in the 2020s are capable of 1080p and 4K resolution. In the 2010s, portable consumer electronics such as laptops, mobile phones, and portable cameras have used flat-panel displays since they consume less power and are lightweight. As of 2016, flat-panel displays have almost completely replaced CRT displays. Most 2010s-era flat-panel displays use LCD or light-emitting diode (LED) technologies, sometimes combined. Most LCD screens are back-lit with color filters used to display colors. In many cases, flat-panel displays are combined with touch screen technology, which allows the user to interact with the display in a natural manner. For example, modern smartphone displays often use OLED panels, with capacitive touch screens. Flat-panel displays can be divided into two display device categories: volatile and static. The former requires that pixels be periodically electronically refreshed to retain their state (e.g. liquid-crystal displays (LCD)), and can only show an image when it has power. On the other hand, static flat-panel displays rely on materials whose color states are bistable, such as displays that make use of e-ink technology, and as such retain content even when power is removed. == History == The first engineering proposal for a flat-panel TV was by General Electric in 1954 as a result of its work on radar monitors. The publication of their findings gave all the basics of future flat-panel TVs and monitors. But GE did not continue with the R&D required and never built a working flat panel at that time. The first production flat-panel display was the Aiken tube, developed in the early 1950s and produced in limited numbers in 1958. This saw some use in military systems as a heads up display and as an oscilloscope monitor, but conventional technologies overtook its development. Attempts to commercialize the system for home television use ran into continued problems and the system was never released commercially. Dennis Gabor, better known as the inventor of holography, patented a flat-screen CRT in 1958. This was substantially similar to Aiken's concept, and led to a years-long patent battle. By the time the lawsuits were complete, with Aiken's patent applying in the US and Gabor's in the UK, the commercial aspects had long lapsed, and the two became friends. Around this time, Clive Sinclair came across Gabor's work and began an ultimately unsuccessful decade-long effort to commercialize it. The Philco Predicta featured a relatively flat (for its day) cathode-ray tube setup and would be the first commercially released "flat panel" upon its launch in 1958; the Predicta was a commercial failure. The plasma display panel was invented in 1964 at the University of Illinois, according to The History of Plasma Display Panels. === Liquid-crystal displays (LC displays, or LCDs) === The MOSFET (metal–oxide–semiconductor field-effect transistor, or MOS transistor) was invented by Mohamed M. Atalla and Dawon Kahng at Bell Labs in 1959, and presented in 1960. Building on their work, Paul K. Weimer at RCA developed the thin-film transistor (TFT) in 1962. It was a type of MOSFET distinct from the standard bulk MOSFET. The idea of a TFT-based LCD was conceived by Bernard J. Lechner of RCA Laboratories in 1968. B.J. Lechner, F.J. Marlowe, E.O. Nester and J. Tults demonstrated the concept in 1968 with a dynamic scattering LCD that used standard discrete MOSFETs. The first active-matrix addressed electroluminescent display was made using TFTs by T. Peter Brody's Thin-Film Devices department at Westinghouse Electric Corporation in 1968. In 1973, Brody, J. A. Asars and G. D. Dixon at Westinghouse Research Laboratories demonstrated the first thin-film-transistor liquid-crystal display. Brody and Fang-Chen Luo demonstrated the first flat active-matrix liquid-crystal display (AM LCD) using TFTs in 1974. By 1982, pocket LCD TVs based on LCD technology were developed in Japan. The 2.1-inch Epson ET-10 Epson Elf was the first color LCD pocket TV, released in 1984. In 1988, a Sharp research team led by engineer T. Nagayasu demonstrated a 14-inch full-color LCD, which convinced the electronics industry that LCD would eventually replace CRTs as the standard television display technology. As of 2013, all modern high-resolution and high-quality electronic visual display devices use TFT-based active-matrix displays. === LED displays === The first usable LED display was developed by Hewlett-Packard (HP) and introduced in 1968. It was the result of research and development (R&D) on practical LED technology between 1962 and 1968, by a research team under Howard C. Borden, Gerald P. Pighini, and Mohamed M. Atalla, at HP Associates and HP Labs. In February 1969, they introduced the HP Model 5082-7000 Numeric Indicator. It was the first alphanumeric LED display, and was a revolution in digital display technology, replacing the Nixie tube for numeric displays and becoming the basis for later LED displays. In 1977, James P Mitchell prototyped and later demonstrated what was perhaps the earliest monochromatic flat-panel LED television display. Ching W. Tang and Steven Van Slyke at Eastman Kodak built the first practical organic LED (OLED) device in 1987. In 2003, Hynix produced an organic EL driver capable of lighting in 4,096 colors. In 2004, the Sony Qualia 005 was the first LED-backlit LCD. The Sony XEL-1, released in 2007, was the first OLED television. == Common types == === Liquid-crystal display (LCD) === Field-effect LCDs are lightweight, compact, portable, cheap, more reliable, and easier on the eyes than CRT screens. LCD screens use a thin layer of liquid crystal, a liquid that exhibits crystalline properties. It is sandwiched between two glass plates carrying transparent electrodes. Two polarizing films are placed at each side of the LCD. By generating a controlled electric field between electrodes, various segments or pixels of the liquid crystal can be activated, causing changes in their polarizing properties. These polarizing properties depend on the alignment of the liquid-crystal layer and the specific field-effect used, being either twisted nematic (TN), in-plane switching (IPS) or vertical alignment (VA). Color is produced by applying appropriate color filters (red, green and blue) to the individual subpixels. LC displays are used in various electronics like watches, calculators, mobile phones, TVs, computer monitors and laptops screens etc. === LED-LCD === Most earlier large LCD screens were back-lit using a number of CCFL (cold-cathode fluorescent lamps). However, small pocket size devices almost always used LEDs as their illumination source. With the improvement of LEDs, almost all new displays are now equipped with LED backlight technology. The image is still generated by the LCD layer. === Plasma panel === A plasma display consists of two glass plates separated by a thin gap filled with a gas such as neon. Each of these plates has several parallel electrodes running across it. The electrodes on the two plates are at right angles to each other. A voltage applied between the two electrodes one on each plate causes a small segment of gas at the two electrodes to glow. The glow of gas segments is maintained by a lower voltage that is continuously applied to all electrodes. By 2010, consumer plasma displays had been discontinued by numerous manufacturers. === Electroluminescent panel === In an electroluminescent display, the image is created by applying electrical signals to the plates which make the phosphor glow. === Organic light-emitting diode === An OLED (organic light-emitting diode) is a light-emitting diode (LED) in which the emissive electroluminescent layer is a film of organic compound which emits light in response to an electric current. This layer of organic semiconductor is situated between two electrodes; typically, at least one of these electrodes is transparent. OLEDs are used to create digital displays in devices such as television screens, computer monitors, portable systems such as mobile phones, handheld game consoles and PDAs. === Quantum-dot light-emitting diode === QLED or quantum dot LED is a flat panel display technology introduced by Samsung under this trademark. Other television set manufacturers such as Sony have used the same technology to enhance the backlighting of LCD TVs already in 2013. Quantum dots create their own unique light when illuminated by a light source of shorter wavelength such as blue LEDs. Th
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Browser sniffing

Browser sniffing (also known as User agent sniffing and browser detection) is a set of techniques used in websites and web applications in order to determine the web browser a visitor is using, and to serve browser-appropriate content to the visitor. It is also used to detect mobile browsers and send them mobile-optimized websites. This practice is sometimes used to circumvent incompatibilities between browsers due to misinterpretation of HTML, Cascading Style Sheets (CSS), or the Document Object Model (DOM). While the World Wide Web Consortium maintains up-to-date central versions of some of the most important Web standards in the form of recommendations, in practice no software developer has designed a browser which adheres exactly to these standards; implementation of other standards and protocols, such as SVG and XMLHttpRequest, varies as well. As a result, different browsers display the same page differently, and so browser sniffing was developed to detect the web browser in order to help ensure consistent display of content. == Sniffer methods == === Client-side sniffing === Web pages can use programming languages such as JavaScript which are interpreted by the user agent, with results sent to the web server. For example: This code is run by the client computer, and the results are used by other code to make necessary adjustments on client-side. In this example, the client computer is asked to determine whether the browser can use a feature called ActiveX. Since this feature was proprietary to Microsoft, a positive result will indicate that the client may be running Microsoft's Internet Explorer. This is no longer a reliable indicator since Microsoft's open-source release of the ActiveX code, however, meaning that it can be used by any browser. === Standard Browser detection method === The web server communicates with the client using a communication protocol known as HTTP, or Hypertext Transfer Protocol, which specifies that the client send the server information about the browser being used to view the website in a User-Agent header. === Server-side sniffing === Extensive browser techniques enable persistent user tracking even if users try to stay anonymous. See device fingerprint for more details on browser fingerprinting. == Issues and standards == Many websites use browser sniffing to determine whether a visitor's browser is unable to use certain features (such as JavaScript, DHTML, ActiveX, or cascading style sheets), and display an error page if a certain browser is not used. However, it is virtually impossible to account for the tremendous variety of browsers available to users. Generally, a web designer using browser sniffing to determine what kind of page to present will test for the three or four most popular browsers, and provide content tailored to each of these. If a user is employing a user agent not tested for, there is no guarantee that a usable page will be served; thus, the user may be forced either to change browsers or to avoid the page. The World Wide Web Consortium, which sets standards for the construction of web pages, recommends that web sites be designed in accordance with its standards, and be arranged to "fail gracefully" when presented to a browser which cannot deal with a particular standard. Browser sniffing increases maintenance needed. Websites treating some browsers differently should provide an alternative version for other browsers. Use of user agent strings are error-prone because the developer must check for the appropriate part, such as "Gecko" instead of "Firefox". They must also ensure that future versions are supported. Furthermore, some browsers allow changing the user agent string, making the technique useless.
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Blue check

A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official
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Connectionist expert system

Connectionist expert systems are artificial neural network (ANN) based expert systems where the ANN generates inferencing rules e.g., fuzzy-multi layer perceptron where linguistic and natural form of inputs are used. Apart from that, rough set theory may be used for encoding knowledge in the weights better and also genetic algorithms may be used to optimize the search solutions better. Symbolic reasoning methods may also be incorporated (see hybrid intelligent system). (Also see expert system, neural network, clinical decision support system.)
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Coupling (electronics)

In electronics, electric power and telecommunication, coupling is the transfer of electrical energy from one circuit to another, or between parts of a circuit. Coupling can be deliberate as part of the function of the circuit, or it may be undesirable, for instance due to coupling to stray fields. For example, energy is transferred from a power source to an electrical load by means of conductive coupling, which may be either resistive or direct coupling. An AC potential may be transferred from one circuit segment to another having a DC potential by use of a capacitor. Electrical energy may be transferred from one circuit segment to another segment with different impedance by use of a transformer; this is known as impedance matching. These are examples of electrostatic and electrodynamic inductive coupling. == Types == Electrical conduction: Direct coupling, also called conductive coupling and galvanic coupling Resistive conduction Atmospheric plasma channel coupling Electromagnetic induction: Electrodynamic induction — commonly called inductive coupling, also magnetic coupling Capacitive coupling Evanescent wave coupling Electromagnetic radiation: Radio waves — Wireless telecommunications. Electromagnetic interference (EMI) — Sometimes called radio frequency interference (RFI), is unwanted coupling. Electromagnetic compatibility (EMC) requires techniques to avoid such unwanted coupling, such as electromagnetic shielding. Microwave power transmission Other kinds of energy coupling: Acoustic coupler
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Active networking

Active networking is a communication pattern that allows packets flowing through a telecommunications network to dynamically modify the operation of the network. Active network architecture is composed of execution environments (similar to a unix shell that can execute active packets), a node operating system capable of supporting one or more execution environments. It also consists of active hardware, capable of routing or switching as well as executing code within active packets. This differs from the traditional network architecture which seeks robustness and stability by attempting to remove complexity and the ability to change its fundamental operation from underlying network components. Network processors are one means of implementing active networking concepts. Active networks have also been implemented as overlay networks. == What does it offer? == Active networking allows the possibility of highly tailored and rapid "real-time" changes to the underlying network operation. This enables such ideas as sending code along with packets of information allowing the data to change its form (code) to match the channel characteristics. The smallest program that can generate a sequence of data can be found in the definition of Kolmogorov complexity. The use of real-time genetic algorithms within the network to compose network services is also enabled by active networking. == How it relates to other networking paradigms == Active networking relates to other networking paradigms primarily based upon how computing and communication are partitioned in the architecture. === Active networking and software-defined networking === Active networking is an approach to network architecture with in-network programmability. The name derives from a comparison with network approaches advocating minimization of in-network processing, based on design advice such as the "end-to-end argument". Two major approaches were conceived: programmable network elements ("switches") and capsules, a programmability approach that places computation within packets traveling through the network. Treating packets as programs later became known as "active packets". Software-defined networking decouples the system that makes decisions about where traffic is sent (the control plane) from the underlying systems that forward traffic to the selected destination (the data plane). The concept of a programmable control plane originated at the University of Cambridge in the Systems Research Group, where (using virtual circuit identifiers available in Asynchronous Transfer Mode switches) multiple virtual control planes were made available on a single physical switch. Control Plane Technologies (CPT) was founded to commercialize this concept. == Fundamental challenges == Active network research addresses the nature of how best to incorporate extremely dynamic capability within networks. In order to do this, active network research must address the problem of optimally allocating computation versus communication within communication networks. A similar problem related to the compression of code as a measure of complexity is addressed via algorithmic information theory. One of the challenges of active networking has been the inability of information theory to mathematically model the active network paradigm and enable active network engineering. This is due to the active nature of the network in which communication packets contain code that dynamically change the operation of the network. Fundamental advances in information theory are required in order to understand such networks. == Nanoscale active networks == As the limit in reduction of transistor size is reached with current technology, active networking concepts are being explored as a more efficient means accomplishing computation and communication. More on this can be found in nanoscale networking.
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Mean opinion score

Mean opinion score (MOS) is a measure used in the domain of Quality of Experience and telecommunications engineering, representing overall quality of a stimulus or system. It is the arithmetic mean over all individual "values on a predefined scale that a subject assigns to his opinion of the performance of a system quality". Such ratings are usually gathered in a subjective quality evaluation test, but they can also be algorithmically estimated. MOS is a commonly used measure for video, audio, and audiovisual quality evaluation, but not restricted to those modalities. ITU-T has defined several ways of referring to a MOS in Recommendation ITU-T P.800.1, depending on whether the score was obtained from audiovisual, conversational, listening, talking, or video quality tests. == Rating scales and mathematical definition == The MOS is expressed as a single rational number, typically in the range 1–5, where 1 is lowest perceived quality, and 5 is the highest perceived quality. Other MOS ranges are also possible, depending on the rating scale that has been used in the underlying test. The Absolute Category Rating scale is very commonly used, which maps ratings between Bad and Excellent to numbers between 1 and 5, as seen in below table. Other standardized quality rating scales exist in ITU-T Recommendations (such as ITU-T P.800 or ITU-T P.910). For example, one could use a continuous scale ranging between 1–100. Which scale is used depends on the purpose of the test. In certain contexts there are no statistically significant differences between ratings for the same stimuli when they are obtained using different scales. The MOS is calculated as the arithmetic mean over single ratings performed by human subjects for a given stimulus in a subjective quality evaluation test. Thus: M O S = ∑ n = 1 N R n N {\displaystyle MOS={\frac {\sum _{n=1}^{N}{R_{n}}}{N}}} Where R {\displaystyle R} are the individual ratings for a given stimulus by N {\displaystyle N} subjects. == Properties of the MOS == The MOS is subject to certain mathematical properties and biases. In general, there is an ongoing debate on the usefulness of the MOS to quantify Quality of Experience in a single scalar value. When the MOS is acquired using a categorical rating scales, it is based on – similar to Likert scales – an ordinal scale. In this case, the ranking of the scale items is known, but their interval is not. Therefore, it is mathematically incorrect to calculate a mean over individual ratings in order to obtain the central tendency; the median should be used instead. However, in practice and in the definition of MOS, it is considered acceptable to calculate the arithmetic mean. It has been shown that for categorical rating scales (such as ACR), the individual items are not perceived equidistant by subjects. For example, there may be a larger "gap" between Good and Fair than there is between Good and Excellent. The perceived distance may also depend on the language into which the scale is translated. However, there exist studies that could not prove a significant impact of scale translation on the obtained results. Several other biases are present in the way MOS ratings are typically acquired. In addition to the above-mentioned issues with scales that are perceived non-linearly, there is a so-called "range-equalization bias": subjects, over the course of a subjective experiment, tend to give scores that span the entire rating scale. This makes it impossible to compare two different subjective tests if the range of presented quality differs. In other words, the MOS is never an absolute measure of quality, but only relative to the test in which it has been acquired. For the above reasons – and due to several other contextual factors influencing the perceived quality in a subjective test – a MOS value should only be reported if the context in which the values have been collected in is known and reported as well. MOS values gathered from different contexts and test designs therefore should not be directly compared. Recommendation ITU-T P.800.2 prescribes how MOS values should be reported. Specifically, P.800.2 says:it is not meaningful to directly compare MOS values produced from separate experiments, unless those experiments were explicitly designed to be compared, and even then the data should be statistically analysed to ensure that such a comparison is valid. == MOS for speech and audio quality estimation == MOS historically originates from subjective measurements where listeners would sit in a "quiet room" and score a telephone call quality as they perceived it. This kind of test methodology had been in use in the telephony industry for decades and was standardized in Recommendation ITU-T P.800. It specifies that "the talker should be seated in a quiet room with volume between 30 and 120 m³ and a reverberation time less than 500 ms (preferably in the range 200–300 ms). The room noise level must be below 30 dBA with no dominant peaks in the spectrum." Requirements for other modalities were similarly specified in later ITU-T Recommendations. == MOS estimation using quality models == Obtaining MOS ratings may be time-consuming and expensive as it requires the recruitment of human assessors. For various use cases such as codec development or service quality monitoring purposes – where quality should be estimated repeatedly and automatically – MOS scores can also be predicted by objective quality models, which typically have been developed and trained using human MOS ratings. A question that arises from using such models is whether the MOS differences produced are noticeable to the users. For example, when rating images on a five point MOS scale, an image with a MOS equal to 5 is expected to be noticeably better in quality than one with a MOS equal to 1. Contrary to that, it is not evident whether an image with a MOS equal to 3.8 is noticeably better in quality than one with a MOS equal to 3.6. Research conducted on determining the smallest MOS difference that is perceptible to users for digital photographs showed that a MOS difference of approximately 0.46 is required in order for 75% of the users to be able to detect the higher quality image. Nevertheless, image quality expectation, and hence MOS, changes over time with the change of user expectations. As a result, minimum noticeable MOS differences determined using analytical methods such as in may change over time.
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Lazy learning

(Not to be confused with the lazy learning regime, see Neural tangent kernel). In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries. The primary motivation for employing lazy learning, as in the K-nearest neighbors algorithm, used by online recommendation systems ("people who viewed/purchased/listened to this movie/item/tune also ...") is that the data set is continuously updated with new entries (e.g., new items for sale at Amazon, new movies to view at Netflix, new clips at YouTube, new music at Spotify or Pandora). Because of the continuous update, the "training data" would be rendered obsolete in a relatively short time especially in areas like books and movies, where new best-sellers or hit movies/music are published/released continuously. Therefore, one cannot really talk of a "training phase". Lazy classifiers are most useful for large, continuously changing datasets with few attributes that are commonly queried. Specifically, even if a large set of attributes exist - for example, books have a year of publication, author/s, publisher, title, edition, ISBN, selling price, etc. - recommendation queries rely on far fewer attributes - e.g., purchase or viewing co-occurrence data, and user ratings of items purchased/viewed. == Advantages == The main advantage gained in employing a lazy learning method is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple problems and deal successfully with changes in the problem domain. At the same time they can reuse a lot of theoretical and applied results from linear regression modelling (notably PRESS statistic) and control. It is said that the advantage of this system is achieved if the predictions using a single training set are only developed for few objects. This can be demonstrated in the case of the k-NN technique, which is instance-based and function is only estimated locally. == Disadvantages == Theoretical disadvantages with lazy learning include: The large space requirement to store the entire training dataset. In practice, this is not an issue because of advances in hardware and the relatively small number of attributes (e.g., as co-occurrence frequency) that need to be stored. Particularly noisy training data increases the case base unnecessarily, because no abstraction is made during the training phase. In practice, as stated earlier, lazy learning is applied to situations where any learning performed in advance soon becomes obsolete because of changes in the data. Also, for the problems for which lazy learning is optimal, "noisy" data does not really occur - the purchaser of a book has either bought another book or hasn't. Lazy learning methods are usually slower to evaluate. In practice, for very large databases with high concurrency loads, the queries are not postponed until actual query time, but recomputed in advance on a periodic basis - e.g., nightly, in anticipation of future queries, and the answers stored. This way, the next time new queries are asked about existing entries in the database, the answers are merely looked up rapidly instead of having to be computed on the fly, which would almost certainly bring a high-concurrency multi-user system to its knees. Larger training data also entail increased cost. Particularly, there is the fixed amount of computational cost, where a processor can only process a limited amount of training data points. There are standard techniques to improve re-computation efficiency so that a particular answer is not recomputed unless the data that impact this answer has changed (e.g., new items, new purchases, new views). In other words, the stored answers are updated incrementally. This approach, used by large e-commerce or media sites, has long been used in the Entrez portal of the National Center for Biotechnology Information (NCBI) to precompute similarities between the different items in its large datasets: biological sequences, 3-D protein structures, published-article abstracts, etc. Because "find similar" queries are asked so frequently, the NCBI uses highly parallel hardware to perform nightly recomputation. The recomputation is performed only for new entries in the datasets against each other and against existing entries: the similarity between two existing entries need not be recomputed. == Examples of Lazy Learning Methods == K-nearest neighbors, which is a special case of instance-based learning. Local regression. Lazy naive Bayes rules, which are extensively used in commercial spam detection software. Here, the spammers keep getting smarter and revising their spamming strategies, and therefore the learning rules must also be continually updated.
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Hardware backdoor

A hardware backdoor is a backdoor implemented within the physical components of a computer system, also known as its hardware. They can be created by introducing malicious code to a component's firmware, or even during the manufacturing process of an integrated circuit. Often, they are used to undermine security in smartcards and cryptoprocessors, unless investment is made in anti-backdoor design methods. They have also been considered for car hacking. Backdoors differ from hardware Trojans as backdoors are introduced intentionally by the original designer or during the design process, whereas hardware Trojans are inserted later by an external party. == Background == The existence of hardware backdoors poses significant security risks for several reasons. They are difficult to detect and are impossible to remove using conventional methods like antivirus software. They can also bypass other security measures, such as disk encryption. Hardware trojans can be introduced during manufacturing where the end-user lacks control over the production chain. == History == In 2008, the FBI reported the discovery of approximately 3,500 counterfeit Cisco network components in the United States, some of which were introduced in military and government infrastructure. In the same year, the possibility of a backdoor SPARC CPU was demonstrated with an FPGA running Linux that supported various hidden malicious services. A few years later, in 2011, Jonathan Brossard presented "Rakshasa", a proof-of-concept hardware backdoor. This backdoor could be installed by an individual with physical access to the hardware. It utilized coreboot to re-flash the BIOS with a SeaBIOS and iPXE-based bootkit composed of legitimate, open-source tools, allowing malware to be fetched from the internet during the boot process. The following year, in 2012, Sergei Skorobogatov and Christopher Woods from the University of Cambridge Computer Laboratory reported the discovery of a backdoor in a military-grade FPGA device, which could be exploited to access and modify sensitive information. It has been said that this was proven to be a software problem and not a deliberate attempt at sabotage. This still brought to attention that equipment manufacturers should ensure that microchips operate as intended. Later that year, two mobile phones developed by the Chinese company ZTE were found to carry a root access backdoor. According to security researcher Dmitri Alperovitch, the exploit used a hard-coded password in its software. Starting in 2012, the United States stated that Huawei might have backdoors present in their products. In 2013, researchers at the University of Massachusetts devised a method of breaking a CPU's internal cryptographic mechanisms by introducing specific impurities into the crystalline structure of transistors to change Intel's random-number generator. Documents revealed from 2013 onwards during the surveillance disclosures initiated by Edward Snowden showed that the Tailored Access Operations (TAO) unit and other NSA employees intercepted servers, routers, and other network gear being shipped to organizations targeted for surveillance to install covert implant firmware onto them before delivery. These tools include custom BIOS exploits that survive the reinstallation of operating systems and USB cables with spy hardware and radio transceiver packed inside. In June 2016 it was reported that University of Michigan Department of Electrical Engineering and Computer Science had built a hardware backdoor that leveraged "analog circuits to create a hardware attack" so that after the capacitors store up enough electricity to be fully charged, it would be switched on, to give an attacker complete access to whatever system or device − such as a PC − that contains the backdoored chip. In the study that won the "best paper" award at the IEEE Symposium on Privacy and Security they also note that microscopic hardware backdoor wouldn't be caught by practically any modern method of hardware security analysis, and could be planted by a single employee of a chip factory. In October 2018 Bloomberg reported that an attack by Chinese spies reached almost 30 U.S. companies, including Amazon and Apple, by compromising America's technology supply chain. == Countermeasures == Skorobogatov has developed a technique capable of detecting malicious insertions into chips. New York University Tandon School of Engineering researchers have developed a way to corroborate a chip's operation using verifiable computing whereby "manufactured for sale" chips contain an embedded verification module that proves the chip's calculations are correct and an associated external module validates the embedded verification module. Another technique developed by researchers at University College London (UCL) relies on distributing trust between multiple identical chips from disjoint supply chains. Assuming that at least one of those chips remains honest the security of the device is preserved. Researchers at the University of Southern California Ming Hsieh Department of Electrical and Computer Engineering and the Photonic Science Division at the Paul Scherrer Institute have developed a new technique called Ptychographic X-ray laminography. This technique is the only current method that allows for verification of the chips blueprint and design without destroying or cutting the chip. It also does so in significantly less time than other current methods. Anthony F. J. Levi Professor of electrical and computer engineering at University of Southern California explains “It’s the only approach to non-destructive reverse engineering of electronic chips—[and] not just reverse engineering but assurance that chips are manufactured according to design. You can identify the foundry, aspects of the design, who did the design. It’s like a fingerprint.” This method currently is able to scan chips in 3D and zoom in on sections and can accommodate chips up to 12 millimeters by 12 millimeters easily accommodating an Apple A12 chip but not yet able to scan a full Nvidia Volta GPU. "Future versions of the laminography technique could reach a resolution of just 2 nanometers or reduce the time for a low-resolution inspection of that 300-by-300-micrometer segment to less than an hour, the researchers say."
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ISO/IEC JTC 1/SC 6

ISO/IEC JTC 1/SC 6 Telecommunications and information exchange between systems is a standardization subcommittee of the Joint Technical Committee ISO/IEC JTC 1. It is part of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of telecommunications and information exchange between systems. ISO/IEC JTC 1/SC 6 was established in 1964, following the creation of a Special Working Group under ISO/TC 97 on Data Link Control Procedures and Modem Interfaces. The international secretariat of ISO/IEC JTC 1/SC 6 is the Korean Agency for Technology and Standards (KATS), located in South Korea. == Scope == The scope of ISO/IEC JTC 1/SC 6 is “Standardization in the field of telecommunications dealing with the exchange of information between open systems including system functions, procedures, parameters as well as the conditions for their use. The standardization encompasses protocols and services of lower layers, including physical, data link, network, and transport as well as those of upper layers including but not limited to Directory and ASN.1.” Future Network has recently been added as an important work scope. A considerable part of the work is done in effective cooperation with ITU-T and other standardization bodies including IEEE 802 and Ecma International. == Structure == ISO/IEC JTC 1/SC 6 has three active working groups (WGs), each of which carries out specific tasks in standards development within the field of telecommunications and information exchange between systems. The focus of each working group is described in the group’s terms of reference. Working groups can be established if new working areas arise, or disbanded if the group’s working area is no longer relevant to standardization needs. Active working groups of ISO/IEC JTC 1/SC 6 are: == Collaborations == ISO/IEC JTC 1/SC 6 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC. Organizations internal to ISO or IEC that collaborate with or are in liaison with ISO/IEC JTC 1/SC 6 include: ISO/IEC JTC 1/WG 7, Sensor networks ISO/IEC JTC 1/SC 17, Cards and personal identification ISO/IEC JTC 1/SC 25, Interconnection of information technology equipment ISO/IEC JTC 1/SC 27, IT security techniques ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 31, Automatic identification and data capture techniques ISO/IEC JTC 1/SC 38, Distributed application platforms & services (DAPS) ISO/TC 68, Financial services ISO/TC 122, Packaging ISO/TC 184/SC 5, Interoperability, integration, and architectures for enterprise systems and automation applications ISO/TC 215, Health Informatics IEC/SC 46A, Coaxial cables IEC/SC 46C, Wires and symmetric cables IEC/TC 48, Electrical connectors and mechanical structures for electrical and electronic equipment IEC/SC 48B, Electrical connectors IEC/TC 65, Industrial-process measurement, control and automation IEC/SC 65C, Industrial networks IEC/TC 86, Fibre optics IEC/SC 86C, Fibre optic systems and active devices IEC/TC 93, Design automation Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 6 include: European Conference of Postal and Telecommunications Administrations (CEPT) European Organization for Nuclear Research (CERN) European Commission (EC) European Telecommunications Standards Institute (ETSI) Ecma International International Civil Aviation Organization (ICAO) IEEE 802 LMSC (LAN/MAN Standards Committee) Internet Society (ISOC) International Telecommunications Satellite Organization (ITSO) ITU-T Organization for the Advancement of Structured Information Standards (OASIS) NFC Forum MFA Forum United Nations Conference on Trade and Development (UNCTAD) United Nations Economic Commission for Europe (UNECE) Universal Postal Union (UPU) World Meteorological Organization (WMO) CEN/TC 247/WG 4 == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 19 "P" (participating) members of ISO/IEC JTC 1/SC 6 are: Austria, Belgium, Canada, China, Czech Republic, Finland, Germany, Greece, Jamaica, Japan, Kazakhstan, Republic of Korea, Netherlands, Russian Federation, Spain, Switzerland, Tunisia, United Kingdom, and United States. The 31 "O" (observing) members of ISO/IEC JTC 1/SC 6 are: Argentina, Bosnia and Herzegovina, Colombia, Cuba, Cyprus, France, Ghana, Hong Kong, Hungary, Iceland, India, Indonesia, Islamic Republic of Iran, Ireland, Italy, Kenya, Luxembourg, Malaysia, Malta, New Zealand, Norway, Philippines, Poland, Romania, Saudi Arabia, Serbia, Singapore, Slovenia, Thailand, Turkey, and Ukraine. == Published standards == There are 365 published standards under the direct responsibility of ISO/IEC JTC 1/SC 6. Published standards by ISO/IEC JTC 1/SC 6 include:
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