AI Assistant Examples

AI Assistant Examples — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Labeled data

  Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags called judgments. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor. Labels can be obtained by having humans make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data. The quality of labeled data directly influences the performance of supervised machine learning models in operation, as these models learn from the provided labels. == Crowdsourced labeled data == In 2006, Fei-Fei Li, the co-director of the Stanford Human-Centered AI Institute, initiated research to improve the artificial intelligence models and algorithms for image recognition by significantly enlarging the training data. The researchers downloaded millions of images from the World Wide Web and a team of undergraduates started to apply labels for objects to each image. In 2007, Li outsourced the data labeling work on Amazon Mechanical Turk, an online marketplace for digital piece work. The 3.2 million images that were labeled by more than 49,000 workers formed the basis for ImageNet, one of the largest hand-labeled database for outline of object recognition. == Automated data labelling == After obtaining a labeled dataset, machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted for that piece of unlabeled data. == Challenges == === Data-driven bias === Algorithmic decision-making is subject to programmer-driven bias as well as data-driven bias. Training data that relies on bias labeled data will result in prejudices and omissions in a predictive model, despite the machine learning algorithm being legitimate. The labeled data used to train a specific machine learning algorithm needs to be a statistically representative sample to not bias the results. For example, in facial recognition systems underrepresented groups are subsequently often misclassified if the labeled data available to train has not been representative of the population,. In 2018, a study by Joy Buolamwini and Timnit Gebru demonstrated that two facial analysis datasets that have been used to train facial recognition algorithms, IJB-A and Adience, are composed of 79.6% and 86.2% lighter skinned humans respectively. === Human error and inconsistency === Human annotators are prone to errors and biases when labeling data. This can lead to inconsistent labels and affect the quality of the data set. The inconsistency can affect the machine learning model's ability to generalize well. === Domain expertise === Certain fields, such as legal document analysis or medical imaging, require annotators with specialized domain knowledge. Without the expertise, the annotations or labeled data may be inaccurate, negatively impacting the machine learning model's performance in a real-world scenario.

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  Acousto-electronics

  Acousto-electronics (also spelled 'Acoustoelectronics') is a branch of physics, acoustics and electronics that studies interactions of ultrasonic and hypersonic waves in solids with electrons and with electro-magnetic fields. Typical phenomena studied in acousto-electronics are acousto-electric effect and also amplification of acoustic waves by flows of electrons in piezoelectric semiconductors, when the drift velocity of the electrons exceeds the velocity of sound. The term 'acousto-electronics' is often understood in a wider sense to include numerous practical applications of the interactions of electro-magnetic fields with acoustic waves in solids. In particular, these are signal processing devices using surface acoustic waves (SAW), different sensors of temperature, pressure, humidity, acceleration, etc.

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  Vue.js

  Vue.js (commonly referred to as Vue; pronounced "view") is an open-source model–view–viewmodel front end JavaScript framework for building user interfaces and single-page applications. It was created by Evan You and is maintained by him and the rest of the active core team members. == Overview == Vue.js features an incrementally adaptable architecture that focuses on declarative rendering and component composition. The core library is focused on the view layer only. Advanced features required for complex applications such as routing, state management and build tooling are offered via officially maintained supporting libraries and packages. Vue.js allows for extending HTML with HTML attributes called directives. The directives offer functionality to HTML applications, and come as either built-in or user defined directives. == History == Vue was created by Evan You after working for Google using AngularJS in several projects. He later summed up his thought process: "I figured, what if I could just extract the part that I really liked about Angular and build something really lightweight." The first source code commit to the project was dated July 2013, at which time it was originally named "Seed". Vue was first publicly announced the following February, in 2014. Version names are often derived from manga and anime series, with the first letters arranged in alphabetical order. === Versions === When a new major is released i.e. v3.y.z, the last minor i.e. 2.x.y will become a LTS release for 18 months (bug fixes and security patches) and for the following 18 months will be in maintenance mode (security patches only). Vue 3 was officially released in September 2020. According to the State of Vue.js Report 2025, 96% of surveyed developers reported having used Vue 3.x. However, 35% also indicated that they used Vue 2.7.x in the past year, reflecting continued reliance on Vue 2 despite its end of support. The report also noted that more than a quarter of respondents encountered challenges when migrating from Vue 2 to Vue 3. === State management evolution === 2015 - Vuex introduced as official state management solution 2021 - Pinia development begins as Vuex 5 experiment 2022 - Pinia becomes officially recommended for new projects 2023 - Vue team announces Vuex maintenance mode transition According to the State of Vue.js Report 2025, the Vue's core team recommendation is reflected in developer adoption–over 80% of surveyed developers reported using Pinia while Vuex still had 38.4% usage, indicating ongoing reliance on the older library. == Features == === Components === Vue components extend basic HTML elements to encapsulate reusable code. At a high level, components are custom elements to which the Vue's compiler attaches behavior. In Vue, a component is essentially a Vue instance with pre-defined options. The code snippet below contains an example of a Vue component. The component presents a button and prints the number of times the button is clicked: === Templates === Vue uses an HTML-based template syntax that allows binding the rendered DOM to the underlying Vue instance's data. All Vue templates are valid HTML that can be parsed by specification-compliant browsers and HTML parsers. Vue compiles the templates into virtual DOM render functions. A virtual Document Object Model (or "DOM") allows Vue to render components in its memory before updating the browser. Combined with the reactivity system, Vue can calculate the minimal number of components to re-render and apply the minimal amount of DOM manipulations when the app state changes. Vue users can use template syntax or choose to directly write render functions using hyperscript either through function calls or JSX. Render functions allow applications to be built from software components. === Reactivity === Vue features a reactivity system that uses plain JavaScript objects and optimized re-rendering. Each component keeps track of its reactive dependencies during its render, so the system knows precisely when to re-render, and which components to re-render. === Transitions === Vue provides a variety of ways to apply transition effects when items are inserted, updated, or removed from the DOM. This includes tools to: Automatically apply classes for CSS transitions and animations Integrate third-party CSS animation libraries, such as Animate.css Use JavaScript to directly manipulate the DOM during transition hooks Integrate third-party JavaScript animation libraries, such as Velocity.js When an element wrapped in a transition component is inserted or removed, this is what happens: Vue will automatically sniff whether the target element has CSS transitions or animations applied. If it does, CSS transition classes will be added/removed at appropriate timings. If the transition component provided JavaScript hooks, these hooks will be called at appropriate timings. If no CSS transitions/animations are detected and no JavaScript hooks are provided, the DOM operations for insertion and/or removal will be executed immediately on next frame. === Routing === A traditional disadvantage of single-page applications (SPAs) is the inability to share links to the exact "sub" page within a specific web page. Because SPAs serve their users only one URL-based response from the server (it typically serves index.html or index.vue), bookmarking certain screens or sharing links to specific sections is normally difficult if not impossible. To solve this problem, many client-side routers delimit their dynamic URLs with a "hashbang" (#!), e.g. page.com/#!/. However, with HTML5 most modern browsers support routing without hashbangs. Vue provides an interface to change what is displayed on the page based on the current URL path – regardless of how it was changed (whether by emailed link, refresh, or in-page links). Additionally, using a front-end router allows for the intentional transition of the browser path when certain browser events (i.e. clicks) occur on buttons or links. Vue itself doesn't come with front-end hashed routing. But the open-source "vue-router" package provides an API to update the application's URL, supports the back button (navigating history), and email password resets or email verification links with authentication URL parameters. It supports mapping nested routes to nested components and offers fine-grained transition control. With Vue, developers are already composing applications with small building blocks building larger components. With vue-router added to the mix, components must merely be mapped to the routes they belong to, and parent/root routes must indicate where children should render. The code above: Sets a front-end route at websitename.com/user/. Which will render in the User component defined in (const User...) Allows the User component to pass in the particular id of the user which was typed into the URL using the $route object's params key: $route.params.id. This template (varying by the params passed into the router) will be rendered into inside the DOM's div#app. The finally generated HTML for someone typing in: websitename.com/user/1 will be: == Ecosystem == The core library comes with tools and libraries both developed by the core team and contributors. === Official tooling === Devtools – Browser devtools extension for debugging Vue.js applications Vite – Standard Tooling for rapid Vue.js development Vue Loader – a webpack loader that allows the writing of Vue components in a format called Single-File Components (SFCs) Vue.js Plugins Collection - Collection of almost 100 plugins and ecosystem libraries across various categories. === Official libraries === Vue Router – The official router, suitable for building SPAs Pinia – The official state management solution === Video courses === Vue School – Expert-led courses on Vue.js and its ecosystem. === State management libraries === Pinia – Official state management solution with modular architecture Vuex – Legacy state management library, now in maintenance mode VueUse – Collection of 200+ composition utilities including state management helpers === Community & Core Teams Resources === The State of Vue.js Report - A comprehensive publication about Vue.js created since 2017 by Monterail, Vue & Nuxt Official Partner. Each edition includes unique data from developer survey, key ecosystem trends and case studies. The latest 5th edition released in March 2025 was co-created with Evan You and Vue&Nuxt Core Teams. Although the Vue.js Ecosystem is generally very well-developed, developers point to some ecosystem gaps as one of the most important thing missing (as of March 2025 Developer Survey in the State of Vue.js Report 2025). 22% of respondents mentioned the lack of robust, official component libraries like MUI or Radix, and the need for better testing utilities. There was also demand for more modular, enterprise-ready solutions for dashboards, e-commerce, and animation libraries similar to Fr

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  Usage share of operating systems

  The usage share of an operating system is the percentage of computers running that operating system (OS). These statistics are estimates as wide scale OS usage data is difficult to obtain and measure. Reliable primary sources are limited and data collection methodology is not formally agreed. Currently devices connected to the internet allow for web data collection to approximately measure OS usage. As of December 2025, Android, which uses the Linux kernel, is the world's most popular operating system with 38.94% of the global market, followed by Windows with 29.99%, iOS with 15.66%, macOS with 2.14%, and other operating systems with 10.78%. This is for all device types excluding embedded devices. For smartphones and other mobile devices, Android has 72% market share, and Apple's iOS has 28%. For desktop computers and laptops, Microsoft Windows has 60.8%, followed by unknown operating systems at 19.7%, Mac OS at 14.4%, desktop Linux at 3.2%, then Google's ChromeOS at 1.6%, as of March 2026. For tablets, Apple's iPadOS (a variant of iOS) has 52% share and Android has 48% worldwide. For the top 500 most powerful supercomputers, Linux distributions have had 100% of the market share since 2017. The global server operating system market share has Linux leading with a 63.1% marketshare, followed by Windows, Unix and other operating systems. Linux is also most used for web servers, and the most common Linux distribution is Ubuntu, followed by Debian. Linux has almost caught up with the second-most popular (desktop) OS, macOS, in some regions, such as in South America, and in Asia it's at 6.4% (7% with ChromeOS) vs 9.7% for macOS. In the US, ChromeOS is third at 5.5%, followed by (desktop) Linux at 4.3%. The most numerous type of device with an operating system are embedded systems. Not all embedded systems have operating systems, instead running their application code on the "bare metal"; of those that do have operating systems, a high percentage are standalone or do not have a web browser, which makes their usage share difficult to measure. Some operating systems used in embedded systems are more widely used than some of those mentioned above; for example, modern Intel microprocessors contain an embedded management processor running a version of the Minix operating system. == Worldwide device shipments == Shipments (to stores) do not necessarily translate to sales to consumers, therefore suggesting the numbers indicate popularity and/or usage could be misleading. Not only do smartphones sell in higher numbers than PCs, but also a lot more by dollar value, with the gap only projected to widen, to well over double. According to Gartner, the following is the worldwide device shipments (referring to wholesale) by operating system from 2012 to 2016, which includes smartphones, tablets, laptops and PCs together. On 27 January 2016, Paul Thurrott summarized the operating system market, the day after Apple announced "one billion devices": Apple's "active installed base" is now one billion devices. [..] Granted, some of those Apple devices were probably sold into the marketplace years ago. But that 1 billion figure can and should be compared to the numbers Microsoft touts for Windows 10 (200 million, most recently) or Windows more generally (1.5 billion active users, a number that hasn’t moved, magically, in years), and that Google touts for Android (over 1.4 billion, as of September). My understanding of iOS is that the user base was previously thought to be around 800 million strong, and when you factor out Macs and other non-iOS Apple devices, that's probably about right. But as you can see, there are three big personal computing platforms. And only one of them is actually declining. We’ll see how Windows 10 fares over the long term, but even if Microsoft hits the 1 billion figure in 1-2 years as promised, it will by then still be the smallest of those three platforms. In 2018, Apple stopped revealing unit sales in its reports. Since 2018, the company have been publishing only revenues per device models which, nonetheless, allowed the analysers to extrapolate the unit sales from the model revenues by applying the wholesale device prices. Other hardware manufacturers usually do not report unit sales. === PC shipments === For 2015 (and earlier), Gartner reports for "the year, worldwide PC shipments declined for the fourth consecutive year, which started in 2012 with the launch of tablets" with an 8% decline in PC sales for 2015 (not including cumulative decline in sales over the previous years). Microsoft backed away from their goal of one billion Windows 10 devices in three years (or "by the middle of 2018") and reported on 26 September 2016 that Windows 10 was running on over 400 million devices, and in March 2019, on more than 800 million. In May 2020, Gartner predicted further decline in all market segments for 2020 due to COVID-19, predicting a decline of 13.6% for all devices. while the "Work from Home Trend Saved PC Market from Collapse", with only a decline of 10.5% predicted for PCs. However, in the end, according to Gartner, PC shipments grew 10.7% in the fourth quarter of 2020 and reached 275 million units in 2020, a 4.8% increase from 2019 and the highest growth in ten years." Apple in 4th place for PCs had the largest growth in shipments for a company in Q4 of 31.3%, while "the fourth quarter of 2020 was another remarkable period of growth for Chromebooks, with shipments increasing around 200% year over year to reach 11.7 million units. In 2020, Chromebook shipments increased over 80% to total nearly 30 million units, largely due to demand from the North American education market." Chromebooks sold more (30 million) than Apple's Macs worldwide (22.5 million) in pandemic year 2020. According to the Catalyst group, the year 2021 had record high PC shipments with total shipments of 341 million units (including Chromebooks), 15% higher than 2020 and 27% higher than 2019, while being the largest shipment total since 2012. According to Gartner, worldwide PC shipments declined by 16.2% in 2022, the largest annual decrease since the mid-1990s, due to geopolitical, economic, and supply chain challenges. In 2024 and 2025, due to lower adoption of Windows 11 and Microsoft ending its support to Windows 10, the number of PCs shipped with pre-installed Windows OS dropped. Pundits attribute the low Windows 11 acceptance to its steep hardware requirements and especially the TPM 2.0 ready chipset requirement and the 2024 CrowdStrike-related IT outages. Meanwhile, the macOS device market share in PC device shipments increased to new heights, with improved numbers seen for Linux devices too. In Q3 2025, the macOS pre-installed device shipments increased by 14.9% year-over-year (YoY), while the overall PC-shipments increased only by 8.1%, in Q2 2025, it grew 21.4% YoY while the global PC-shipments increased only by 6.5%, and in Q1 2025, it grew 7% YoY while the global PC-shipments increased by 4.8%. === Tablet computers shipments === In 2015, eMarketer estimated at the beginning of the year that the tablet installed base would hit one billion for the first time (with China's use at 328 million, which Google Play doesn't serve or track, and the United States's use second at 156 million). At the end of the year, because of cheap tablets – not counted by all analysts – that goal was met (even excluding cumulative sales of previous years) as: Sales quintupled to an expected 1 billion units worldwide this year, from 216 million units in 2014, according to projections from the Envisioneering Group. While that number is far higher than the 200-plus million units globally projected by research firms IDC, Gartner and Forrester, Envisioneering analyst Richard Doherty says the rival estimates miss all the cheap Asian knockoff tablets that have been churning off assembly lines.[..] Forrester says its definition of tablets "is relatively narrow" while IDC says it includes some tablets by Amazon — but not all.[..] The top tech purchase of the year continued to be the smartphone, with an expected 1.5 billion sold worldwide, according to projections from researcher IDC. Last year saw some 1.2 billion sold.[..] Computers didn’t fare as well, despite the introduction of Microsoft's latest software upgrade, Windows 10, and the expected but not realized bump it would provide for consumers looking to skip the upgrade and just get a new computer instead. Some 281 million PCs were expected to be sold, according to IDC, down from 308 million in 2014. Folks tend to be happy with the older computers and keep them for longer, as more of our daily computing activities have moved to the smartphone.[..] While Windows 10 got good reviews from tech critics, only 11% of the 1-billion-plus Windows user base opted to do the upgrade, according to Microsoft. This suggests Microsoft has a ways to go before the software gets "hit" status. Apple's new operating system El Capitan has been

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  Speech segmentation

  Speech segmentation is the process of identifying the boundaries between words, syllables, or phonemes in spoken natural languages. The term applies both to the mental processes used by humans, and to artificial processes of natural language processing. In the field of automatic pronunciation assessment, the process of segmenting an utterance against expected word(s) is called forced alignment. Speech segmentation is a subfield of general speech perception and an important subproblem of the technologically focused field of speech recognition, and cannot be adequately solved in isolation. As in most natural language processing problems, one must take into account context, grammar, and semantics, and even so the result is often a probabilistic division (statistically based on likelihood) rather than a categorical one. Though it seems that coarticulation—a phenomenon which may happen between adjacent words just as easily as within a single word—presents the main challenge in speech segmentation across languages, some other problems and strategies employed in solving those problems can be seen in the following sections. This problem overlaps to some extent with the problem of text segmentation that occurs in some languages which are traditionally written without inter-word spaces, like Chinese and Japanese, compared to writing systems which indicate speech segmentation between words by a word divider, such as the space. However, even for those languages, text segmentation is often much easier than speech segmentation, because the written language usually has little interference between adjacent words, and often contains additional clues not present in speech (such as the use of Chinese characters for word stems in Japanese). == Lexical recognition == In natural languages, the meaning of a complex spoken sentence can be understood by decomposing it into smaller lexical segments (roughly, the words of the language), associating a meaning to each segment, and combining those meanings according to the grammar rules of the language. Though lexical recognition is not thought to be used by infants in their first year, due to their highly limited vocabularies, it is one of the major processes involved in speech segmentation for adults. Three main models of lexical recognition exist in current research: first, whole-word access, which argues that words have a whole-word representation in the lexicon; second, decomposition, which argues that morphologically complex words are broken down into their morphemes (roots, stems, inflections, etc.) and then interpreted and; third, the view that whole-word and decomposition models are both used, but that the whole-word model provides some computational advantages and is therefore dominant in lexical recognition. To give an example, in a whole-word model, the word "cats" might be stored and searched for by letter, first "c", then "ca", "cat", and finally "cats". The same word, in a decompositional model, would likely be stored under the root word "cat" and could be searched for after removing the "s" suffix. "Falling", similarly, would be stored as "fall" and suffixed with the "ing" inflection. Though proponents of the decompositional model recognize that a morpheme-by-morpheme analysis may require significantly more computation, they argue that the unpacking of morphological information is necessary for other processes (such as syntactic structure) which may occur parallel to lexical searches. As a whole, research into systems of human lexical recognition is limited due to little experimental evidence that fully discriminates between the three main models. In any case, lexical recognition likely contributes significantly to speech segmentation through the contextual clues it provides, given that it is a heavily probabilistic system—based on the statistical likelihood of certain words or constituents occurring together. For example, one can imagine a situation where a person might say "I bought my dog at a ____ shop" and the missing word's vowel is pronounced as in "net", "sweat", or "pet". While the probability of "netshop" is extremely low, since "netshop" isn't currently a compound or phrase in English, and "sweatshop" also seems contextually improbable, "pet shop" is a good fit because it is a common phrase and is also related to the word "dog". Moreover, an utterance can have different meanings depending on how it is split into words. A popular example, often quoted in the field, is the phrase "How to wreck a nice beach", which sounds very similar to "How to recognize speech". As this example shows, proper lexical segmentation depends on context and semantics which draws on the whole of human knowledge and experience, and would thus require advanced pattern recognition and artificial intelligence technologies to be implemented on a computer. Lexical recognition is of particular value in the field of computer speech recognition, since the ability to build and search a network of semantically connected ideas would greatly increase the effectiveness of speech-recognition software. Statistical models can be used to segment and align recorded speech to words or phones. Applications include automatic lip-synch timing for cartoon animation, follow-the-bouncing-ball video sub-titling, and linguistic research. Automatic segmentation and alignment software is commercially available. == Phonotactic cues == For most spoken languages, the boundaries between lexical units are difficult to identify; phonotactics are one answer to this issue. One might expect that the inter-word spaces used by many written languages like English or Spanish would correspond to pauses in their spoken version, but that is true only in very slow speech, when the speaker deliberately inserts those pauses. In normal speech, one typically finds many consecutive words being said with no pauses between them, and often the final sounds of one word blend smoothly or fuse with the initial sounds of the next word. The notion that speech is produced like writing, as a sequence of distinct vowels and consonants, may be a relic of alphabetic heritage for some language communities. In fact, the way vowels are produced depends on the surrounding consonants just as consonants are affected by surrounding vowels; this is called coarticulation. For example, in the word "kit", the [k] is farther forward than when we say 'caught'. But also, the vowel in "kick" is phonetically different from the vowel in "kit", though we normally do not hear this. In addition, there are language-specific changes which occur in casual speech which makes it quite different from spelling. For example, in English, the phrase "hit you" could often be more appropriately spelled "hitcha". From a decompositional perspective, in many cases, phonotactics play a part in letting speakers know where to draw word boundaries. In English, the word "strawberry" is perceived by speakers as consisting (phonetically) of two parts: "straw" and "berry". Other interpretations such as "stra" and "wberry" are inhibited by English phonotactics, which does not allow the cluster "wb" word-initially. Other such examples are "day/dream" and "mile/stone" which are unlikely to be interpreted as "da/ydream" or "mil/estone" due to the phonotactic probability or improbability of certain clusters. The sentence "Five women left", which could be phonetically transcribed as [faɪvwɪmɘnlɛft], is marked since neither /vw/ in /faɪvwɪmɘn/ nor /nl/ in /wɪmɘnlɛft/ are allowed as syllable onsets or codas in English phonotactics. These phonotactic cues often allow speakers to easily distinguish the boundaries in words. Vowel harmony in languages like Finnish can also serve to provide phonotactic cues. While the system does not allow front vowels and back vowels to exist together within one morpheme, compounds allow two morphemes to maintain their own vowel harmony while coexisting in a word. Therefore, in compounds such as "selkä/ongelma" ('back problem') where vowel harmony is distinct between two constituents in a compound, the boundary will be wherever the switch in harmony takes place—between the "ä" and the "ö" in this case. Still, there are instances where phonotactics may not aid in segmentation. Words with unclear clusters or uncontrasted vowel harmony as in "opinto/uudistus" ('student reform') do not offer phonotactic clues as to how they are segmented. From the perspective of the whole-word model, however, these words are thought be stored as full words, so the constituent parts would not necessarily be relevant to lexical recognition. == In infants and non-natives == Infants are one major focus of research in speech segmentation. Since infants have not yet acquired a lexicon capable of providing extensive contextual clues or probability-based word searches within their first year, as mentioned above, they must often rely primarily upon phonotactic and rhythmic cues (with prosody being the dominant cue), all

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  Quality of experience

  Quality of experience (QoE) is a measure of the delight or annoyance of a customer's experiences with a service (e.g., web browsing, phone call, TV broadcast). QoE focuses on the entire service experience; it is a holistic concept, similar to the field of user experience, but with its roots in telecommunication. QoE is an emerging multidisciplinary field based on social psychology, cognitive science, economics, and engineering science, focused on understanding overall human quality requirements. == Definition and concepts == In 2013, within the context of the COST Action QUALINET, QoE has been defined as:The degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and / or enjoyment of the application or service in the light of the user’s personality and current state.This definition has been adopted in 2016 by the International Telecommunication Union in Recommendation ITU-T P.10/G.100. Before, various definitions of QoE had existed in the domain, with the above-mentioned definition now finding wide acceptance in the community. QoE has historically emerged from Quality of Service (QoS), which attempts to objectively measure service parameters (such as packet loss rates or average throughput). QoS measurement is most of the time not related to a customer, but to the media or network itself. QoE however is a purely subjective measure from the user's perspective of the overall quality of the service provided, by capturing people's aesthetic and hedonic needs. QoE looks at a vendor's or purveyor's offering from the standpoint of the customer or end user, and asks, "What mix of goods, services, and support, do you think will provide you with the perception that the total product is providing you with the experience you desired and/or expected?" It then asks, "Is this what the vendor/purveyor has actually provided?" If not, "What changes need to be made to enhance your total experience?" In short, QoE provides an assessment of human expectations, feelings, perceptions, cognition and satisfaction with respect to a particular product, service or application. QoE is a blueprint of all human subjective and objective quality needs and experiences arising from the interaction of a person with technology and with business entities in a particular context. Although QoE is perceived as subjective, it is an important measure that counts for customers of a service. Being able to measure it in a controlled manner helps operators understand what may be wrong with their services and how to improve them. == QoE factors == QoE aims at taking into consideration every factor that contributes to a user's perceived quality of a system or service. This includes system, human and contextual factors. The following so-called "influence factors" have been identified and classified by Reiter et al.: Human Influence Factors Low-level processing (visual and auditory acuity, gender, age, mood, …) Higher-level processing (cognitive processes, socio-cultural and economic background, expectations, needs and goals, other personality traits…) System Influence Factors Content-related Media-related (encoding, resolution, sample rate, …) Network-related (bandwidth, delay, jitter, …) Device-related (screen resolution, display size, …) Context Influence Factors Physical context (location and space) Temporal context (time of day, frequency of use, …) Social context (inter-personal relations during experience) Economic context Task context (multitasking, interruptions, task type) Technical and information context (relationship between systems) Studies in the field of QoE have typically focused on system factors, primarily due to its origin in the QoS and network engineering domains. Through the use of dedicated test laboratories, the context is often sought to be kept constant. == QoE versus User Experience == QoE is strongly related to but different from the field of User Experience (UX), which also focuses on users' experiences with services. Historically, QoE has emerged from telecommunication research, while UX has its roots in Human–Computer Interaction. Both fields can be considered multi-disciplinary. In contrast to UX, the goal of improving QoE for users was more strongly motivated by economic needs. Wechsung and De Moor identify the following key differences between the fields: == QoE measurement == As a measure of the end-to-end performance at the service level from the user's perspective, QoE is an important metric for the design of systems and engineering processes. This is particularly relevant for video services because – due to their high traffic demands –, bad network performance may highly affect the user's experience. So, when designing systems, the expected output, i.e. the expected QoE, is often taken into account – also as a system output metric and optimization goal. To measure this level of QoE, human ratings can be used. The mean opinion score (MOS) is a widely used measure for assessing the quality of media signals. It is a limited form of QoE measurement, relating to a specific media type, in a controlled environment and without explicitly taking into account user expectations. The MOS as an indicator of experienced quality has been used for audio and speech communication, as well as for the assessment of quality of Internet video, television and other multimedia signals, and web browsing. Due to inherent limitations in measuring QoE in a single scalar value, the usefulness of the MOS is often debated. Subjective quality evaluation requires a lot of human resources, establishing it as a time-consuming process. Objective evaluation methods can provide quality results faster, but require dedicated computing resources. Since such instrumental video quality algorithms are often developed based on a limited set of subjective data, their QoE prediction accuracy may be low when compared to human ratings. QoE metrics are often measured at the end devices and can conceptually be seen as the remaining quality after the distortion introduced during the preparation of the content and the delivery through the network, until it reaches the decoder at the end device. There are several elements in the media preparation and delivery chain, and some of them may introduce distortion. This causes degradation of the content, and several elements in this chain can be considered as "QoE-relevant" for the offered services. The causes of degradation are applicable for any multimedia service, that is, not exclusive to video or speech. Typical degradations occur at the encoding system (compression degradation), transport network, access network (e.g., packet loss or packet delay), home network (e.g. WiFi performance) and end device (e.g. decoding performance). == QoE management == Several QoE-centric network management and bandwidth management solutions have been proposed, which aim to improve the QoE delivered to the end-users. When managing a network, QoE fairness may be taken into account in order to keep the users sufficiently satisfied (i.e., high QoE) in a fair manner. From a QoE perspective, network resources and multimedia services should be managed in order to guarantee specific QoE levels instead of classical QoS parameters, which are unable to reflect the actual delivered QoE. A pure QoE-centric management is challenged by the nature of the Internet itself, as the Internet protocols and architecture were not originally designed to support today's complex and high demanding multimedia services. As an example for an implementation of QoE management, network nodes can become QoE-aware by estimating the status of the multimedia service as perceived by the end-users. This information can then be used to improve the delivery of the multimedia service over the network and proactively improve the users' QoE. This can be achieved, for example, via traffic shaping. QoE management gives the service provider and network operator the capability to minimize storage and network resources by allocating only the resources that are sufficient to maintain a specific level of user satisfaction. As it may involve limiting resources for some users or services in order to increase the overall network performance and QoE, the practice of QoE management requires that net neutrality regulations are considered.

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  G7 Rapid Response Mechanism

  The G7 Rapid Response Mechanism (RRM) is an initiative introduced in the "Charlevoix Commitment on Defending Democracy from Foreign Threats", issued by the leaders of the Group of Seven (G7) countries—United States, Canada, Japan, United Kingdom, France, Germany and Italy—on June 9, 2018, during their summit in Charlevoix, Quebec. The RRM's mandate is to strengthen the coordination of G7 member countries, as well as "to identify and respond to diverse and evolving threats to our democracies, including through sharing information and analysis, and identifying opportunities for coordinated response" The G7 is an informal international intergovernmental economic organization that meets annually, whose members represent the seven wealthiest advanced economies in the world, as measured by the International Monetary Fund (IMF). == Constituents == The following countries and organisations are members and observers (associate members) of the G7 Rapid Response Mechanism: Australia Canada France Germany Italy Japan Netherlands New Zealand Poland Sweden United Kingdom United States European Union North Atlantic Treaty Organization == Mandate == The RRM was mandated to "strengthen coordination to prevent, thwart and respond to malign and evolving threats to G7 democracies." It "will share information and threat analysis related to various threats to democracy, and is an established mechanism to identify opportunities for coordinated response." According to the Institute for Research on Public Policy's Policy Options magazine, the "RRM initiative seeks to strengthen the leading democracies' coordination to identify and respond to diverse and evolving threats…including through sharing information and analysis, and identifying opportunities for a coordinated response." == Administration == The RRM initiative is led by Canada through Global Affairs Canada's Centre for International Digital Policy. Tara Denham, Director of the Centre for International Digital Policy at Global Affairs Canada, directed the team responsible for setting up the RRM Coordination Unit. Global Affairs Canada—the Department of Foreign Affairs, Trade and Development—is the federal Canadian ministry responsible for diplomatic and consular relations, international trade, and international development and humanitarian assistance. The Centre for International Digital Policy includes the Digital Inclusion Lab and the RRM. Denham is also the RRM's Canadian Focal Point. At a briefing on "the security and intelligence threats to elections" of the House of Commons Standing Committee on Access to Information, Privacy and Ethics, the chair Bob Zimmer (CPC), said that the 2019 general election "may be different" from past elections in Canada. as the "tools that were used to strengthen civic engagement are being used to undermine, disrupt and destabilize democracy." "Democracies around the world have entered a new era—an era of heightened threat and heightened vigilance—and 2019 will see a number of countries brace for volleys of attempted disruption: India, Australia, Ukraine, Switzerland, Belgium, the EU and, of course, Canada. Evidence has confirmed that the most recent Canadian general election, in 2015, was unencumbered by interference, although there were some relatively primitive attempts to disrupt, misinform and divide. These efforts were few in number and uncoordinated, and had no visible impact on the voter, either online or in line." Zimmer described the initiative's three pillars. "enhancing citizen preparedness" through the "digital citizen initiative" "improving organizational readiness" with national security and intelligence agencies supporting Elections Canada "ensure a comprehensive understanding of and response to any threats to Canada's democratic process." by establishing the Security and Intelligence Threats to Elections Task Force (SITE) which works as a team with the Communications Security Establishment (CSE), the Canadian Security Intelligence Service (CSIS), the Royal Canadian Mounted Police (RCMP), as well as Global Affairs Canada Zimmer said that as part of the third pillar, "We have activated the G7 rapid response mechanism, announced at the G7 leaders' summit in Charlevoix, to strengthen coordination among our G7 allies and to ensure that there is international collaboration and coordination in responding to foreign threats to democracy." == Background == === Charlevoix summit === The G7 met from June 8 to 9, 2018 during their summit at the Manoir Richelieu in Charlevoix, in La Malbaie, Quebec. The Charlevoix Summit was the 44th G7 summit. The group issued eight "Commitments" at the summit. They included: Commitment on Defending Democracy from Foreign Threats Commitment on Equality and Economic Growth Commitment to End Sexual and Gender-Based Violence, Abuse and Harassment in Digital Contexts Declaration on Quality Education for Girls, Adolescent Girls and Women in Developing Countries Commitment on Innovative Financing for Development. Prime Minister Justin Trudeau announced five themes for Canada's G7 presidency which began in January 2018. === Defending Democracy from Foreign Threats === "We commit to take concerted action in responding to foreign actors who seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security as outlined in the Charlevoix Commitment on Defending Democracy from Foreign Threats. We recognize that such threats, particularly those originating from state actors, are not just threats to G7 nations, but to international peace and security and the rules-based international order. We call on others to join us in addressing these growing threats by increasing the resilience and security of our institutions, economies and societies, and by taking concerted action to identify and hold to account those who would do us harm." They committed to "cooperate in defending democracies from foreign threats and establish a response mechanism for that purpose". "Democracy and the rules-based international order are increasingly being challenged by authoritarianism and the defiance of international norms. In particular, foreign actors seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security. These malicious, multi-faceted and ever-evolving tactics constitute a serious strategic threat which we commit to confront together, working with other governments that share our democratic values." The Charlevoix Commitment states that "foreign actors seek to undermine our democratic societies and institutions, our electoral processes, our sovereignty and our security. These malicious, multi-faceted and ever-evolving tactics constitute a serious strategic threat which we commit to confront together, working together with other governments that share our democratic values." The Charlevoix Summit resolved to "establish a G7 Rapid Response Mechanism to strengthen our coordination to identify and respond to diverse and evolving threats to our democracies, including through sharing information and analysis, and identifying opportunities for coordinated response." == Monitored elections == === 2019 European Parliament election === RRM Canada's comprehensive report on the 2019 European Parliament election analyzed open data "related to foreign interference during and leading up to the 2019 European Union Parliamentary Elections, May 23–26, 2019". RRM Canada did not find "significant evidence of state-based foreign interference, or any large-scale, organized and coordinated efforts by non-state actors". They did find that "national or international non-state actors" used tactics based on those used by the Russian sponsored Internet Research Agency (IRA) in previous elections, "such as the 2016 U.S. Elections". For example, blogs, webpages, and social media accounts on Twitter, Facebook and Reddit "were used to spread divisive and false information to damage and negatively impact social cohesion and trust in democratic processes and institutions" in coordinated networks of Facebook groups. === 2019 Alberta general election === RRM Canada's analyz report on the 2019 Alberta general election was intended to "identify any emerging tactics in foreign interference and draw lessons learned for the Canadian general elections scheduled to take place in October 2019." No foreign activity was detected, although the data revealed ""suspicious account creation pattern that is indicative of troll or bot activity". They found "automated inauthentic behaviour and trolling activities" but concluded that they were "very likely domestic". The data showed "suspicious account creation pattern that is indicative of troll or bot activity", and "spikes in account creation" which suggested the "presence of accounts developed for a specific purpose." The accounts were very likely domestic and were "mainly comprised of supporters of the United Conservative Party (UCP)." A seco

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  Creepy treehouse

  Creepy treehouse is a social media term, or internet slang, referring to websites or technologies that are used for educational purposes but regarded by students as an invasion of privacy. == History == The term was first described in 2008 by Utah Valley University instructional-design services director Jared Stein as "institutionally controlled technology/tool that emulates or mimics pre-existing [sic] technologies or tools that may already be in use by the learners, or by learners' peer groups." This was when social media such as Facebook was starting to become mainstream and professors would try and get students to interact with them on the site for educational purposes. Some professors would require their students to use Facebook or Twitter as part of class assignments. == Usage == The term was first described as "technological innovations by faculty members that make students’ skin crawl." The term also refers to online accounts and websites that users tend to avoid, especially young people who avoid visiting the pages of educators and other adults. Author Martin Weller defines creepy treehouse as a digital space where authority figures are viewed as invading younger people's privacy. One such example is a professor giving his students an option to use a popular video game to learn about history instead of writing an essay. Students in that class chose to write the essay instead as the method was previously unmentioned and it was not an unnatural method of interaction. Another example given was Blackboard Sync, a feature that was used to connect the school website Blackboard with students' Facebook accounts. == Solutions == University of Regina professor Alec Couros suggests that instead of "forcing" student participation with their own digital platforms, professors should use methods like online forums. Jason Jones of chronicle.com suggested letting students create social media groups for the class themselves and explaining why using technologies is required and important.

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  Distinguishable interfaces

  Distinguishable interfaces use computer graphic principles to automatically generate easily distinguishable appearance for computer data. Although the desktop metaphor revolutionized user interfaces, there is evidence that a spatial layout alone does little to help in locating files and other data; distinguishable appearance is also required. Studies have shown that average users have considerable difficulty finding files on their personal computers, even ones that they created the same day. Search engines do not always help, since it has been found that users often know of the existence of a file without being able to specify relevant search terms. On the contrary, people appear to incrementally search for files using some form of context. Recently researchers and web developers have argued that the problem is the lack of distinguishable appearance: in the traditional computer interface most objects and locations appear identical. This problem rarely occurs in the real world, where both objects and locations generally have easily distinguishable appearance. Discriminability was one of the recommendations in the ISO 9241-12 recommendation on presentation of information on visual displays (part of the overall report on Ergonomics of Human System Interaction), however it was assumed in that report that this would be achieved by manual design of graphical symbols. == VisualIDs, semanticons, and identicons == The mass availability of computer graphics supported the introduction of approaches that make better use of the brain's "visual hardware", by providing individual files and other abstract data with distinguishable appearance. This idea initially appeared in strictly academic VisualIDs and Semanticons works, but the web community has explored and rapidly adopted similar ideas, such as the Identicon. The VisualIDs project automatically generated icons for files or other data based on a hash of the data identifier, so the icons had no relation to the content or meaning of the data. It was argued not only that generating meaningful icons is unnecessary (their user study showed rapid learning of the arbitrary icons), but also that basing icons on content is actually incorrect ("contrasting visualization with visual identifiers"). The Semanticons project developed by Setlur et al. demonstrated an algorithm to create icons that reflect the content of files. In this work the name, location and content of a file are parsed and used to retrieve related image(s) from an image database. These are then processed using a Non-photorealistic rendering technique in order to generate graphical icons. Developer Don Park introduced the identicon library for making a visual icon from a hash of a data identifier. This initial public implementation has spawned a large number of implementations for various environments. In particular, identicons are now being used as default visual user identifiers (avatars) for several widely used systems. They are also used as a complement to Gravatars, which are pre-existing avatar images created or chosen by users, instead of automatically generated images. (see #External links). == Current research == While current web practice has followed the semantics-free approach of VisualIDs, recent research has followed the semantics-based approach of Semanticons. Examples include using data mining principles to automatically create "intelligent icons" that reflect the contents of files and creating icons for music files that reflect audio characteristics or affective content.

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  Webedia

  Webedia S.A. is a company specializing in online media, a subsidiary of the Fimalac group based in Levallois-Perret, France. Webedia is active in more than twenty countries including France (AlloCiné, Jeuxvideo.com, MGG, Puremédias, Ode, Pureshopping, Volum, Terrafemina, 750g, easyVoyage, l’Automobile Magazine, Le 10 Sport), Brazil (AdoroCinema, Tudo Gostoso, Minhavida), Germany (Filmstarts, Moviepilot, GameStar), Spain and Latin America (Xataka, SensaCine, Raiser Games), Poland (Gry-Online and GetHero) and the United States (Boxoffice Pro). == History == === Early years (2007-2013) === Webedia was created in France in 2007, following the successive launches of the websites Purepeople, Puretrend and Purefans. Webedia bought the comparison shopping website Shopoon in 2008 and renamed it Pureshopping, and the website Ozap (media news) from M6 group in 2011 and renamed it Puremédias. Webedia was acquired by Fimalac in May 2013 and became its Internet media subsidiary. === Growth (2013-2016) === In 2013, Fimalac acquired AlloCiné, the websites Newsring and Youmag, the cooking website 750g and the cultural platform Exponaute. In 2014, Webedia acquired OverBlog, Jeuxvideo.com (through L'Odyssée Interactive and moved to Paris in 2015), Moviepilot (Germany), and Gameo Consulting (owner of Millenium, electronic sports), In December 2014, Webedia announced a license agreement with Ziff Davis to launch sites under the IGN franchise in Brazil and France at the beginning of 2015. The French version of IGN was launched on 2, it targets the general public and casual gamers. In 2015, Webedia acquired Côté Ciné Group (technological solutions for movie theaters and specialized press magazines: BoxOffice Pro in the United States and Côté Ciné in France), 57% of Easyvoyage group (online travel comparators Easyvol and Alibabuy, Mixicom (website JeuxActu and multi-channel network), 50% of the Brazilian network Paramaker, and West World Media (digital marketing company for the film industry). In 2016, Webedia bought Scimob (mobile video game studio), Surprizemi (home-delivered surprise boxes), Eklablog (blogging platform) Oxent (eSports World Convention), and Bang Bang Management (sports PR agency). In addition, an agreement is made with Paris Saint-Germain for Webedia to recruit and manage e-sports players on behalf of Paris Saint-Germain eSports. On November 15, 2016, the LFP announced that it had reached an agreement with beIN Sports and Webedia for the broadcasting of the first edition of the e-League 1. The competition is renewed for two additional seasons on July 26, 2017, the broadcasting agreements are renewed. On December 8, 2016, Webedia joined forces with Chronopost to launch Pourdebon, a home delivery service that connects Internet users and labeled producers (AOC, organic AB, etc.). Webedia has a slight majority (53%) in this new platform. === 2017 === On January 19, 2017, Webedia announced the acquisition of the English company Peach Digital, specializing in web development and digital marketing for movie theaters. In February 2017, Le Figaro announced that Webedia had invested 10 million euros in Illico Fresco, a home delivery service for baskets of recipes. The same month, FDJ and Webedia announced a partnership for the creation of eSports competitions: a professional one (FDJ Masters League) and another one for amateur gamers (FDJ Open Series) starting in March 2017. They are broadcast on Webedia's Web TV. At the end of February 2017, the media group finalized the acquisition of MyPoseo, a SaaS publisher specialized on SEO analytics. On March 8, 2017, Webedia launched LeStream, a Twitch Web TV dedicated to video games, the result of two years of development, in the company of several YouTubers including Cyprien and Squeezie,. On March 29, 2017, Webedia bought the Brazilian web publisher Minha Vida, a website devoted to health, nutrition, beauty and fitness, which attracts 14.3 million unique monthly visitors. Webedia reaches 44 million unique visitors in Brazil, and thus becomes the leading publisher on entertainment themes. In June 2017, the company made its largest international acquisition, with the American agency 3BlackDot, a media and marketing agency focused on videogamers. The agency, based in Los Angeles, manages 36 YouTubers followed by millions of subscribers on their channels which total 700 million videos viewed per month. In July 2017, Webedia bought IDZ, an audiovisual production company, and thus strengthened its production activities and its leadership on the YouTube channel networks in France. That year, Webedia was the first French media group to use the measurement of their global audiences by Comscore. It represents deduplicated coverage on desktops, laptops, smartphones and tablets, and includes audiences for websites, mobile applications and videos. This new measure allows Webedia to establish a deduplicated global audience of 177 million unique visitors in April 2017. In October 2017, Webedia announced its intention to launch a TV channel dedicated to electronic sports, called ES1. The channel was officially launched on January 10, 2018, on Orange TV and on February 6, 2018, on Free and Bouygues Telecom. In November 2017, Webedia, with the support of CDC International Capital, entered into exclusive negotiations with the Saudi company Uturn Entertainment, specializing in online entertainment, particularly on YouTube, and the production of digital content for the region's youth, with a view to merging it with Diwanee, a Webedia subsidiary in the Middle East, for an amount close to $100 million. In December 2017, Webedia acquired a majority stake in the United States–based company called Creators Media, which brings together social and video production platforms specializing in popular culture and entertainment. That same month, Webedia joined forces with Elephant, Emmanuel Chain's audiovisual production company, to create a new content production label aimed at Millennials. === 2018-2019 === In January 2018, Webedia launched a sports marketing agency: Only Sports & Passions. That same month, Illico Fresco, specialist in the delivery of kit meals belonging to Webedia, joined forces with Weight Watchers, the world leader in slimming products. In April 2018, Webedia published new audience figures in partnership with Comscore, 188 million unique monthly visitors in December 2017, an increase of 6.2% compared to the previous measure dating from April 2017. The same month, Webedia unveils its ambitions concerning content production, as a partnership with the video game studio Focus Home Interactive is signed with a title "Fear the Wolves" already planned for 2018, co-production projects of films, cartoons or series are announced. In July 2018, Webedia bought the American authors company Full Fathom Five, a company that helps authors produce books, TV series, films and video games. In October 2018, Webedia announced that it was focusing on both esports clubs PSG Esports and LeStream Esport. The first one being geared towards international competitions and the second devoted mainly to the French esports scene. The "Millenium" brand is thus refocusing around its media activities and esports merchandising products, and the "Millenium esport club" being gradually closed. The same month, the company announced the acquisition of Weblogs, a Spanish-speaking website publisher, thereby strengthening its activity in Spain and Latin America. On October 22, 2018, Webedia announced the merger of BoxOffice magazine with Film Journal International. On November 13, 2018, Groupe SEB announced the acquisition from Webedia of 750g International, the international branch of the French recipe site 750g (the original French website 750g.com being retained by Webedia). The group is thus separating from Gourmandize (United States and United Kingdom), HeimGourmet (Germany), Rebañando (Spain), Receitas Sem Fronteiras (Brazil / Portugal) and Tribù Golosa (Italy). The same month, Webedia joined forces with Riot Games to launch the French League of League of Legends (LFL), the first French professional league on the League of Legends game, which will bring together the 8 best teams on the French scene. In March 2019, Webedia bought 51% of the audiovisual production company Elephant. The new set will weigh 500 million euros, a quarter of which will be made outside France. The same month, Webedia purchased a majority stake in the company Partoo, which publishes a SaaS platform specializing in local marketing for brands and merchants. On March 14, 2019, a new measurement of the international audience of Webedia sites was produced by Comscore, posting 250 million unique visitors in December 2018, up 9.2% compared to December 2017. In June 2019, the group joined forces with Michel Cymes, a famous doctor and French TV host by taking a majority stake in his company Club Santé Débat, in order to develop a health platform around the Dr. Good! Brand. In Sep

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  Bootstrap (front-end framework)

  Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML, CSS and (optionally) JavaScript-based design templates for typography, forms, buttons, navigation, and other interface components. As of May 2023, Bootstrap is the 17th most starred project (4th most starred library) on GitHub, with over 164,000 stars. According to W3Techs, Bootstrap is used by 19.2% of all websites. == Features == Bootstrap is an HTML, CSS and JS library that focuses on simplifying the development of informative web pages (as opposed to web applications). The primary purpose of adding it to a web project is to apply Bootstrap's choices of color, size, font and layout to that project. As such, the primary factor is whether the developers in charge find those choices to their liking. Once added to a project, Bootstrap provides basic style definitions for all HTML elements. The result is a uniform appearance for prose, tables and form elements across web browsers. In addition, developers can take advantage of CSS classes defined in Bootstrap to further customize the appearance of their contents. For example, Bootstrap has provisioned for light- and dark-colored tables, page headings, more prominent pull quotes, and text with a highlight. Bootstrap also comes with several JavaScript components which do not require other libraries like jQuery. They provide additional user interface elements such as dialog boxes, tooltips, progress bars, navigation drop-downs, and carousels. Each Bootstrap component consists of an HTML structure, CSS declarations, and in some cases accompanying JavaScript code. They also extend the functionality of some existing interface elements, including for example an auto-complete function for input fields. The most prominent components of Bootstrap are its layout components, as they affect an entire web page. The basic layout component is called "Container", as every other element in the page is placed in it. Developers can choose between a fixed-width container and a fluid-width container. While the latter always fills the width with the web page, the former uses one of the five predefined fixed widths, depending on the size of the screen showing the page: Smaller than 576 pixels 576–768 pixels 768–992 pixels 992–1200 pixels 1200–1400 pixels Larger than 1400 pixels Once a container is in place, other Bootstrap layout components implement a CSS Flexbox layout through defining rows and columns. A precompiled version of Bootstrap is available in the form of one CSS file and three JavaScript files that can be readily added to any project. The raw form of Bootstrap, however, enables developers to implement further customization and size optimizations. This raw form is modular, meaning that the developer can remove unneeded components, apply a theme and modify the uncompiled Sass files. == History == === Early beginnings === Bootstrap, originally named Twitter Blueprint, was developed by Mark Otto and Jacob Thornton at Twitter in 2010 as a framework to encourage consistency across internal tools. Before Bootstrap, various libraries were used for interface development, which led to inconsistencies and a high maintenance burden. According to Otto: A super small group of developers and I got together to design and build a new internal tool and saw an opportunity to do something more. Through that process, we saw ourselves build something much more substantial than another internal tool. Months later, we ended up with an early version of Bootstrap as a way to document and share common design patterns and assets within the company. After a few months of development by a small group, many developers at Twitter began to contribute to the project as a part of Hack Week, a hackathon-style week for the Twitter development team. It was renamed from Twitter Blueprint to Twitter Bootstrap and released as an open-source project on August 19, 2011. It has continued to be maintained by Otto, Thornton, a small group of core developers, and a large community of contributors. === Bootstrap 2 === On January 31, 2012, Bootstrap 2 was released, which added built-in support for Glyphicons, several new components, as well as changes to many of the existing components. This version supports responsive web design, meaning the layout of web pages adjusts dynamically, taking into account the characteristics of the device used (whether desktop, tablet, mobile phone). Shortly before the release of Bootstrap 2.1.2, Otto and Thornton left Twitter, but committed to continue to work on Bootstrap as an independent project. === Bootstrap 3 === On August 19, 2013, Bootstrap 3 was released. It redesigned components to use flat design and a mobile first approach. Bootstrap 3 features new plugin system with namespaced events. Bootstrap 3 dropped Internet Explorer 7 and Firefox 3.6 support, but there is an optional polyfill for these browsers. Bootstrap 3 was also the first version released under the twbs organization on GitHub instead of the Twitter one. === Bootstrap 4 === Otto announced Bootstrap 4 on October 29, 2014. The first alpha version of Bootstrap 4 was released on August 19, 2015. The first beta version was released on August 10, 2017. Otto suspended work on Bootstrap 3 on September 6, 2016, to free up time to work on Bootstrap 4. Bootstrap 4 was finalized on January 18, 2018. Significant changes include: Major rewrite of the code Replacing Less with Sass Addition of Reboot, a collection of element-specific CSS changes in a single file, based on Normalize Dropping support for IE8, IE9, and iOS 6 CSS Flexible Box support Adding navigation customization options Adding responsive spacing and sizing utilities Switching from the pixels unit in CSS to root ems Increasing global font size from 14px to 16px for enhanced readability Dropping the panel, thumbnail, pager, and well components Dropping the Glyphicons icon font Huge number of utility classes Improved form styling, buttons, drop-down menus, media objects and image classes Bootstrap 4 supports the latest versions of Google Chrome, Firefox, Internet Explorer, Opera, and Safari (except on Windows). It additionally supports back to IE10 and the latest Firefox Extended Support Release (ESR). === Bootstrap 5 === Bootstrap 5 was officially released on May 5, 2021. Major changes include: New offcanvas menu component Removing dependence on jQuery in favor of vanilla JavaScript Rewriting the grid to support responsive gutters and columns placed outside of rows Migrating the documentation from Jekyll to Hugo Dropping support for Internet Explorer Moving testing infrastructure from QUnit to Jasmine Adding custom set of SVG icons Adding CSS custom properties Improved API Enhanced grid system Improved customizing docs Updated forms RTL support Built in darkmode support

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  Supercomputer operating system

  A supercomputer operating system is an operating system intended for supercomputers. Since the end of the 20th century, supercomputer operating systems have undergone major transformations, as fundamental changes have occurred in supercomputer architecture. While early operating systems were custom tailored to each supercomputer to gain speed, the trend has been moving away from in-house operating systems and toward some form of Linux, with it running all the supercomputers on the TOP500 list in November 2017. In 2021, top 10 computers run for instance Red Hat Enterprise Linux (RHEL), or some variant of it or other Linux distribution e.g. Ubuntu. Given that modern massively parallel supercomputers typically separate computations from other services by using multiple types of nodes, they usually run different operating systems on different nodes, e.g., using a small and efficient lightweight kernel such as Compute Node Kernel (CNK) or Compute Node Linux (CNL) on compute nodes, but a larger system such as a Linux distribution on server and input/output (I/O) nodes. While in a traditional multi-user computer system job scheduling is in effect a tasking problem for processing and peripheral resources, in a massively parallel system, the job management system needs to manage the allocation of both computational and communication resources, as well as gracefully dealing with inevitable hardware failures when tens of thousands of processors are present. Although most modern supercomputers use the Linux operating system, each manufacturer has made its own specific changes to the Linux distribution they use, and no industry standard exists, partly because the differences in hardware architectures require changes to optimize the operating system to each hardware design. == Context and overview == In the early days of supercomputing, the basic architectural concepts were evolving rapidly, and system software had to follow hardware innovations that usually took rapid turns. In the early systems, operating systems were custom tailored to each supercomputer to gain speed, yet in the rush to develop them, serious software quality challenges surfaced and in many cases the cost and complexity of system software development became as much an issue as that of hardware. In the 1980s the cost for software development at Cray came to equal what they spent on hardware and that trend was partly responsible for a move away from the in-house operating systems to the adaptation of generic software. The first wave in operating system changes came in the mid-1980s, as vendor specific operating systems were abandoned in favor of Unix. Despite early skepticism, this transition proved successful. By the early 1990s, major changes were occurring in supercomputing system software. By this time, the growing use of Unix had begun to change the way system software was viewed. The use of a high level language (C) to implement the operating system, and the reliance on standardized interfaces was in contrast to the assembly language oriented approaches of the past. As hardware vendors adapted Unix to their systems, new and useful features were added to Unix, e.g., fast file systems and tunable process schedulers. However, all the companies that adapted Unix made unique changes to it, rather than collaborating on an industry standard to create "Unix for supercomputers". This was partly because differences in their architectures required these changes to optimize Unix to each architecture. As general purpose operating systems became stable, supercomputers began to borrow and adapt critical system code from them, and relied on the rich set of secondary functions that came with them. However, at the same time the size of the code for general purpose operating systems was growing rapidly. By the time Unix-based code had reached 500,000 lines long, its maintenance and use was a challenge. This resulted in the move to use microkernels which used a minimal set of the operating system functions. Systems such as Mach at Carnegie Mellon University and ChorusOS at INRIA were examples of early microkernels. The separation of the operating system into separate components became necessary as supercomputers developed different types of nodes, e.g., compute nodes versus I/O nodes. Thus modern supercomputers usually run different operating systems on different nodes, e.g., using a small and efficient lightweight kernel such as CNK or CNL on compute nodes, but a larger system such as a Linux-derivative on server and I/O nodes. == Early systems == The CDC 6600, generally considered the first supercomputer in the world, ran the Chippewa Operating System, which was then deployed on various other CDC 6000 series computers. The Chippewa was a rather simple job control oriented system derived from the earlier CDC 3000, but it influenced the later KRONOS and SCOPE systems. The first Cray-1 was delivered to the Los Alamos Lab with no operating system, or any other software. Los Alamos developed the application software for it, and the operating system. The main timesharing system for the Cray 1, the Cray Time Sharing System (CTSS), was then developed at the Livermore Labs as a direct descendant of the Livermore Time Sharing System (LTSS) for the CDC 6600 operating system from twenty years earlier. In developing supercomputers, rising software costs soon became dominant, as evidenced by the 1980s cost for software development at Cray growing to equal their cost for hardware. That trend was partly responsible for a move away from the in-house Cray Operating System to UNICOS system based on Unix. In 1985, the Cray-2 was the first system to ship with the UNICOS operating system. Around the same time, the EOS operating system was developed by ETA Systems for use in their ETA10 supercomputers. Written in Cybil, a Pascal-like language from Control Data Corporation, EOS highlighted the stability problems in developing stable operating systems for supercomputers and eventually a Unix-like system was offered on the same machine. The lessons learned from developing ETA system software included the high level of risk associated with developing a new supercomputer operating system, and the advantages of using Unix with its large extant base of system software libraries. By the middle 1990s, despite the extant investment in older operating systems, the trend was toward the use of Unix-based systems, which also facilitated the use of interactive graphical user interfaces (GUIs) for scientific computing across multiple platforms. The move toward a commodity OS had opponents, who cited the fast pace and focus of Linux development as a major obstacle against adoption. As one author wrote "Linux will likely catch up, but we have large-scale systems now". Nevertheless, that trend continued to gain momentum and by 2005, virtually all supercomputers used some Unix-like OS. These variants of Unix included IBM AIX, the open source Linux system, and other adaptations such as UNICOS from Cray. By the end of the 20th century, Linux was estimated to command the highest share of the supercomputing pie. == Modern approaches == The IBM Blue Gene supercomputer uses the CNK operating system on the compute nodes, but uses a modified Linux-based kernel called I/O Node Kernel (INK) on the I/O nodes. CNK is a lightweight kernel that runs on each node and supports a single application running for a single user on that node. For the sake of efficient operation, the design of CNK was kept simple and minimal, with physical memory being statically mapped and the CNK neither needing nor providing scheduling or context switching. CNK does not even implement file I/O on the compute node, but delegates that to dedicated I/O nodes. However, given that on the Blue Gene multiple compute nodes share a single I/O node, the I/O node operating system does require multi-tasking, hence the selection of the Linux-based operating system. While in traditional multi-user computer systems and early supercomputers, job scheduling was in effect a task scheduling problem for processing and peripheral resources, in a massively parallel system, the job management system needs to manage the allocation of both computational and communication resources. It is essential to tune task scheduling, and the operating system, in different configurations of a supercomputer. A typical parallel job scheduler has a master scheduler which instructs some number of slave schedulers to launch, monitor, and control parallel jobs, and periodically receives reports from them about the status of job progress. Some, but not all supercomputer schedulers attempt to maintain locality of job execution. The PBS Pro scheduler used on the Cray XT3 and Cray XT4 systems does not attempt to optimize locality on its three-dimensional torus interconnect, but simply uses the first available processor. On the other hand, IBM's scheduler on the Blue Gene supercomputers aims to exploit locality a

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  Spatial embedding

  Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types. representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per geographic object to a continuous vector space with a much lower dimension. Such embedding methods allow complex spatial data to be used in neural networks and have been shown to improve performance in spatial analysis tasks == Embedded data types == Geographic data can take many forms: text, images, graphs, trajectories, polygons. Depending on the task, there may be a need to combine multimodal data from different sources. The next section describes examples of different types of data and their uses. === Text === Geolocated posts on social media can be used to acquire a library of documents bound to a given place that can be later transformed to embedded vectors using word embedding techniques. === Image === Satellites and aircraft collect digital spatial data acquired from remotely sensed images which can be used in machine learning. They are sometimes hard to analyse using basic image analysis methods and convolutional neural networks can be used to acquire an embedding of images bound to a given geographical object or a region. === Point === A single point of interest (POI) can be assigned multiple features that can be used in machine learning. These could be demographic, transportation, meteorological, or economic data, for example. When embedding single points, it is common to consider the entire set of available points as nodes in a graph. === Line / multiline === Among other things, motion trajectories are represented as lines (multilines). Individual trajectories are embedded taking into account travel time, distances and also features of points visited along the way. Embedding of trajectories allows to improve performance of such tasks as clustering and also categorization. === Polygon === The geographic areas analyzed in machine learning are defined by both administrative boundaries and top-down division into grids of regular shapes such as rectangles, for example. Both types are represented as polygons and, like points, can be assigned different demographic, transportation, or economic features. A polygon can also have features related to the size of the area or shape it represents. === Graph === An example domain where graph representation is used is the street layout in a city, where vertices can be intersections and edges can be roads. The vertices can also be destination points like public transport stops or important points in the city, and the edges represent the flow between them. Embedding graphs or single vertices allows to improve accuracy of analysis methods in which the treated geographical domain can be represented as a network. == Usage == POI recommendation - generating personalized point of interest recommendations based on user preferences. Next/future location prediction - prediction of the next location a person will go to based on their historical trajectory. Zone functions classification - based on different mobility of people or POI distribution a function of a given area in a city can be predicted. Crime prediction - estimation of crime rate in different regions of a city. Local event detection - studying spatio-temporal changes in embeddings can provide valuable information in detection of local event occurring in specific location. Regional mobility popularity prediction - analysis of mobility can show patterns in popularity of different regions in a city. Shape matching - finding a similar shape of given polygon, for example finding building with the same shape as input building. Travel time estimation - predicting estimated travel time given current traffic conditions and special occurring events. Time estimation for on-demand food delivery - estimation of delivery time when placing an order through the website. == Temporal aspect == Some of the data analyzed has a timestamp associated with it. In some cases of data analysis this information is omitted and in others it is used to divide the set into groups. The most common division is the separation of weekdays from weekends or division into hours of the day. This is particularly important in the analysis of mobility data, because the characteristics of mobility during the week and at different times of the day are very different from each other. Another area in which time division into, for example, individual months can be used is in the analysis of tourism of a given region. In order to take such a split into account, embedding methods treat the time stamp specifically or separate versions of the model are developed for different subgroups of the analyzed set.

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  Microelectronics

  Microelectronics is a subfield of electronics. As the name suggests, microelectronics relates to the study and manufacture (or microfabrication) of very small electronic designs and components. Usually, but not always, this means micrometre-scale or smaller. These devices are typically made from semiconductor materials. Many components of a normal electronic design are available in a microelectronic equivalent. These include transistors, capacitors, inductors, resistors, diodes and (naturally) insulators and conductors can all be found in microelectronic devices. Unique wiring techniques such as wire bonding are also often used in microelectronics because of the unusually small size of the components, leads and pads. This technique requires specialized equipment and is expensive. Digital integrated circuits (ICs) consist of billions of transistors, resistors, diodes, and capacitors. Analog circuits commonly contain resistors and capacitors as well. Inductors are used in some high frequency analog circuits, but tend to occupy larger chip area due to their lower reactance at low frequencies. Gyrators can replace them in many applications. As techniques have improved, the scale of microelectronic components has continued to decrease. At smaller scales, the relative impact of intrinsic circuit properties, such as unintended interactions between components or their parts, may become more significant. These are called parasitic effects, and the goal of the microelectronics design engineer is to find ways to compensate for or to minimize these effects, while delivering smaller, faster, and cheaper devices. Today, microelectronics design is largely aided by electronic design automation (EDA) software.

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  List of operating systems

  This is a list of operating systems. Computer operating systems can be categorized by technology, ownership, licensing, working state, usage, and by many other characteristics. In practice, many of these groupings may overlap. Criteria for inclusion is notability, as shown either through an existing Wikipedia article or citation to a reliable source. == Proprietary == === Acorn Computers === Arthur ARX MOS RISC iX RISC OS === Amazon === Fire OS === Amiga Inc. === AmigaOS AmigaOS 1.0-3.9 (Motorola 68000) AmigaOS 4 (PowerPC) Amiga Unix (a.k.a. Amix) === Amstrad === AMSDOS Contiki CP/M 2.2 CP/M Plus SymbOS === Apple === Apple II Apple DOS Apple Pascal ProDOS GS/OS GNO/ME Contiki Apple III Apple SOS Apple Lisa Mac Classic Mac OS A/UX (UNIX System V with BSD extensions) Copland MkLinux Pink Rhapsody macOS (formerly Mac OS X and OS X) macOS Server (formerly Mac OS X Server and OS X Server) Apple Network Server IBM AIX (Apple-customized) Apple MessagePad Newton OS iPhone and iPod Touch iOS (formerly iPhone OS) iPad iPadOS Apple Watch watchOS Apple TV tvOS Embedded operating systems bridgeOS Apple Vision Pro visionOS Embedded operating systems A/ROSE iPod software (unnamed embedded OS for iPod) Unnamed NetBSD variant for Airport Extreme and Time Capsule === Apollo Computer, Hewlett-Packard === Domain/OS – One of the first network-based systems. Run on Apollo/Domain hardware. Later bought by Hewlett-Packard. === Atari === Atari DOS (for 8-bit computers) Atari TOS Atari MultiTOS Contiki (for 8-bit, ST, Portfolio) === BAE Systems === XTS-400 === Be Inc. === BeOS BeIA BeOS r5.1d0 magnussoft ZETA (based on BeOS r5.1d0 source code, developed by yellowTAB) === Bell Labs === Unix ("Ken's new system," for its creator (Ken Thompson), officially Unics and then Unix, the prototypic operating system created in Bell Labs in 1969 that formed the basis for the Unix family of operating systems) UNIX Time-Sharing System v1 UNIX Time-Sharing System v2 UNIX Time-Sharing System v3 UNIX Time-Sharing System v4 UNIX Time-Sharing System v5 UNIX Time-Sharing System v6 MINI-UNIX PWB/UNIX USG CB Unix UNIX Time-Sharing System v7 (It is from Version 7 Unix (and, to an extent, its descendants listed below) that almost all Unix-based and Unix-like operating systems descend.) Unix System III Unix System IV Unix System V Unix System V Releases 2.0, 3.0, 3.2, 4.0, and 4.2 UNIX Time-Sharing System v8 UNIX Time-Sharing System v9 UNIX Time-Sharing System v10 Non-Unix Operating Systems: BESYS Plan 9 from Bell Labs Inferno === Burroughs Corporation, Unisys === Burroughs MCP === CII === Siris 8 === Commodore International === GEOS AmigaOS AROS Research Operating System === Control Data Corporation === ==== Lower 3000 series ==== SCOPE (Supervisory Control Of Program Execution) ==== Upper 3000 series ==== SCOPE (Supervisory Control Of Program Execution) Drum SCOPE ==== 6x00 and related Cyber ==== Chippewa Operating System (COS) MACE (Mansfield and Cahlander Executive) Kronos (Kronographic OS) NOS (Network Operating System) NOS/VE (NOS Virtual Environment) SCOPE (Supervisory Control Of Program Execution) NOS/BE NOS Batch Environment SIPROS (Simultaneous Processing Operating System) ==== Star-100 ==== Multiple Console Time Sharing System (MCTS), from General Motors Research === CloudMosa === Puffin OS === Convergent Technologies === Convergent Technologies Operating System (CTOS) – later acquired by Unisys === Cromemco === Cromemco DOS (CDOS) – a Disk Operating system compatible with CP/M Cromix – a multitasking, multi-user, Unix-like OS for Cromemco microcomputers with Z80A and/or 68000 CPU === Data General === AOS for 16-bit Data General Eclipse computers and AOS/VS for 32-bit (MV series) Eclipses, MP/AOS for microNOVA-based computers DG/UX RDOS Real-time Disk Operating System, with variants: RTOS and DOS (not related to PC DOS, MS-DOS etc.) === Datapoint === CTOS Cassette Tape Operating System for the Datapoint 2200 DOS Disk Operating System for the Datapoint 2200, 5500, and 1100 === DDC-I, Inc. === Deos – Time & Space Partitioned RTOS, Certified to DO-178B, Level A since 1998 HeartOS – POSIX-based Hard Real-Time Operating System === Digital Research, Inc. === CP/M CP/M CP/M for Intel 8080/8085 and Zilog Z80 Personal CP/M, a refinement of CP/M CP/M Plus with BDOS 3.0 CP/M-68K CP/M for Motorola 68000 CP/M-8000 CP/M for Zilog Z8000 CP/M-86 CP/M for Intel 8088/8086 CP/M-86 Plus Personal CP/M-86 MP/M Multi-user version of CP/M-80 MP/M II MP/M-86 Multi-user version of CP/M-86 MP/M 8-16, a dual-processor variant of MP/M for 8086 and 8080 CPUs. Concurrent CP/M, the successor of CP/M-80 and MP/M-80 Concurrent CP/M-86, the successor of CP/M-86 and MP/M-86 Concurrent CP/M 8-16, a dual-processor variant of Concurrent CP/M for 8086 and 8080 CPUs. Concurrent CP/M-68K, a variant for the 68000 DOS Concurrent DOS, the successor of Concurrent CP/M-86 with PC-MODE Concurrent PC DOS, a Concurrent DOS variant for IBM compatible PCs Concurrent DOS 8-16, a dual-processor variant of Concurrent DOS for 8086 and 8080 CPUs Concurrent DOS 286 Concurrent DOS XM, a real-mode variant of Concurrent DOS with EEMS support Concurrent DOS 386 Concurrent DOS 386/MGE, a Concurrent DOS 386 variant with advanced graphics terminal capabilities Concurrent DOS 68K, a port of Concurrent DOS to Motorola 68000 CPUs with DOS source code portability capabilities FlexOS 1.0 – 2.34, a derivative of Concurrent DOS 286 FlexOS 186, a variant of FlexOS for terminals FlexOS 286, a variant of FlexOS for hosts Siemens S5-DOS/MT, an industrial control system based on FlexOS IBM 4680 OS, a POS operating system based on FlexOS IBM 4690 OS, a POS operating system based on FlexOS Toshiba 4690 OS, a POS operating system based on IBM 4690 OS and FlexOS FlexOS 386, a later variant of FlexOS for hosts IBM 4690 OS, a POS operating system based on FlexOS Toshiba 4690 OS, a POS operating system based on IBM 4690 OS and FlexOS FlexOS 68K, a derivative of Concurrent DOS 68K Multiuser DOS, the successor of Concurrent DOS 386 CCI Multiuser DOS Datapac Multiuser DOS Datapac System Manager, a derivative of Datapac Multiuser DOS IMS Multiuser DOS IMS REAL/32, a derivative of Multiuser DOS IMS REAL/NG, the successor of REAL/32 DOS Plus 1.1 – 2.1, a single-user, multi-tasking system derived from Concurrent DOS 4.1 – 5.0 DR-DOS 3.31 – 6.0, a single-user, single-tasking native DOS derived from Concurrent DOS 6.0 Novell PalmDOS 1.0 Novell "Star Trek" Novell DOS 7, a single-user, multi-tasking system derived from DR DOS Caldera OpenDOS 7.01 Caldera DR-DOS 7.02 and higher === Digital Equipment Corporation, Compaq, Hewlett-Packard, Hewlett Packard Enterprise === Batch-11/DOS-11 OS/8 RSTS/E – multi-user time-sharing OS for PDP-11s RSX-11 – multiuser, multitasking OS for PDP-11s RT-11 – single user OS for PDP-11 TOPS-10 – for the PDP-10 TENEX – an ancestor of TOPS-20 from BBN, for the PDP-10 TOPS-20 – for the PDP-10 DEC MICA – for the DEC PRISM Digital UNIX – derived from OSF/1, became HP's Tru64 UNIX Ultrix VMS – originally by DEC (now by VMS Software Inc.) for the VAX mini-computer range; later renamed OpenVMS and ported to Alpha, and subsequently ported to Intel Itanium and then to x86-64 WAITS – for the PDP-6 and PDP-10 === ENEA AB === OSE – Flexible, small footprint, high-performance RTOS for control processors === Fujitsu === Towns OS XSP OS/IV MSP MSP-EX === GEC Computers === COS DOS OS4000 === General Electric, Honeywell, Bull === Real-Time Multiprogramming Operating System GCOS Multics === Google === ChromiumOS is an open source operating system development version of ChromeOS. Both operating systems are based on the Linux kernel. ChromeOS is designed to work exclusively with web applications, though has been updated to run Android apps with full support for Google Play Store. Announced on July 7, 2009, ChromeOS is currently publicly available and was released summer 2011. The ChromeOS source code was released on November 19, 2009, under the BSD license as ChromiumOS. Container-Optimized OS (COS) is an operating system that is optimized for running Docker containers, based on ChromiumOS. Android is an operating system for mobile devices. It consists of Android Runtime (userland) with Linux (kernel), with its Linux kernel modified to add drivers for mobile device hardware and to remove unused Vanilla Linux drivers. gLinux, a Linux distribution that Google uses internally Fuchsia is a capability-based real-time operating system (RTOS) scalable to universal devices, in early development, from the tiniest embedded hardware, wristwatches, tablets to the largest personal computers. Unlike ChromeOS and Android, it is not based on the Linux kernel, but instead began on a new microkernel called "Zircon", derived from "Little Kernel". Wear OS a version of Google's Android operating system designed for smartwatches and other wearables. === Green Hills Software === INTEGRITY – Reliable Operating system INTEGRITY-178B – A DO-178B certified version of INTEGRITY. μ-

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