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  • Relational data mining

    Relational data mining

    Relational data mining is the data mining technique for relational databases. Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional patterns, there are corresponding relational patterns. For example, there are relational classification rules (relational classification), relational regression tree, and relational association rules. There are several approaches to relational data mining: Inductive Logic Programming (ILP) Statistical Relational Learning (SRL) Graph Mining Propositionalization Multi-view learning == Algorithms == Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) is a new class of association rules which in contrast to primitive, simple and even multi-relational association rules (that are usually extracted from multi-relational databases), each rule item consists of one entity but several relations. These relations indicate indirect relationship between the entities. Consider the following MRAR where the first item consists of three relations live in, nearby and humid: “Those who live in a place which is near by a city with humid climate type and also are younger than 20 -> their health condition is good”. Such association rules are extractable from RDBMS data or semantic web data. == Software == Safarii: a Data Mining environment for analysing large relational databases based on a multi-relational data mining engine. Dataconda: a software, free for research and teaching purposes, that helps mining relational databases without the use of SQL. == Datasets == Relational dataset repository: a collection of publicly available relational datasets.

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  • Digital inclusion

    Digital inclusion

    Digital inclusion involves the activities necessary to ensure equitable access to and use of information and communications technologies for participation in social and economic life including for education, social services, health, social, and community participation. Digital inclusion includes access to affordable broadband Internet services, Internet-enabled devices, access to digital literacy training, quality technical support, and applications and online content designed to enable and encourage self-sufficiency, participation, and collaboration. Related concepts include digital divide, digital exclusion and digital inequality, however digital inclusion focuses more on the strategies, policies, and programs required to address the digital divide. As many services have moved online and with the increasing use of telehealth to deliver primary care, particularly during the COVID-19 pandemic in 2020, digital inclusion, including digital literacy and internet access is increasingly regarded as a social determinant of health. Accessibility, relevance, and impact have been identified as essential elements of digital inclusion as it pertains to health information systems. "Digital inclusion is broadly defined as different strategies designed to ensure that all people have equal access, opportunities and skills to benefit from digital technologies and systems" (ITU, 2019, as cited in Carmi and Yates, 2020). Since 2020, there have been many technology companies that have begun implementing different features or roles within their companies to support breaking down the digital divide. For example, HP has announced the digital divide accelerator. This accelerator will support nonprofits in Greece, Indonesia, Nigeria, and Spain. The goal for this role is to help equip children and other people within the community to understand the skills needed to become a part of the digital community. == Background == With the increasing use of computers and the Internet in the 1990s and early 2000s concerns rose around digital equality, however this primarily focused on the physical access to technology. This gave rise to the concept of the digital divide which was originally developed to describe the growing disparity in Internet access between rural and urban areas of the United States of America. This gradually expanded to considerations of digital access between countries in what is termed the global digital divide, which mirrors many of the disparities seen within countries but on an international scale. However, with the adoption of digital technologies across most sectors of society, and the increasing diversity of technologies and programs, access and use of ICT became more complex and essential for many aspects of daily life. This led to new terminology and a second wave of research on digital inequality which has been identified as the (1) usage gap, (2) second level digital divide, (3) emerging digital differentiation, and (4) digital inclusion. == Strategies for digital inclusion == A review of the literature in 2019 found that while physical access to digital technologies and the internet continues to be a barrier to digital inclusion, digital ability and attitude were also potential barriers. Key strategies identified for improving digital inclusion are social support, direct user experience and collaborative learning/design. Education is a key aspect of digital inclusion as digital technologies have become a key means of engaging with all levels of the education system, requiring levels of digital competence for successful engagement with the curriculum. In addition lifelong learning is required as technologies, services and systems are changing constantly. Public libraries and community service providers play a key role in supporting digital inclusion through access to computers, internet connection and expertise and training. Designing for digital inclusion may also help with poor written literacy, which remains a barrier for 10% of the world's population. UNESCO has developed Guidelines for designing digital technologies in ways that could assist those who are illiterate. == Indigenous digital inclusion == Digital inclusion is a critical issue for many Indigenous communities across the globe, many of whom lack access to adequate resources. The Australian Government has set a National Closing the Gap target for Aboriginal and Torres Strait Islander people to have equal levels of digital inclusion by 2026. Many people on tribal land and in Native Hawaiian land struggle with the technology gap. The Native Entities Capacity and Planning Grant Program has $45.3 million available to help address these challenges and empower Indian Tribes, Alaska Native entities, and Native Hawaiian organizations. Some of the impacts so far are in the education and workforce development and healthcare access through telehealth.[13] == Measuring digital inclusion == The Australian Digital Inclusion Index (ADII) is a research project which has been tracking digital inclusion throughout Australia since 2016. It uses survey data to measure digital inclusion across three dimensions of access, affordability and digital ability. == The Future of Digital Inclusion == On February 16, 2021, a global dialogue within the United Nations (UN) took a look at the future of digital inclusion. Through the adoption of the 2030 UN Agenda for Sustainable Development, Member States made a commitment. They pledged to "leave no one behind." By 2030, the UN's goal is to close the digital divide by providing access to the Internet and mobile technologies for all nations and peoples and for all segments of society. The UN sees the crisis of too many people in our global society still living unconnected and how the digital divide remains a challenge that must be addressed. == Gaming == The Xbox Adaptive Controller is a groundbreaking example of digital inclusion, designed to make gaming more accessible to people with limited mobility. Developed by Microsoft, it features large programmable buttons and ports that connect to a wide range of external devices like switches, joysticks, and mounts, allowing users to customize their gaming experience based on their unique needs. By removing traditional physical barriers to gameplay, the Xbox Adaptive Controller empowers more people to participate in digital entertainment, promotes equal access to technology, and fosters a more inclusive gaming community. == Digital inclusion advocacy groups == Australian Digital Inclusion Alliance National Digital Inclusion Alliance (US)

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  • Hydration (web development)

    Hydration (web development)

    In web development, hydration or rehydration is a technique in which client-side JavaScript converts a web page that is static from the perspective of the web browser, delivered either through static rendering or server-side rendering, into a dynamic web page by attaching event handlers to the HTML elements in the DOM. Because the HTML is pre-rendered on a server, this allows for a fast "first contentful paint" (when useful data is first displayed to the user), but there is a period of time afterward where the page appears to be fully loaded and interactive, but is not until the client-side JavaScript is executed and event handlers have been attached. Frameworks that use hydration include Next.js and Nuxt. React v16.0 introduced a "hydrate" function, which hydrates an element, in its API. == Variations == === Streaming server-side rendering === Streaming server-side rendering allows one to send HTML in chunks that the browser can progressively render as it is received. This can provide a fast first paint and first contentful paint as HTML markup arrives to users faster. === Progressive rehydration === In progressive rehydration, individual pieces of a server-rendered application are “booted up” over time, rather than the current common approach of initializing the entire application at once. This can help reduce the amount of JavaScript required to make pages interactive, since client-side upgrading of low priority parts of the page can be deferred to prevent blocking the main thread. It can also help avoid one of the most common server-side rendering rehydration pitfalls, where a server-rendered DOM tree gets destroyed and then immediately rebuilt – most often because the initial synchronous client-side render required data that wasn't quite ready, perhaps awaiting Promise resolution. === Partial rehydration === Partial rehydration has proven difficult to implement. This approach is an extension of the idea of progressive rehydration, where the individual pieces (components/views/trees) to be progressively rehydrated are analyzed and those with little interactivity or no reactivity are identified. For each of these mostly-static parts, the corresponding JavaScript code is then transformed into inert references and decorative functionality, reducing their client-side footprint to near-zero. The partial hydration approach comes with its own issues and compromises. It poses some interesting challenges for caching, and client-side navigation means it cannot be assumed that server-rendered HTML for inert parts of the application will be available without a full page load. One framework that supports partial rehydration is Elder.js, which is based on Svelte. === Trisomorphic rendering === Trisomorphic rendering is a technique which uses streaming server-side rendering for initial/non-JavaScript navigations, and then uses service workers to take on rendering of HTML for navigations after it has been installed. This can keep cached components and templates up to date and enables SPA-style navigations for rendering new views in the same session. This approach works best when one can share the same templating and routing code between the server, client page, and service worker.

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  • Digital backlot

    Digital backlot

    A digital backlot or virtual backlot is a motion-picture set that is neither a genuine location nor a constructed studio; the shooting takes place entirely on a stage with a blank background (often a greenscreen) that will later on project an artificial environment put in during post-production. Digital backlots are mainly used for genres such as science fiction, where building a real set would be too expensive or outright impossible. == Notable films == Among the first films to introduce the technique was Mini Moni the Movie by Shinji Higuchi in 2002, predated by Rest In Peace by Stolpskott Film (2000). Others include: === Released === Rest in Peace (Sweden, 2000) – Shot entirely with green-screen. Some sections fully CGI. Casshern (Japan, 2004) – Shot on celluloid. A few practical set pieces used. Able Edwards (United States, 2004) – Shot digitally on Canon XL1 cameras. Immortal (France, 2004) – Shot on celluloid. Also showed CGI characters interacting with live actors. Sky Captain and the World of Tomorrow (United States, 2004) – Shot digitally on Sony CineAlta cameras. Sin City (United States, 2005) – Shot digitally on CineAlta cameras. Three practical sets used. MirrorMask (United States/United Kingdom, 2005) – Shot on celluloid. 80% of film uses digital backlot. Some practical set pieces used. The Cabinet of Dr. Caligari (United States, 2005) – Shot digitally. 300 (United States, 2007) – Shot on celluloid. Two practical sets used. Speed Racer (United States, 2008) – Directed by the Wachowskis. Three practical sets used. The Spirit (United States, 2008) – Director Frank Miller shot the film with the same techniques he and Robert Rodriguez used on Sin City. Avatar (United States, 2009) – Directed by James Cameron. Two practical sets used. Goemon (Japan, 2009) – The second film from Casshern helmer Kazuaki Kiriya. Alice in Wonderland (United States, 2010) – Directed by Tim Burton. Practical sets used. Sin City: A Dame to Kill For (United States 2014) – Co-directed by Robert Rodriguez and Frank Miller. Sequel to Sin City. === Upcoming === Tribes of October

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  • AppValley

    AppValley

    AppValley is an independent American digital distribution service operated and trademarked by AppValley LLC. It serves as an alternative app store for the iOS mobile operating system, which allows users to download applications that are not available on the App Store, most commonly tweaked "++" apps, jailbreak apps, and apps including paid apps on the app store. == Legality == AppValley is among several services that violate enterprise developer certificates from Apple. The terms under which these are granted make clear that they are for companies who wish to distribute apps to their employees. AppValley uses these certificates to distribute software directly to non-employees, thereby bypassing the AppStore. AppValley's conduct had implications in U.S. sanctioned markets like Iran, Iraq, North Korea, Cuba, and Venezuela, which have all been subject to commercial sanctions. Among the software offered by AppValley and other services is pirated software, including paid apps on the app store and premium versions of Instagram, Spotify, Pokémon Go, and others. For instance, AppValley distributes an ad-free version of the music streaming app Spotify even on the free tier. == History == The website was founded in May 2017, releasing late that month with a very basic version of the app. There were less than 100 apps available for download at this time. On Jan 19, 2018, a new version dubbed AppValley 2.0 was released bringing dark mode, more categories, a search, and a much faster interface. On February 14, 2019, a Chinese partner "Jason Wu" allegedly took control of the main Twitter account and domain, causing the original AppValley developers to migrate to the domain app-valley.vip and the Twitter account handle @App_Valley_vip. As of September 2024, the app-valley.vip domain now redirects to appvalley.signulous.com. Today, AppValley continues to offer an alternative to Apple's App Store where app developers can publish their applications. == Features == AppValley is a mobile app installer which can also support iOS version that can be installed and downloaded on the mobile or the devices of the people who wish to get access to many different applications available. AppValley also contains apps that have been modified or tweaked for user preferences, and allows the user to by pass national restrictions on the use of apps, without having to resort to jailbreaking. As of June 2, 2020, there are over 1300 apps available for download.

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  • VibeOS

    VibeOS

    VibeOS is an operating system built from scratch entirely by generative artificial intelligence, using code produced through prompts to Claude (vibe coding). It is capable of running on QEMU and was successfully tested on a Raspberry Pi Zero. It has been released under the MIT license. == Features == === Core === Custom kernel with cooperative multitasking (preemptive backup) FAT32 filesystem with long filename support Memory allocator, process scheduler, interrupt handling GIC-400 (QEMU) and BCM2836/BCM2835 (Pi) interrupt controllers Configurable boot (splash screen, boot target) === GUI === Desktop environment with draggable windows Menu bar, dock, window minimize/maximize/close Mouse and keyboard input Modern macOS-inspired aesthetic === Networking === Full TCP/IP stack (Ethernet, ARP, IP, ICMP, UDP, TCP) DNS resolver HTTP client TLS 1.2 with HTTPS support === Apps === Web browser with HTML/CSS rendering Terminal emulator with readline-style shell Text editor (vim clone) with syntax highlighting File manager with drag-and-drop Music player (MP3/WAV) Calculator, system monitor VibeCode IDE Doom port === Development === TCC (Tiny C Compiler) - compile C programs directly on VibeOS MicroPython interpreter with full kernel API bindings 60+ userspace programs (coreutils, games, GUI apps) === Hardware === Runs on Raspberry Pi Zero 2W USB keyboard and mouse via DWC2 driver SD card via EMMC driver 1920×1080 framebuffer == Further projects == There are other independent projects under the VibeOS name, including an independent development by Ben, also developed using vibe coding, aimed at creating a Unix-like operating system for educational purposes. Another project is Vib-OS, an operating system also built using vibe coding, capable of booting on a Raspberry Pi. It offers a desktop environment with a customizable wallpaper, a file manager, and a web browser currently in an early stage of development, a functional Doom port, among other features that are not very polished given the state of development.

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  • CSS box model

    CSS box model

    In web development, the CSS box model refers to how HTML elements are modeled in browser engines and how the dimensions of those HTML elements are derived from CSS properties. It is a fundamental concept for the composition of HTML webpages. The guidelines of the box model are described by web standards World Wide Web Consortium (W3C) specifically the CSS Working Group. For much of the late-1990s and early 2000s there had been non-standard compliant implementations of the box model in mainstream browsers. With the advent of CSS2 in 1998, which introduced the box-sizing property, the problem had mostly been resolved. == Specifics == The Cascading Style Sheets (CSS) specification describes how elements of web pages are displayed by graphical browsers. Section 4 of the CSS1 specification defines a "formatting model" that gives block-level elements—such as p and blockquote—a width and height, and three levels of boxes surrounding it: padding, borders, and margins. While the specification never uses the term "box model" explicitly, the term has become widely used by web developers and web browser vendors. All HTML elements can be considered "boxes", this includes div tag, p tag, or a tag. Each of those boxes has five modifiable dimensions: the height and width describe dimensions of the actual content of the box (text, images, ...) the padding describes the space between this content and the border of the box the border is any kind of line (solid, dotted, dashed...) surrounding the box, if present the margin is the space around the border According to the CSS1 specification, released by W3C in 1996 and revised in 1999, when a width or height is explicitly specified for any block-level element, it should determine only the width or height of the visible element, with the padding, borders, and margins applied afterward. Before CSS3, this box model was known as W3C box model, in CSS3, it is known as the content-box. The total width of a box is therefore margin-left + border-left + padding-left + width + padding-right + border-right + margin-right. Similarly, the total height of a box equals margin-top + border-top + padding-top + height + padding-bottom + border-bottom + margin-bottom. For example, the following CSS code would specify the box dimensions of each block belonging to 'my-class'. Moreover, each such box will have total height 140px and width 240px. CSS3 introduced the Internet Explorer box model to the standard, known referred to as border-box. == History == Before HTML 4 and CSS, very few HTML elements supported both border and padding, so the definition of the width and height of an element was not very contentious. However, it varied depending on the element. The HTML width attribute of a table defined the width of the table including its border. On the other hand, the HTML width attribute of an image defined the width of the image itself (inside any border). The only element to support padding in those early days was the table cell. Width for the cell was defined as "the suggested width for a cell content in pixels excluding the cell padding." In 1996, CSS introduced margin, border and padding for many more elements. It adopted a definition width in relation to content, border, margin and padding similar to that for a table cell. This has since become known as the W3C box model. At the time, very few browser vendors implemented the W3C box model to the letter. The two major browsers at the time, Netscape 4.0 and Internet Explorer 4.0 both defined width and height as the distance from border to border. This has been referred to as the traditional or the Internet Explorer box model. Internet Explorer in "quirks mode" includes the content, padding and borders within a specified width or height; this results in a narrower or shorter rendering of a box than would result following the standard behavior. The Internet Explorer box model behavior was often considered a bug, because of the way in which earlier versions of Internet Explorer handle the box model or sizing of elements in a web page, which differs from the standard way recommended by the W3C for the Cascading Style Sheets language. As of Internet Explorer 6, the browser supports an alternative rendering mode (called the "standards-compliant mode") which solves this discrepancy. However, for backward compatibility reasons, all versions still behave in the usual, non-standard way by default (see quirks mode). Internet Explorer for Mac is not affected by this non-standard behavior. === Workarounds === Internet Explorer versions 6 and onward are not affected by the bug if the page contains certain HTML document type declarations. These versions maintain the buggy behavior when in quirks mode for reasons of backward compatibility. For example, quirks mode is triggered: When the document type declaration is absent or incomplete; When an HTML 3 or earlier document is encountered; When an HTML 4.0 Transitional or Frameset document type declaration is used and a system identifier (URI) is not present; When an SGML comment or other unrecognized content appears before the document type declaration Internet Explorer 6 also uses quirks mode if there is an XML declaration prior to the document type declaration. Various workarounds have been devised to force Internet Explorer versions 5 and earlier to display Web pages using the W3C box model. These workarounds generally exploit unrelated bugs in Internet Explorer's CSS selector processing in order to hide certain rules from the browser. The best known of these workarounds is the "box model hack" developed by Tantek Çelik, a former Microsoft employee who developed this idea while working on Internet Explorer for the Macintosh. It involves specifying a width declaration for Internet Explorer for Windows, and then overriding it with another width declaration for CSS-compliant browsers. This second declaration is hidden from Internet Explorer for Windows by exploiting other bugs in the way that it parses CSS rules. The implementation of these CSS “hacks” has been further complicated by the public release of Internet Explorer 7, which has had some issues fixed, but not others, causing undesired results in pages using these hacks. Box model hacks have proven unreliable because they rely on bugs in browsers' CSS support that may be fixed in later versions. For this reason, some Web developers have instead recommended either avoiding specifying both width and padding for the same element or using conditional comment and/or CSS filters to work around the box model bug in older versions of Internet Explorer. == Support for Internet Explorer's box model == Web designer Doug Bowman has said that the original Internet Explorer box model represents a better, more logical approach. Peter-Paul Koch gives the example of a physical box, whose dimensions always refer to the box itself, including potential padding, but never its content. He says that this box model is more useful for graphic designers, who create designs based on the visible width of boxes rather than the width of their content. Bernie Zimmermann says that the Internet Explorer box model is closer to the definition of cell dimensions and padding used in the HTML table model. The W3C has included a "box-sizing" property in CSS3. When box-sizing: border-box; is specified for an element, any padding or border of the element is drawn inside the specified width and height, "as commonly implemented by legacy HTML user agents". Internet Explorer 8, WebKit browsers such as Apple Safari 5.1+ and Google Chrome, Gecko-based browsers such as Mozilla Firefox 29.0 and later, Opera 7.0 and later, and Konqueror 3.3.2 and later support the CSS3 box-sizing property. Gecko browsers previous than 29.0 support the same functionality using the browser-specific -moz-box-sizing property. border-box is the default box model used in Bootstrap framework.

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  • Mean opinion score

    Mean opinion score

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

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  • INDIAai

    INDIAai

    INDIAai is a web portal launched by the Government of India on 07 March 2024 for artificial intelligence-related developments in India. It is known as the National AI Portal of India, which was jointly started by the Ministry of Electronics and Information Technology (MeitY), the National e-Governance Division (NeGD) and the National Association of Software and Service Companies (NASSCOM) with support from the Department of School Education and Literacy (DoSE&L) and Ministry of Human Resource Development. == History == The portal was launched on 30 May 2020, by Ravi Shankar Prasad, the Union Minister for Electronics and IT, Law and Justice and Communications, on the first anniversary of the second tenure of Prime Minister Narendra Modi-led government. A national program for the youth, 'Responsible AI for Youth', was also launched on the same day. As of 2022, the website was visited by more than 4.5 lakh users with 1.2 million page views. It has 1151 articles on artificial intelligence, 701 news stories, 98 reports, 95 case studies and 213 videos on its portal. It maintains a database on AI ecosystem of India featuring 121 government initiatives and 281 startups. In May 2022, INDIAai released a book titled 'AI for Everyone' that covers the basics of AI. Cabinet chaired by the Prime Minister Narendra Modi has approved the comprehensive national-level IndiaAI mission with a budget outlay of Rs.10,371.92 crore. The Mission will be implemented by ‘IndiaAI’ Independent Business Division (IBD) under Digital India Corporation (DIC). == Objective and features == It aims to function as a one-stop portal for all AI-related development in India. The platform publishes resources such as articles, news, interviews, and investment funding news and events for AI startups, AI companies, and educational firms related to artificial intelligence in India. It also distributes documents, case studies, and research reports. Additionally, the platform provides education and employment opportunities related to AI. It offers AI courses, both free and paid.

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  • Terrorism and social media

    Terrorism and social media

    Terrorism, fear, and media are interconnected. Terrorists use the media to advertise their attacks and or messages, and the media uses terrorism events to further aid their ratings. Both promote unwarranted propaganda that instills mass amounts of public fear. The leader of al-Qaeda, Osama bin Laden, discussed the weaponization of media in a letter written after his organization committed the terrorist attacks on September 11, 2001. In that letter, bin Laden stated that fear was the deadliest weapon. He noted that the Western civilization has become obsessed with mass media, quickly consuming what will bring them fear. He further stated that societies are bringing this problem on their own people by giving media coverage an inherent power. In relation to one's need for media coverage, al-Qaeda and other militant Jihadi terrorist organizations can be classified as a far-right radical offshoot of mainstream mass media. The Jihad needs to conceptualize their martyrdom by leaving behind manifestos and live videos of their attacks; it is crucially important to them that their ill deeds are being covered by news media. The components the media looks for to deem the news "worthy" enough to publicize are categorized into ten qualities; terrorists usually exceed half in their attacks. These include: Immediacy, Conflict, Negativity, Human Interest, Photographability, Simple Story Lines, Topicality, Exclusivity, Reliability, and Local Interest. Historically, morality and profitability are two motivations which are not easily weighed when delivering news; recent news coverage has become far more motivated in making money for their parent corporation than serving as a defender of truth, doing true journalistic fact-finding, and shielding the public from news which is sensational, outright untrue, or politically-motivated propaganda. A study concerning the disparity in coverage of terrorist events took attacks from the ten‑year span of 2005–2015 and found that 136 episodes of terrorism occurred in the United States. LexisNexis Academic and CNN were the platforms used to measure the media coverage. It was found that out of other terrorist attacks showed on the news, one's with Muslim perpetrators received more than 357% coverage. In addition to this disparity, attacks also received more coverage when they were targeted at the government, had high fatality rates, and showed arrests being made. These findings were aligned with America's tendency to categorize Muslim people as a threat to national security. Thus, mass media coverage on terrorism is creating fake narratives and an absence of related coverage. For instance, the American public believes that crime rates have been on the rise which in fact they have been on an all-time low. Given that the media often covers crime almost immediately and frequently, suggests that people infer it happening all the time. In reference to the disparity in terror attacks, three attacks were seen to have the least media coverage of all the 136. The Sikh Temple massacre in Wisconsin which had 2.6% coverage, the Kansas synagogue killings which had 2.2%, and the Charleston Church deaths which only resulted in 5.1% coverage. The three events had commonalities worth mentioning in that they all had white perpetrators and were not directed at government intuitions (in fact all targeted minorities). The media's obsession with terror is making people fearful of the wrong things and not attentive enough to the issues that are radically unseen. Not only are minorities usually not the perpetrators of domestic terrorism, but they are common victims in mass casualties or proximal witnesses to the attacks. In an early 2000s study, 72 Israeli adults were measured pre and posttest for increased anxiety after being exposed to news broadcasts of terrorism attacks. The study found that the group exposed to the broadcasts without any treatment (preparation intervention) had heightened levels of anxiety compared to the group that received the treatment along with viewing the broadcast. Since preparatory intervention is not yet normalized, people in proximity to ongoing coverage of terror events are suffering from the lasting impacts of fear and anxiety. Preparatory Intervention, in this case, was conducted by a group facilitator who introduced a topic concerning terrorism in which participants were instructed to write down feelings to share with the group and later learn to cope with. A discourse of fear created by mass media presence, but false information is leading people to prepare for the wrong situations. In the early 2000s, police units circulated public schools flooding the idea of Stranger Danger into the minds of adolescents. Children and their parents cautiously separated from strangers while perpetrators in those families' social circles continued to offend under the radar. For myths are becoming common, precedent and real danger is buried beneath the surface. It is these implementations of fear that are falsifying the true narrative which for terrorism is a huge social problem but one that is not resolved through entertainment and mass media production. Mass media like news outlets and even social media platforms are contributing to the growing discourse of fear surrounding terrorism. Terrorism and social media refers to the use of social media platforms to radicalize and recruit violent and non-violent extremists. According to some researchers the convenience, affordability, and broad reach of social media platforms such as YouTube, Facebook and Twitter, terrorist groups and individuals have increasingly used social media to further their goals, recruit members, and spread their message. Attempts have been made by various governments and agencies to thwart the use of social media by terrorist organizations.Terror groups take to social media because it's cheap, accessible, and facilitates quick access to a lot of people. Social media allow them to engage with their networks. In the past, it wasn't so easy for these groups to engage with the people they wanted to whereas social media allows terrorists to release their messages right to their intended audience and interact with them in real time. "Spend some time following the account, and you realize that you're dealing with a real human being with real ideas- albeit boastful, hypocritical, violent ideas". Al- Qaeda has been noted as being as being one of the terror groups that uses social media the most extensively. "While almost all terrorist groups have websites, al qaeda [sic] is the first to fully exploit the internet. This reflects al-Qaeda's unique characteristics." Despite the risks of making statements, such as enabling governments to locate terror group leaders, terror leaders communicate regularly with video and audio messages which are posted on the website and disseminated on the internet. ISIS uses social media to their advantage when releasing threatening videos of beheadings. ISIS uses this tactic to scare normal people on social media. Similarly, Western domestic terrorists also use social media and technology to spread their ideas. == Traditional media == Many authors have proposed that media attention increases perceptions of risk of fear of terrorism and crime and relates to how much attention the person pays to the news. The relationship between terrorism and the media has long been noted. Terrorist organizations depend on the open media systems of democratic countries to further their goals and spread their messages. To garner publicity for their cause, terrorist organizations resort to acts of violence and aggression that deliberately target civilians. This method has proven to be effective in gathering attention: It cannot be denied that although terrorism has proved remarkably ineffective as the major weapon for taking down governments and capturing political power, it has been a remarkably successful means of publicizing a political cause and relaying the terrorist threat to a wider audience, particularly in the open and pluralistic countries of the West. When one says 'terrorism' in a democratic society, one also says 'media'. While a media organization may not support the goals of terrorist organizations, it is their job to report current events and issues. In the fiercely competitive media environment, when a terrorist attack occurs, media outlets scramble to cover the event. In doing so, the media help to further the message of terrorist organizations: To summarise briefly on the symbiotic nature of the relationship between terrorists and the media, the recent history of terrorism in many democratic countries vividly demonstrates that terrorists do thrive on the oxygen of publicity, and it is foolish to deny this. This does not mean that the established democratic media share the values of the terrorists. It does demonstrate, however, that the free media in an open society are particularly vulnerable to exploitation and manipulation by ru

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  • Technology company

    Technology company

    A technology company, or tech company, is a company that focuses primarily on the manufacturing, support, research and development of—most commonly computing, telecommunication and consumer electronics–based—technology-intensive products and services, which include businesses relating to digital electronics, software, optics, new energy, and Internet-related services such as cloud storage and e-commerce services. Big Tech refers to the 6 largest companies, both in the United States and globally, symbolized by the metonym 'Silicon Valley', where 4 of them are based. == Details == According to Fortune, as of 2020, the ten largest technology companies by revenue are: Apple Inc., Samsung, Foxconn, Alphabet Inc., Microsoft, Huawei, Dell Technologies, Hitachi, IBM, and Sony. Amazon has higher revenue than Apple, but is classified by Fortune in the retail sector. The most profitable listed in 2020 are Apple Inc., Microsoft, Alphabet Inc., Intel, Meta Platforms, Samsung, and Tencent. Apple Inc., Alphabet Inc. (owner of Google), Meta Platforms (owner of Facebook), Microsoft, and Amazon.com, Inc. are often referred to as the Big Five multinational technology companies based in the United States. These five technology companies dominate major functions, e-commerce channels, and information of the entire Internet ecosystem. As of 2017, the Big Five had a combined valuation of over $3.3 trillion and make up more than 40 percent of the value of the Nasdaq-100 index. Many large tech companies have a reputation for innovation, spending large sums of money annually on research and development. According to PwC's 2017 Global Innovation 1000 ranking, tech companies made up nine of the 20 most innovative companies in the world, with the top R&D spender (as measured by expenditure) being Amazon, followed by Alphabet Inc., and then Intel. As a result of numerous influential tech companies and tech startups opening offices in proximity to one another, a number of technology districts have developed in various areas across the globe. These include: Silicon Valley in the San Francisco Bay Area, Silicon Wadi in Israel, Silicon Docks in Dublin, Silicon Hills in Austin, Tech City in London; Digital Media City in Seoul, Zhongguancun in Beijing, Cyberjaya in Malaysia and Cyberabad in Hyderabad, India. As of 2026, Europe has six of the world's 100 most valuable tech companies, compared with 56 in the United States and 16 in China.

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  • Asymmetric follow

    Asymmetric follow

    An asymmetric follow social network is one which allows many people to follow an individual or account without having to follow them back. It is also known as asynchronous follow or sometimes asymmetric friendship. Asymmetric follow is a common pattern on Twitter, where someone may have thousands of followers, but themselves follow few (or no) accounts. In September 2010 Facebook started experimenting with a similar feature, which Facebook calls "Subscribe To."

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  • Tessellation (computer graphics)

    Tessellation (computer graphics)

    In computer graphics, tessellation is the dividing of datasets of polygons (sometimes called vertex sets) presenting objects in a scene into suitable structures for rendering. Especially for real-time rendering, data is tessellated into triangles, for example in OpenGL 4.0 and Direct3D 11. == In graphics rendering == A key advantage of tessellation for realtime graphics is that it allows detail to be dynamically added and subtracted from a 3D polygon mesh and its silhouette edges based on control parameters (often camera distance). In previously leading realtime techniques such as parallax mapping and bump mapping, surface details could be simulated at the pixel level, but silhouette edge detail was fundamentally limited by the quality of the original dataset. In Direct3D 11 pipeline (a part of DirectX 11), the graphics primitive is the patch. The tessellator generates a triangle-based tessellation of the patch according to tessellation parameters such as the TessFactor, which controls the degree of fineness of the mesh. The tessellation, along with shaders such as a Phong shader, allows for producing smoother surfaces than would be generated by the original mesh. By offloading the tessellation process onto the GPU hardware, smoothing can be performed in real time. Tessellation can also be used for implementing subdivision surfaces, level of detail scaling and fine displacement mapping. OpenGL 4.0 uses a similar pipeline, where tessellation into triangles is controlled by the Tessellation Control Shader and a set of four tessellation parameters. == In computer-aided design == In computer-aided design the constructed design is represented by a boundary representation topological model, where analytical 3D surfaces and curves, limited to faces, edges, and vertices, constitute a continuous boundary of a 3D body. Arbitrary 3D bodies are often too complicated to analyze directly. So they are approximated (tessellated) with a mesh of small, easy-to-analyze pieces of 3D volume—usually either irregular tetrahedra, or irregular hexahedra. The mesh is used for finite element analysis. The mesh of a surface is usually generated per individual faces and edges (approximated to polylines) so that original limit vertices are included into mesh. To ensure that approximation of the original surface suits the needs of further processing, three basic parameters are usually defined for the surface mesh generator: The maximum allowed distance between the planar approximation polygon and the surface (known as "sag"). This parameter ensures that mesh is similar enough to the original analytical surface (or the polyline is similar to the original curve). The maximum allowed size of the approximation polygon (for triangulations it can be maximum allowed length of triangle sides). This parameter ensures enough detail for further analysis. The maximum allowed angle between two adjacent approximation polygons (on the same face). This parameter ensures that even very small humps or hollows that can have significant effect to analysis will not disappear in mesh. An algorithm generating a mesh is typically controlled by the above three and other parameters. Some types of computer analysis of a constructed design require an adaptive mesh refinement, which is a mesh made finer (using stronger parameters) in regions where the analysis needs more detail.

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  • FaceApp

    FaceApp

    FaceApp is a photo and video editing application for iOS and Android developed by FaceApp Technology Limited, a company based in Cyprus. The app generates highly realistic transformations of human faces in photographs by using neural networks. The app can transform a face to make it smile, look younger, look older, or change gender. == History == FaceApp was launched on iOS in January 2017 and on Android in February 2017. It was developed by Yaroslav Goncharov, a former executive at Yandex, and created by the Russian company Wireless Lab. == Features == There are multiple options to manipulate the photo uploaded such as editor options of adding an impression, make-up, smiles, hair colors, hairstyles, glasses, age or beards. Filters, lens blur and backgrounds along with overlays, tattoos, and vignettes are also a part of the app. The gender change transformations of FaceApp have attracted particular interest from the LGBT and transgender communities, due to their ability to realistically simulate the appearance of a person as the opposite gender. == Criticism == In 2017, FaceApp faced criticism for a "hot" filter that appeared to lighten users' skin tones, prompting accusations of racial bias. The feature was briefly renamed "spark" before being removed. Founder Yaroslav Goncharov attributed the issue to training data bias and apologized. In August of that year, more criticism arose when it featured "ethnicity filters" depicting "White", "Black", "Asian", and "Indian". The filters were immediately removed from the app. In 2019, FaceApp faced criticism over its handling of user data, including concerns that it stored users' photos on its servers and could use them for commercial purposes. Founder Yaroslav Goncharov stated that images were processed on cloud servers like Google Cloud Platform and Amazon Web Services, not transferred to Russia, and were temporarily stored only to support editing functions before being deleted. U.S. Senator Chuck Schumer raised concerns about data privacy and called for an FBI investigation.

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  • Digital Cinema Package

    Digital Cinema Package

    A Digital Cinema Package (DCP) is a collection of digital files used to store and convey digital cinema (DC) audio, image, and data streams. The term was popularized by Digital Cinema Initiatives, LLC in its original recommendation for packaging DC contents. However, the industry tends to apply the term to the structure more formally known as the composition. A DCP is a container format for compositions, a hierarchical file structure that represents a title version. The DCP may carry a partial composition (e.g. not a complete set of files), a single complete composition, or multiple and complete compositions. The composition consists of a Composition Playlist (in XML format) that defines the playback sequence of a set of Track Files. Track Files carry the essence (audio, image, subtitles), which is wrapped using Material eXchange Format (MXF). Track Files must contain only one essence type. Two track files at a minimum must be present in every composition (see SMPTE ST429-2 D-Cinema Packaging – DCP Constraints, or Cinepedia): a track file carrying picture essence, and a track file carrying audio essence. The composition, consisting of a Composition Playlist (CPL) and associated track files, are distributed as a Digital Cinema Package (DCP). A composition is a complete representation of a title version, while the DCP need not carry a full composition. However, as already noted, it is commonplace in the industry to discuss the title in terms of a DCP, as that is the deliverable to the cinema. The Picture Track File essence is compressed using JPEG 2000 and the Audio Track File carries a 24-bit linear PCM uncompressed multichannel WAV file. Encryption may optionally be applied to the essence of a track file to protect it from unauthorized use. The encryption used is AES 128-bit in CBC mode. In practice, there are two versions of composition in use. The original version is called Interop DCP. In 2009, a specification was published by SMPTE (SMPTE ST 429-2 Digital Cinema Packaging – DCP Constraints) for what is commonly referred to as SMPTE DCP. SMPTE DCP is similar but not backwards compatible with Interop DCP, resulting in an uphill effort to transition the industry from Interop DCP to SMPTE DCP. SMPTE DCP requires significant constraints to ensure success in the field, as shown by ISDCF. While legacy support for Interop DCP is necessary for commercial products, new productions are encouraged to be distributed in SMPTE DCP. == Technical specifications == The DCP root folder (in the storage medium) contains a number of files, some used to store the image and audio contents, and some other used to organize and manage the whole playlist. === Picture MXF files === Picture contents may be stored in one or more reels corresponding to one or more MXF files. Each reel contains pictures as MPEG-2 or JPEG 2000 essence, depending on the adopted codec. MPEG-2 is no longer compliant with the DCI specification. JPEG 2000 is the only accepted compression format. Supported frame rates are: SMPTE (JPEG 2000) 24, 25, 30, 48, 50, and 60 fps @ 2K 24, 25, and 30 fps @ 4K 24 and 48 fps @ 2K stereoscopic MXF Interop (JPEG 2000) – Deprecated 24 and 48 fps @ 2K (MXF Interop can be encoded at 25 frame/s but support is not guaranteed) 24 fps @ 4K 24 fps @ 2K stereoscopic MXF Interop (MPEG-2) – Deprecated 23.976 and 24 fps @ 1920 × 1080 Maximum frame sizes are 2048 × 1080 for 2K DC, and 4096 × 2160 for 4K DC. Common formats are: SMPTE (JPEG 2000) Flat (1998 × 1080 or 3996 × 2160), = 1.85:1 aspect ratio Scope (2048 × 858 or 4096 × 1716), ~2.39:1 aspect ratio HDTV (1920 × 1080 or 3840 × 2160), 16:9 aspect ratio (~1.78:1) (although not specifically defined in the DCI specification, this resolution is DCI compliant per section 8.4.3.2). Full (2048 × 1080 or 4096 × 2160) (~1.9:1 aspect ratio, official name by DCI is Full Container. Not widely accepted in cinemas.) MXF Interop (MPEG-2) – Deprecated Full Frame (1920 × 1080) 12 bits per component precision (36 bits total per pixel) XYZ' colorspace; the prime mark indicates gamma encoding (gamma=2.6) Maximum bit rate is 250 Mbit/s (1.3 MBytes per frame at 24 frame per second) === Sound MXF files === Sound contents are also stored in reels corresponding to picture reels in number and duration. In case of multilingual features, separate reels are required to convey different languages. Each file contains linear PCM essence. Sampling rate is 48,000 or 96,000 samples per second Sample precision of 24 bits Linear mapping (no companding) Up to 16 independent channels === Asset map file === List of all files included in the DCP, in XML format. === Composition playlist file === Defines the playback order during presentation. The order is saved in XML format in this file; each picture and sound reel is identified by its UUID. In the following example, a reel is composed by picture and sound: === Packing list file or package key list (PKL) === All files in the composition are hashed and their hash is stored here, in XML format. This file is generally used during ingestion in a digital cinema server to verify if data have been corrupted or tampered with in some way. For example, an MXF picture reel is identified by the following element: The hash value is the Base64 encoding of the SHA-1 checksum. It can be calculated with the command: openssl sha1 -binary "FILE_NAME" | openssl base64 === Volume index file === A single DCP may be stored in more than one medium (e.g., multiple hard disks). The XML file VOLINDEX is used to identify the volume order in the series. == 3D DCP == The DCP format is also used to store stereoscopic (3D) contents for 3D films. In this case, 48 frames exist for every second – 24 frames for the left eye, 24 frames for the right. Depending on the projection system used, the left eye and right eye pictures are either shown alternately (double or triple flash systems) at 48 fps or, on 4k systems, both left and right eye pictures are shown simultaneously, one above the other, at 24 fps. In triple flash systems, active shutter glasses are required whereas optical filtering such as circular polarisation is used in conjunction with passive glasses on polarized systems. Since the maximum bit rate is always 250 Mbit/s, this results in a net 125 Mbit/s for single frame, but the visual quality decrease is generally unnoticeable. == D-Box == D-Box codes for motion controlled seating (labelled as "Motion Data" in the DCP specification), if present, are stored as a monoaural WAV file on Sound Track channel 13. Motion Data tracks are unencrypted and not watermarked. == Creation == Most film producers and distributors rely on digital cinema encoding facilities to produce and quality control check a digital cinema package before release. Facilities follow strict guidelines set out in the DCI recommendations to ensure compatibility with all digital cinema equipment. For bigger studio release films, the facility will usually create a Digital Cinema Distribution Master (DCDM). A DCDM is the post-production step prior to a DCP. The frames are in XYZ TIFF format and both sound and picture are not yet wrapped into MXF files. A DCP can be encoded directly from a DCDM. A DCDM is useful for archiving purposes and also facilities can share them for international re-versioning purposes. They can easily be turned into alternative version DCPs for foreign territories. For smaller release films, the facility will usually skip the creation of a DCDM and instead encode directly from the Digital Source Master (DSM) the original film supplied to the encoding facility. A DSM can be supplied in a multitude of formats and color spaces. For this reason, the encoding facility needs to have extensive knowledge in color space handling including, on occasion, the use of 3D LUTs to carefully match the look of the finished DCP to a celluloid film print. This can be a highly involved process in which the DCP and the film print are "butterflied" (shown side by side) in a highly calibrated cinema. Less demanding DCPs are encoded from tape formats such as HDCAM SR. Quality control checks are always performed in calibrated cinemas and carefully checked for errors. QC checks are often attended by colorists, directors, sound mixers and other personnel to check for correct picture and sound reproduction in the finished DCP. == Accessibility == === Hearing impaired audio === A Hearing Impaired (HI) audio track is designed for people who are hearing-impaired to better hear dialog. Moviegoers can wear headphones which play this audio track synchronized with the film. Hearing Impaired audio is stored in the DCP on Sound Track channel 7. === Audio description === Audio description is narration for people who are blind or visually impaired. Audio description is stored in the DCP as "Visually Impaired-Native" (VI-N) audio on Sound Track channel 8. === Sign Language Video === A Sign Language Video track can be included in a DCP to allow for display of sign la

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