AI Coding Wiki

AI Coding Wiki — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Algorithmic bias

  Algorithmic bias describes systematic and repeatable harmful tendency in a computerized sociotechnical system to create "unfair" outcomes, such as "privileging" one category over another in ways that may or may not be different from the intended function of the algorithm. Bias can emerge from many factors, including intentionally biased design decisions or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms. This bias can have impacts ranging from privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity. The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination. This bias has only recently been addressed in legal frameworks, such as the European Union's General Data Protection Regulation (enforced in 2018) and the Artificial Intelligence Act (proposed in 2021 and adopted in 2024). As algorithms expand their ability to organize society, politics, institutions, and behavior, sociologists have become concerned with the ways in which unanticipated output and manipulation of data can impact the physical world. Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise (in part due to the psychological phenomenon of automation bias), and in some cases, reliance on algorithms can displace human responsibility for their outcomes, without last mile thinking. Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; by how features and labels are chosen; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design. Algorithmic bias has been cited in cases ranging from election outcomes to the spread of online hate speech. It has also arisen in criminal justice, healthcare, and hiring, compounding existing racial, socioeconomic, and gender biases. The relative inability of facial recognition technology to accurately identify darker-skinned faces has been linked to multiple wrongful arrests of black men, an issue stemming from imbalanced datasets. Problems in understanding, researching, and discovering algorithmic bias persist due to the proprietary nature of algorithms, which are typically treated as trade secrets. Even when full transparency is provided, the complexity of certain algorithms poses a barrier to understanding their functioning. Furthermore, algorithms may change, or respond to input or output in ways that cannot be anticipated or easily reproduced for analysis. In many cases, even within a single website or application, there is no single "algorithm" to examine, but a network of many interrelated programs and data inputs, even between users of the same service. A 2021 survey identified multiple forms of algorithmic bias, including historical, representation, and measurement biases, each of which can contribute to unfair outcomes. == Definitions == Algorithms are difficult to define, but may be generally understood as lists of instructions that determine how programs read, collect, process, and analyze data to generate a usable output. For a rigorous technical introduction, see Algorithms. Advances in computer hardware and software have led to an increased capability to process, store and transmit data. This has in turn made the design and adoption of technologies such as machine learning and artificial intelligence technically and commercially feasible. By analyzing and processing data, algorithms are the backbone of search engines, social media websites, recommendation engines, online retail, online advertising, and more. Contemporary social scientists are concerned with algorithmic processes embedded into hardware and software applications because of their political and social impact, and question the underlying assumptions of an algorithm's neutrality. The term algorithmic bias describes systematic and repeatable errors that create unfair outcomes, such as privileging one arbitrary group of users over others. For example, a credit score algorithm may deny a loan without being unfair, if it is consistently weighing relevant financial criteria. If the algorithm recommends loans to one group of users, but denies loans to another set of nearly identical users based on unrelated criteria, and if this behavior can be repeated across multiple occurrences, an algorithm can be described as biased. This bias may be intentional or unintentional (for example, it can come from biased data obtained from a worker that previously did the job the algorithm is going to do from now on). == Methods == Bias can be introduced to an algorithm in several ways. During the assemblage of a dataset, data may be collected, digitized, adapted, and entered into a database according to human-designed cataloging criteria. Next, programmers assign priorities, or hierarchies, for how a program assesses and sorts that data. This requires human decisions about how data is categorized, and which data is included or discarded. Some algorithms collect their own data based on human-selected criteria, which can also reflect the bias of human designers. Other algorithms may reinforce stereotypes and preferences as they process and display "relevant" data for human users, for example, by selecting information based on previous choices of a similar user or group of users. Beyond assembling and processing data, bias can emerge as a result of design. For example, algorithms that determine the allocation of resources or scrutiny (such as determining school placements) may inadvertently discriminate against a category when determining risk based on similar users (as in credit scores). Meanwhile, recommendation engines that work by associating users with similar users, or that make use of inferred marketing traits, might rely on inaccurate associations that reflect broad ethnic, gender, socio-economic, or racial stereotypes. Another example comes from determining criteria for what is included and excluded from results. These criteria could present unanticipated outcomes for search results, such as with flight-recommendation software that omits flights that do not follow the sponsoring airline's flight paths. Algorithms may also display an uncertainty bias, offering more confident assessments when larger data sets are available. This can skew algorithmic processes toward results that more closely correspond with larger samples, which may disregard data from underrepresented populations. == History == === Early critiques === The earliest computer programs were designed to mimic human reasoning and deductions, and were deemed to be functioning when they successfully and consistently reproduced that human logic. In his 1976 book Computer Power and Human Reason, artificial intelligence pioneer Joseph Weizenbaum suggested that bias could arise both from the data used in a program, but also from the way a program is coded. Weizenbaum wrote that programs are a sequence of rules created by humans for a computer to follow. By following those rules consistently, such programs "embody law", that is, enforce a specific way to solve problems. The rules a computer follows are based on the assumptions of a computer programmer for how these problems might be solved. That means the code could incorporate the programmer's imagination of how the world works, including their biases and expectations. While a computer program can incorporate bias in this way, Weizenbaum also noted that any data fed to a machine additionally reflects "human decision making processes" as data is being selected. Finally, he noted that machines might also transfer good information with unintended consequences if users are unclear about how to interpret the results. Weizenbaum warned against trusting decisions made by computer programs that a user doesn't understand, comparing such faith to a tourist who can find his way to a hotel room exclusively by turning left or right on a coin toss. Crucially, the tourist has no basis of understanding how or why he arrived at his destination, and a successful arrival does not mean the process is accurate or reliable. An early example of algorithmic bias resulted in as many as 60 women and ethnic minorities denied entry to St. George's Hospital Medical School per year from 1982 to 1986, based on implementation of a new computer-guidance assessment system that denied entry to women and men with "foreign-sounding names" based on historical trends in admissions. While many schools at the time employed similar biases in their selection process, St. George was most notable for automating said bias through the use of an algorithm, thus gaining the attention of people on a much

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  Comparison of user features of operating systems

  Comparison of user features of operating systems refers to a comparison of the general user features of major operating systems in a narrative format. It does not encompass a full exhaustive comparison or description of all technical details of all operating systems. It is a comparison of basic roles and the most prominent features. It also includes the most important features of the operating system's origins, historical development, and role. == Overview == An operating system (OS) is system software that manages computer hardware, software resources, and provides common services for computer programs. Time-sharing operating systems schedule tasks for efficient use of the system and may also include accounting software for cost allocation of processor time, mass storage, printing, and other resources. For hardware functions such as input and output and memory allocation, the operating system acts as an intermediary between programs and the computer hardware, although the application code is usually executed directly by the hardware and frequently makes system calls to an OS function or is interrupted by it. Operating systems are found on many devices that contain a computer – from cellular phones and video game consoles to web servers and supercomputers. As of June 2024, the dominant general-purpose desktop operating system is Microsoft Windows with a market share of around 72.91%. macOS by Apple Inc. is in second place (14.93%), and the varieties of Linux are collectively in third place (4.04%). In the mobile sector, including both smartphones and tablets, Android is dominant with a market share of 71%, followed by Apple's iOS with 28%; for smartphones alone, Android has 72% and iOS has 28%. Linux distributions are dominant in the server and supercomputing sectors. Other specialized classes of operating systems (special-purpose operating systems)), such as embedded and real-time systems, exist for many applications. Security-focused operating systems also exist. Some operating systems have low system requirements (i.e. light-weight Linux distribution). Others may have higher system requirements. Some operating systems require installation or may come pre-installed with purchased computers (OEM-installation), whereas others may run directly from media (i.e. live cd) or flash memory (i.e. USB stick). == MS-DOS == === Overview === MS-DOS (acronym for Microsoft Disk Operating System) is an operating system for x86-based personal computers mostly developed by Microsoft. Collectively, MS-DOS, its rebranding as IBM PC DOS, and some operating systems attempting to be compatible with MS-DOS, are sometimes referred to as "DOS" (which is also the generic acronym for disk operating system). MS-DOS was the main operating system for IBM PC compatible personal computers during the 1980s, from which point it was gradually superseded by operating systems offering a graphical user interface (GUI), in various generations of the graphical Microsoft Windows operating system. IBM licensed and re-released it in 1981 as PC DOS 1.0 for use in its PCs. Although MS-DOS and PC DOS were initially developed in parallel by Microsoft and IBM, the two products diverged after twelve years, in 1993, with recognizable differences in compatibility, syntax, and capabilities. During its lifetime, several competing products were released for the x86 platform, and MS-DOS went through eight versions, until development ceased in 2000. Initially, MS-DOS was targeted at Intel 8086 processors running on computer hardware using floppy disks to store and access not only the operating system, but application software and user data as well. Progressive version releases delivered support for other mass storage media in ever greater sizes and formats, along with added feature support for newer processors and rapidly evolving computer architectures. Ultimately, it was the key product in Microsoft's development from a programming language company to a diverse software development firm, providing the company with essential revenue and marketing resources. It was also the underlying basic operating system on which early versions of Windows ran as a GUI. == Microsoft Windows == === Overview === Microsoft Windows, commonly referred to as Windows, is a group of several proprietary graphical operating system families, all of which are developed and marketed by Microsoft. Each family caters to a certain sector of the computing industry. Active Microsoft Windows families include Windows NT and Windows IoT; these may encompass subfamilies, (e.g. Windows Server or Windows Embedded Compact) (Windows CE). Defunct Microsoft Windows families include Windows 9x, Windows Mobile, and Windows Phone. Microsoft announced an operating environment named Windows on 10 November 1983, as a graphical operating system shell for MS-DOS in response to the growing interest in graphical user interfaces (GUIs); Windows 1.0 first shipped on 20 November 1985. Microsoft Windows came to dominate the world's personal computer (PC) market with over 90% market share, overtaking Mac OS, which had been introduced in 1984, while Microsoft has in 2020 lost its dominance of the consumer operating system market, with Windows down to 30%, lower than Apple's 31% mobile-only share (65% for desktop operating systems only, i.e. "PCs" vs. Apple's 28% desktop share) in its home market, the US, and 32% globally (77% for desktops), where Google's Android leads. Apple came to see Windows as an unfair encroachment on their innovation in GUI development as implemented on products such as the Lisa and Macintosh (eventually settled in court in Microsoft's favor in 1993). As of January 2023, on PCs, Windows is still the most popular operating system in all countries. However, in 2014, Microsoft admitted losing the majority of the overall operating system market to Android, because of the massive growth in sales of Android smartphones. In 2014, the number of Windows devices sold was less than 25% that of Android devices sold. This comparison, however, may not be fully relevant, as the two operating systems traditionally target different platforms. Still, numbers for server use of Windows (that are comparable to competitors) show one third market share, similar to that for end user use. As of October 2020, the most recent version of Windows for PCs, tablets and embedded devices is Windows 10, version 20H2. The most recent version for server computers is Windows Server, version 20H2. A specialized version of Windows also runs on the Xbox One video game console. === Windows 95 === Windows 95 introduced a redesigned shell based around a desktop metaphor; File shortcuts (also known as shell links) were introduced and the desktop was re-purposed to hold shortcuts to applications, files and folders, reminiscent of Mac OS. In Windows 3.1 the desktop was used to display icons of running applications. In Windows 95, the currently running applications were displayed as buttons on a taskbar across the bottom of the screen. The taskbar also contained a notification area used to display icons for background applications, a volume control and the current time. The Start menu, invoked by clicking the "Start" button on the taskbar or by pressing the Windows key, was introduced as an additional means of launching applications or opening documents. While maintaining the program groups used by its predecessor Program Manager, it also displayed applications within cascading sub-menus. The previous File Manager program was replaced by Windows Explorer and the Explorer-based Control Panel and several other special folders were added such as My Computer, Dial Up Networking, Recycle Bin, Network Neighborhood, My Documents, Recent documents, Fonts, Printers, and My Briefcase among others. AutoRun was introduced for CD drives. The user interface looked dramatically different from prior versions of Windows, but its design language did not have a special name like Metro, Aqua or Material Design. Internally it was called "the new shell" and later simply "the shell". The subproject within Microsoft to develop the new shell was internally known as "Stimpy". In 1994, Microsoft designers Mark Malamud and Erik Gavriluk approached Brian Eno to compose music for the Windows 95 project. The result was the six-second start-up music-sound of the Windows 95 operating system, The Microsoft Sound and it was first released as a startup sound in May 1995 on Windows 95 May Test Release build 468. When released for Windows 95 and Windows NT 4.0, Internet Explorer 4 came with an optional Windows Desktop Update, which modified the shell to provide several additional updates to Windows Explorer, including a Quick Launch toolbar, and new features integrated with Internet Explorer, such as Active Desktop (which allowed Internet content to be displayed directly on the desktop). Some of the user interface elements introduced in Windows 95, such as the desktop, taskbar, Start menu and Windows

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  Packingham v. North Carolina

  Packingham v. North Carolina, 582 U.S. 98 (2017), is a case in which the Supreme Court of the United States held that a North Carolina statute that prohibited registered sex offenders from using social media websites was unconstitutional because it violated the First Amendment to the U.S. Constitution, which protects freedom of speech. In 2010, Lester Gerard Packingham, a registered sex offender, posted on Facebook under a pseudonym to comment favorably on a recent traffic court experience. Police then identified Packingham and charged him with violating North Carolina's law. Packingham moved to dismiss the charges, arguing that the state's law violated the First Amendment. The trial court dismissed this motion and ultimately convicted Packingham. A state appellate court initially reversed the trial court, holding that the law did violate the First Amendment, but the North Carolina Supreme Court, the state's highest court, disagreed and reinstated the conviction. In June 2017, the U.S. Supreme Court unanimously reversed the North Carolina Supreme Court's judgment. In the majority opinion authored by Justice Anthony Kennedy, the Court held that social media—defined broadly to include Facebook, Amazon.com, The Washington Post, and WebMD, among many others—is a "protected space" under the First Amendment for lawful speech. The Court offered that North Carolina could protect children through less restrictive means, such as prohibiting "conduct that often presages a sexual crime, like contacting a minor or using a website to gather information about a minor". == Background == === North Carolina statute === In 2008, the state of North Carolina passed a law that made it a felony for a registered sex offender "to access a commercial social networking Web site where the sex offender knows that the site permits minor children to become members or to create or maintain personal Web pages". The law defined a "commercial social networking Web site" using four criteria. Specifically, the website must: be "operated by a person who derives revenue from membership fees, advertising, or other sources related to the operation of the Web site". facilitate "the social introduction between two or more persons for the purposes of friendship, meeting other persons, or information exchanges". allow "users to create Web pages or personal profiles that contain information such as the name or nickname of the user, photographs placed on the personal Web page by the user, other personal information about the user, and links to other personal Web pages on the commercial social networking Web site of friends or associates of the user that may be accessed by other users or visitors to the Web site". provide "users or visitors... mechanisms to communicate with other users, such as a message board, chat room, electronic mail, or instant messenger". The law exempted websites that "Provid[e] only one of the following discrete services: photo-sharing, electronic mail, instant messenger, or chat room or message board platform", as well as websites that have as their primary purpose "the facilitation of commercial transactions involving goods or services between [their] members or visitors". === Facts of the case === In 2002, Lester Gerard Packingham was convicted of taking "indecent liberties with a child", a felony that required him to register as a sex offender. A North Carolina court sentenced him to 10–12 months in prison with 24 months of supervised release. He was given no other special instructions on his behavior outside of prison other than to "remain away from" the minor. In 2010, after a state court dismissed a traffic ticket against Packingham, he submitted a post on Facebook under the name "J. R. Gerrard", stating: "Man God is Good! How about I got so much favor they dismissed the ticket before court even started? No fine, no court cost, no nothing spent. . . . . .Praise be to GOD, WOW! Thanks JESUS!" The Durham Police Department identified Packingham as the author of the post after cross-checking the time of the post with recently dismissed traffic tickets, and a grand jury indicted him for violating the North Carolina statute. === Lower court proceedings === Initially, Packingham moved to dismiss his indictment, arguing that it violated the First Amendment. A North Carolina Superior Court judge denied this motion, and he was convicted of violating the North Carolina social media law. Packingham appealed his conviction to the North Carolina Court of Appeals, which reversed the trial court's decision in 2013. Applying intermediate scrutiny, the court of appeals determined that North Carolina's law violated the First Amendment because it was too broad, applying to all registered sex offenders regardless of whether the offender had committed a crime involving a minor or whether the offender was a continuing threat to minors. The appeals court also stated that the law had been defined broadly enough to prohibit a registered sex offender from conducting a wide array of Internet activity, such as "conducting a 'Google' search, purchasing items on Amazon.com, or accessing a plethora of Web sites unrelated to online communication with minors". In 2015, the North Carolina Supreme Court, the state's highest court, reversed the court of appeals, holding that the law was "constitutional in all respects". The North Carolina Supreme Court found that the statute was a "limitation on conduct" and did not impede any free speech. The state had a vested interest in “forestalling the illicit lurking and contact of minors” by registered sex offenders and potential future victims, and upheld Packingham's conviction. == Supreme Court ruling == Packingham filed a petition for a writ of certiorari with the Supreme Court of the United States. The federal government also filed a brief recommending that the Supreme Court grant certiorari, arguing that the North Carolina Supreme Court incorrectly decided the case in favor of the state. The U.S. Supreme Court granted certiorari in October 2016. Amicus briefs in support of Packingham were filed by the libertarian Cato Institute and the American Civil Liberties Union. The North Carolina Supreme Court filed a brief supporting its prior decision, urging the importance of protecting minors from being stalked online. === Oral argument === The oral argument took place in February 2017. Packingham’s lawyer, David T. Goldberg, argued that the law banned “vast swaths of First Amendment activity”, went too far in restricting which Internet sites could be accessed, and forbade use of the Internet in general. The law targeted speech on some of the platforms that Americans use most often, Goldberg noted, and that under the law Packingham could not even use Twitter to read the myriad messages discussing his own case. He further noted that the law imposes punishment without regard to whether the offender actually did anything wrong. North Carolina’s senior deputy Attorney General, Robert C. Montgomery, argued for the state, and claimed that communication through social media sites is a “crucial channel”. Justice Sonia Sotomayor asked Montgomery to provide evidence as to the claim that by giving Packingham Internet privileges, he would commit another crime. Justice Stephen Breyer added that “It seems to be well-settled law that the state can’t (bar usage) unless there is a 'clear and present danger'." === Opinion of the Court === In June 2017 the Supreme Court delivered a judgment in favor of Packingham, unanimously voting to reverse the state court's ruling. Justice Anthony Kennedy authored the decision, joined by Justice Ginsburg, Justice Breyer, Justice Sotomayor, and Justice Kagan. Kennedy explained the decision: "A fundamental principle of the First Amendment is that all persons have access to places where they can speak and listen, and then, after reflection, speak and listen once more." He continued that "By prohibiting sex offenders from using those websites, North Carolina with one broad stroke bars access to what for many are the principal sources for knowing current events, checking ads for employment, speaking and listening in the modern public square, and otherwise exploring the vast realms of human thought and knowledge." Citing Ashcroft v. Free Speech Coalition as a precedent, Kennedy also wrote: "It is well established that, as a general rule, the Government 'may not suppress lawful speech as the means to suppress unlawful speech'." === Concurring opinion === Justice Samuel Alito wrote an opinion concurring in the judgment, joined by John Roberts and Clarence Thomas. While Alito agreed that the state statute at issue violated the First Amendment, he noted that there are reasonable scenarios for which legal bans for sex offenders can be placed, such as for sites targeted at teenagers. Justice Gorsuch took no part in the decision of the case. == Impact == Packingham v. North Carolina was one of the first U.S. Supreme Court cases to ana

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  Pridgen v University of Calgary

  Pridgen v University of Calgary was freedom of speech case which took place in Alberta, Canada, in 2010. The case deals with two university students, Keith and Steven Pridgen, who were found guilty and punished by the University of Calgary in 2008, on grounds of "non-academic misconduct". The University of Calgary defines "non-academic misconduct" as:(a) conduct which causes injury to a person and/or damage to University property and/or the property of any member of the University community; (b) unauthorized removal and/or unauthorized possession of University property; and (c) conduct which seriously disrupts the lawful educational and related activities of other students and/or University staff.The Court of the Queen's Bench of Alberta found the University of Calgary to be wrong in prosecuting ten students, including the Pridgen brothers, in regards to comments made about a professor on Facebook. The key ruling in this case was that the universities are not exempt from, and that these students were in fact protected under, section 2(b) of the Charter of Rights and Freedoms. This case is notable as it highlights the jurisdiction of the Charter in terms of both new media technologies and university institutions in Canada. == Background == Keith and Steven Pridgen were undergraduate students at the University of Calgary in 2008. The twin brothers shared a Law and Society class being taught by Aruna Mitra. Professor Mitra was teaching this class for the first time in her career, and many of the students were very critical of her knowledge of the course. A Facebook page entitled “I NO Longer Fear Hell, I Took a Course with Aruna Mitra” was created, and many students began posting comments. In particular, Steven Pridgen's comment on November 13, 2007, read: “Somehow I think she just got lazy and gave everybody a 65....that's what I got. Does anybody know how to apply to have it remarked?” Many students had similar concerns to Pridgen's and after having their work re-marked, a number of them did in fact receive higher grades. Keith Pridgen also commented on August 26, 2008: “Hey fellow LWSO. Homees.. So I am quite sure Mitra is NO LONGER TEACHING ANY COURSES WITH THE U OF C !!!!! Remember when she told us she was a long-term professor? Well, Actually she was only sessional and picked up our class at the last moment because another prof wasn't able to do it ...lucky us. Well, anyways I think we should all congratulate ourselves for leaving a Mitra-free legacy for future students!” On September 4, 2008, Aruna Mitra complained about the Facebook page to the Interim Dean of the Faculty of Communication and Culture at the University of Calgary. Dean Tettey called a meeting for the ten students who posted material about Mitra on the Facebook page. The meeting took place on September 18, 2008, and included four professors from the department as well as the Dean. At this meeting, all ten students, including the Pridgen brothers, were found guilty of non-academic misconduct. On November 20, 2008, the Appellant's received a letter from Dean Tettey advising them that their comments “clearly caused unwarranted professional and personal injury to Prof. Mitra and clearly meets the criteria for non-academic misconduct as outlined in the University of Calgary Calendar”. Keith Pridgen was put on probation for 24 months, and both brothers were required to write a letter of apology to Prof. Mitra and refrain from posting or circulating defamatory material regarding any faculty members of the University of Calgary. The Pridgen brothers appealed the decision to the University of Calgary Review Committee and later to the Board of Governors of the University of Calgary however neither of these attempts succeeded in having the decision overturned. == Opinion of the Court == Eight main issues to be determined were laid out by the Honourable Madam Justice J. Strekaf: (a) Does the Charter apply to the disciplinary proceedings taken by the Respondent; (b) If, so were the Applicants' Charter rights infringed; (c) Were the actions taken by the University ultra vires the jurisdiction of the Province of Alberta; (d) Did the Board of Governors err in refusing to hear the Applicants appeals; (e) Were the Applicants' denied a fair hearing; (f) Did the Review Committee provide adequate reasons for its decisions; (g) Did the Review Committee err in concluding that the activities of the Applicants constituted non-academic misconduct; and (h) What, if any, remedy should be granted to the Applicants. The Court determined from previous cases that "a non-government entity may still be subject to the Charter of Rights and freedoms when implementing a specific government policy or program". Justice Strekaf distinguished that the University was acting as agent of the provincial government in providing accessible post-secondary education services to students in Alberta pursuant to the provisions of the PSL Act. Justice Strekaf felt there was sufficient evidence to show that universities in Alberta have some level of reliance on government funds and therefore they are not a "Charter free zone". Justice Strekaf concluded that comments made by Keith and Steven Pridgen, regarding Professor Mitra, on Facebook did not constitute academic misconduct and the Pridgen brothers' right to freedom of expression, under section 2(b) of the Charter, was infringed by the University of Calgary Review Committee.

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  YaDICs

  YaDICs is a program written to perform digital image correlation on 2D and 3D tomographic images. The program was designed to be both modular, by its plugin strategy and efficient, by it multithreading strategy. It incorporates different transformations (Global, Elastic, Local), optimizing strategy (Gauss-Newton, Steepest descent), Global and/or local shape functions (Rigid-body motions, homogeneous dilatations, flexural and Brazilian test models)... == Theoretical background == === Context === In solid mechanics, digital image correlation is a tool that allows to identify the displacement field to register a reference image (called herein fixed image) to images during an experiment (mobile image). For example, it is possible to observe the face of a specimen with a painted speckle on it in order to determine its displacement fields during a tensile test. Before the appearance of such methods, researchers usually used strain gauges to measure the mechanical state of the material but strain gauges only measure the strain on a point and don't allow to understand material with an heterogeneous behavior. One can obtain a full in plane strain tensor by derivation of the displacement fields. Many methods are based upon the optical flow. In fluid mechanics a similar method is used, called Particle Image Velocimetry (PIV); the algorithms are similar to those of DIC but it is impossible to ensure that the optical flow is conserved so a vast majority of the software used the normalized cross correlation metric. In mechanics the displacement or velocity fields are the only concern, registering images is just a side effect. There is another process called image registration using the same algorithms (on monomodal images) but where the goal is to register images and thereby identifying the displacement field is just a side effect. YaDICs uses the general principle of image registration with a particular attention to the displacement fields basis. === Image registration principle === YaDICs can be explained using the classical image registration framework: === Image registration general scheme === The common idea of image registration and digital image correlation is to find the transformation between a fixed image and a moving one for a given metric using an optimization scheme. While there are many methods to achieve such a goal, Yadics focuses on registering images with the same modality. The idea behind the creation of this software is to be able to process data that comes from a μ-tomograph; i.e.: data cube over 10003 voxels. With such a size it is not possible to use naive approach usually used in a two-dimensional context. In order to get sufficient performances OpenMP parallelism is used and data are not globally stored in memory. As an extensive description of the different algorithms is given in. === Sampling === Contrary to image registration, Digital Image Correlation targets the transformation, one wants to extracted the most accurate transformation from the two images and not just match the images. Yadics uses the whole image as a sampling grid: it is thus a total sampling. === Interpolator === It is possible to choose between bilinear interpolation and bicubic interpolation for the grey level evaluation at non integer coordinates. The bi-cubic interpolation is the recommended one. === Metrics === ==== Sum of squared differences (SSD) ==== The SSD is also known as mean squared error. The equation below defines the SSD metric: S S D ( μ , I F , I M ) = 1 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) 2 , {\displaystyle SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {1}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)^{2},} where I F {\displaystyle {\mathcal {I_{F}}}} is the fixed image, I M {\displaystyle {\mathcal {I_{M}}}} the moving one, Ω F {\displaystyle \Omega _{F}} the integration area | Ω F | {\displaystyle \left|\Omega _{F}\right|} the number of pi(vo)xels (cardinal) and T μ {\displaystyle {T}_{\mu }} the transformation parametrized by μ The transformation can be written as: T μ ( x ) = x + { Φ ( x ) } t { μ } . {\displaystyle T_{\mu }(x)=x+\left\{\Phi (x)\right\}^{t}\left\{\mu \right\}.} This metric is the main one used in the YaDICs as it works well with same modality images. One has to find the minimum of this metric ==== Normalized cross-correlation ==== The normalized cross-correlation (NCC) is used when one cannot assure the optical flow conservation; it happens in case of change of lighting or if particles disappear from the scene can occur in particle images velocimetry (PIV). The NCC is defined by: N C C ( μ , I F , I M ) = ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) ( I M ( T μ ( x i ) ) − I M ¯ ) ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) 2 ∑ x i ∈ Ω F ( I M ( T μ ( x i ) ) − I M ¯ ) 2 , {\displaystyle NCC(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)}{\sqrt {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)^{2}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)^{2}}}},} where I F ¯ {\displaystyle {\overline {\mathcal {I_{F}}}}} and I M ¯ {\displaystyle {\overline {\mathcal {I_{M}}}}} are the mean values of the fixed and mobile images. This metric is only used to find local translation in Yadics. This metric with translation transform can be solved using cross-correlation methods, which are non iterative and can be accelerated using Fast Fourier Transform . === Classification of transformations === There are three categories of parametrization: elastic, global and local transformation. The elastic transformations respect the partition of unity, there are no holes created or surfaces counted several times. This is commonly used in Image Registration by the use of B-Spline functions and in solid mechanics with finite element basis. The global transformations are defined on the whole picture using rigid body or affine transformation (which is equivalent to homogeneous strain transformation). More complex transformations can be defined such as mechanically based one. These transformations have been used for stress intensity factor identification by and for rod strain by. The local transformation can be considered as the same global transformation defined on several Zone Of Interest (ZOI) of the fixed image. ==== Global ==== Several global transforms have been implemented: Rigid and homogeneous (Tx,Ty,Rz in 2D; Tx,Ty,Tz,Rx,Ry,Rz,Exx,Eyy,Ezz,Eyz,Exz,Exy in 3D) Brazilian (Only in 2D), Dynamic Flexion, ==== Elastic ==== First-order quadrangular finite elements Q4P1 are used in Yadics. ===== Local ===== Every global transform can be used on a local mesh. === Optimization === The YaDICs optimization process follows a gradient descent scheme. The first step is to compute the gradient of the metric regarding the transform parameters ∂ S S D ( μ , I F , I M ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ∂ I M ( T μ ( x i ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ( ∂ T μ ( x i ) ∂ μ ) t ∂ I M ( T μ ( x i ) ) ∂ x {\displaystyle {\begin{array}{lcl}{\dfrac {\partial SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right){\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i})}{\partial \mu }}\\&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)\left({\dfrac {\partial {T}_{\mu }(x_{i})}{\partial \mu }}\right)^{t}{\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i}))}{\partial x}}\\\end{array}}} ==== Gradient method ==== Once the metric gradient has been computed, one has to find an optimization strategy The gradient method principle is explained below: μ k + 1 = μ k + α k d k {\displaystyle \mu _{k+1}=\mu _{k}+\alpha _{k}d_{k}} The gradient step can be constant or updated at every iteration. d k = − γ k ∂ C ( μ , I F , I M ) ∂ μ {\displaystyle d_{k}=-\gamma _{k}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}} , γ k {\displaystyle \gamma _{k}} allows one to choose between the following methods : γ k {\displaystyle \gamma _{k}} ⟹ {\displaystyle \Longrightarrow } steepest descent, γ k = [ ∂ C ( μ , I F , I M ) ∂ μ ∂ C ( μ , I F , I M ) ∂ μ t ] − 1 {\displaystyle \gamma _{k}=\left[{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}^{t}\right]^{-1}} ⟹ {\displaystyle \Longrightarrow } Gauss-Newto

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  Nanonetwork

  A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometers at most in size) which are able to perform only very simple tasks such as computing, data storing, sensing and actuation. Nanonetworks are expected to expand the capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information. Nanonetworks enable new applications of nanotechnology in the biomedical field, environmental research, military technology and industrial and consumer goods applications. Nanoscale communication is defined in IEEE P1906.1. == Communication approaches == Classical communication paradigms need to be revised for the nanoscale. The two main alternatives for communication in the nanoscale are based either on electromagnetic communication or on molecular communication. === Electromagnetic === This is defined as the transmission and reception of electromagnetic radiation from components based on novel nanomaterials. Recent advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas. From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power for a given input energy, amongst others. For the time being, two main alternatives for electromagnetic communication in the nanoscale have been envisioned. First, it has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nanoradio, i.e., an electromechanically resonating carbon nanotube which is able to decode an amplitude or frequency modulated wave. Second, graphene-based nano-antennas have been analyzed as potential electromagnetic radiators in the terahertz band. === Molecular === Molecular communication is defined as the transmission and reception of information by means of molecules. The different molecular communication techniques can be classified according to the type of molecule propagation in walkaway-based, flow-based or diffusion-based communication. In walkway-based molecular communication, the molecules propagate through pre-defined pathways by using carrier substances, such as molecular motors. This type of molecular communication can also be achieved by using E. coli bacteria as chemotaxis. In flow-based molecular communication, the molecules propagate through diffusion in a fluidic medium whose flow and turbulence are guided and predictable. The hormonal communication through blood streams inside the human body is an example of this type of propagation. The flow-based propagation can also be realized by using carrier entities whose motion can be constrained on the average along specific paths, despite showing a random component. A good example of this case is given by pheromonal long range molecular communications. In diffusion-based molecular communication, the molecules propagate through spontaneous diffusion in a fluidic medium. In this case, the molecules can be subject solely to the laws of diffusion or can also be affected by non-predictable turbulence present in the fluidic medium. Pheromonal communication, when pheromones are released into a fluidic medium, such as air or water, is an example of diffusion-based architecture. Other examples of this kind of transport include calcium signaling among cells, as well as quorum sensing among bacteria. Based on the macroscopic theory of ideal (free) diffusion the impulse response of a unicast molecular communication channel was reported in a paper that identified that the impulse response of the ideal diffusion based molecular communication channel experiences temporal spreading. Such temporal spreading has a deep impact in the performance of the system, for example in creating the intersymbol interference (ISI) at the receiving nanomachine. In order to detect the concentration-encoded molecular signal two detection methods named sampling-based detection (SD) and energy-based detection (ED) have been proposed. While the SD approach is based on the concentration amplitude of only one sample taken at a suitable time instant during the symbol duration, the ED approach is based on the total accumulated number of molecules received during the entire symbol duration. In order to reduce the impact of ISI a controlled pulse-width based molecular communication scheme has been analysed. The work presented in showed that it is possible to realize multilevel amplitude modulation based on ideal diffusion. A comprehensive study of pulse-based binary and sinus-based, concentration-encoded molecular communication system have also been investigated.

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  RR Media

  RR Media was a NASDAQ listed provider of global digital media services to the broadcast industry and content owners. Its services can be divided into four main groups: global content distribution network (satellite, fiber and the internet); content management & playout; sports, news & live events; and online video services. The company was rebranded to RR Media from RRsat in September 2014. In February 2016, it was announced that, subject to regulatory approvals, RR Media was to be acquired by SES, based in Betzdorf, Luxembourg, and merged with SES subsidiary company, SES Platform Services a media services provider for television broadcasters, production companies and platform operators, based in Unterföhring near Munich, Germany. In July 2016, the merged company was named MX1. == Digital media services == Global content distribution services RR Media's global distribution network uses a combination of satellite, fiber and the internet. The network includes satellite downlink and uplink; fiber connectivity to digital media hubs; connectivity to TV service providers; and internet-based content delivery. RR Media's network delivers live television channels, streaming media and Video on demand (VOD) content in all formats including Standard-definition television (SD), High-definition television (HD), 4K resolution (4K) & 3D television (3D). End-to-end content management & playout services RR Media manages, prepares and plays out content from its media centers. Services include: content preparation (digitization, localization, conversion, ingest, multiple formatting, editing, restoration); content management (digital asset management, media ingest and library, streamlined workflows, metadata curation, Video on demand (VOD) delivery) and playout, channel creation, playlist management, advertising insertion/management, graphics, titles & overlay, live events operations). RR Media also creates branded or white label product television channels using live and archived materials. Sports, news & live events RR Media delivers live sports and event content for sports rights holders, broadcasters and news channels. Services include: live production (Outside broadcasting vans, Satellite news gathering (SNG), studios), global live distribution, sports content preparation and content management, playout and origination.RR Media provides downlink, uplink, simultaneous translation, turnaround and live production services for sports events like football, basketball, tennis and golf, news and entertainment channels. Online video services RR Media converts existing and archive content into programs, channels and other digital assets, and converges broadcast and internet delivery. Services include converged media (preparing content for broadcast or online use) Content Management Systems (CMS), VOD services, branded platforms, multi-screen delivery, web video portals and viewer measurement tools (using digital analytics). == Media centers == RR Media's media centers are based in Hawley, PA (USA), Emeq Ha’Ela (Israel) Bucharest (Romania), with another facility opened in London, (UK) in June 2015. An additional facility in Miami, FL United States was announced in April 2016. The centers provide RR Media's services, including content preparation, management, online video, live content and distribution, and 24/7 service and support. == Awards == In November 2014, RR Media won the award for Achievement in Legacy Content at the 2014 TVB Europe awards in London, in recognition for its work with British Pathe and the restoration for YouTube. In February 2014, the World Teleport Association named Avi Cohen, CEO of RR Media (formerly RRsat), as its 2014 Teleport Executive of the Year. In 2009, the World Teleport Association awarded RR Media (then RRsat) the Independent Teleport Operator of the Year award for excellence. == History == RR Media (as RRsat) was established in 1981 as a communications provider. The company was founded by David Rivel, an electronics, computers and communications engineer. Rivel is CEO of the company for 31 years and from 2012 a Member of RR Media's board of directors. Under management of Rivel RRsat Communications Network Ltd. went public on 2006-11-01 - NASDAQ:RRST In 2014, the Company rebranded from RRsat Global Communications Network to RR Media. The rebrand was launched at the International Broadcasting Convention (IBC) Show in Amsterdam. In 2015, RR Media announced its NASDAQ stock ticker symbol change to RRM. == Acquisitions == In April 2015, RR Media acquired Eastern Space Systems (ESS) in Romania, a privately held provider of content management and content distribution services and related consulting services. In June 2015, RR Media acquired Satlink Communications as part of strategy to increase scale and expand its global content distribution network and content management footprint, strengthening its customer mix and leverage media industry expertise.

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  Digital asset

  A digital asset is anything that exists only in digital form and comes with a distinct usage right or distinct permission for use. Data that do not possess those rights are not considered assets. Digital assets include, but are not limited to: digital documents, audio content, motion pictures, and other relevant digital data currently in circulation or stored on digital appliances, such as personal computers, laptops, portable media players, tablets, data storage devices, and telecommunication devices. This encompasses any apparatus that currently exists or will exist as technology progresses to accommodate the conception of new modalities capable of carrying digital assets. This holds true regardless of the ownership of the physical device on which the digital asset is located. == Types == Types of digital assets include, but are not limited to: software, photography, logos, illustrations, animations, audiovisual media, presentations, spreadsheets, digital paintings, word documents, electronic mails, websites, and various other digital formats with their respective metadata. The number of different types of digital assets is exponentially increasing due to the rising number of devices that leverage these assets, such as smartphones, serving as conduits for digital media. In Intel's presentation at the 'Intel Developer Forum 2013,' they introduced several new types of digital assets related to medicine, education, voting, friendships, conversations, and reputation, among others. == Digital asset management system == A digital asset management (DAM) is an integrated structure that combines software, hardware, and/or other services to manage, store, ingest, organize, and retrieve digital assets. These systems enable users to find and use content when needed. == Digital asset metadata == Metadata is data about other data. Any structured information that defines a specification of any form of data is referred to as metadata. Metadata is also a claimed relationship between two entities, often used to establish connections or associations. Librarian Lorcan Dempsey says "Think of metadata as data which removes from a user (human or machine) the need to have full advance knowledge of the existence or characteristics of things of potential interest in the environment". At first, the term metadata was used for digital data exclusively, but nowadays metadata can apply to both physical and digital data. Catalogs, inventories, registers, and other similar standardized forms of organizing, managing, and retrieving resources contain metadata. Metadata can be stored and contained directly within the file it refers to or independently from it with the help of other forms of data management such as a DAM system. The more metadata is assigned to an asset the easier it gets to categorize it, especially as the amount of information grows. The asset's value rises the more metadata it has for it becomes more accessible, easier to manage, and more complex. Structured metadata can be shared with open protocols like OAI-PMH to allow further aggregation and processing. Open data sources like institutional repositories have thus been aggregated to form large datasets and academic search engines comprising tens of millions of open access works, like BASE, CORE, and Unpaywall. == Issues == Due to a lack of either legislation or legal precedent, there is limited existing governmental control and regulation surrounding digital assets in the United States and other large economies globally. Many of the control issues relating to access and transferability are maintained by individual companies. Some consequences of this include 'What is to become of the assets once their owner is deceased?' as well as can, and, if so, how, may they be inherited. This subject was broached in a bogus story about Bruce Willis allegedly looking to sue Apple as the end user agreement prevented him from bequeathing his iTunes collection to his children. Another case of this was when a soldier died on duty and the family requested access to the Yahoo! account. When Yahoo! refused to grant access, the probate judge ordered them to give the emails to the family but Yahoo! still was not required to give access. The Music Modernization Act was passed in September 2018 by the U.S. Congress to create a new music licensing system, with the aim to help songwriters get paid more.

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  Adrozek

  Adrozek is malware that injects fake ads into online search results. Microsoft announced the malware threat on 10 December 2020, and noted that many different browsers are affected, including Google Chrome, Microsoft Edge, Mozilla Firefox and Yandex Browser. The malware was first detected in May 2020 and, at its peak in August 2020, controlled over 30,000 devices a day. But during the December 2020 announcement, Microsoft claimed "hundreds of thousands" of infected devices worldwide between May and September 2020. According to Microsoft, if not detected and blocked, Adrozek adds browser extensions, modifies a specific DLL per target browser, and changes browser settings to insert additional, unauthorized ads into web pages, often on top of legitimate ads from search engines. For each user tricked into clicking on the fake ads, the scammers earn affiliate advertising dollars. The malware has been observed to extract device data and, in some cases, steal credentials, sending them to remote servers. Users may unintentionally install the malware because of a drive-by download, by visiting a tampered website, opening an e-mail attachment, or clicking on a deceptive link or a deceptive pop-up window. The main malware program is downloaded to the “Programs Files” folder using file names such as Audiolava.exe, QuickAudio.exe, and converter.exe. According to PC Magazine, a good way to avoid, or mitigate, infection by Adrozek is to keep browser and related software programs up to date.

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  BitClout

  BitClout was an open source blockchain-based social media platform. On the platform, users could post short-form writings and photos, award money to posts they particularly like by clicking a diamond icon, as well as buy and sell "creator coins" (personalized tokens whose value depends on people's reputations). BitClout ran on a custom proof of work blockchain, and was a prototype of what can be built on DeSo (short for "Decentralized Social"). BitClout's founder and primary leader is Nader al-Naji, known pseudonymously as "Diamondhands". Under development since 2019, BitClout's blockchain created its first block in January 2021, and BitClout itself launched publicly in March 2021. The platform launched with 15,000 "reserved" accounts — a move intended to prevent impersonation, but which backfired as some people with reserved accounts tried to actively distance themselves. Later, in September 2021, BitClout was revealed to be the flagship product of the DeSo blockchain. == History == === Origins (2019 - March 2021) === In early 2019, Nader al-Naji became interested in "mixing investing and social media". He started creating a custom blockchain in May 2019, but didn't tell anyone else until November 2020. However, in the fall of 2020, al-Naji pitched BitClout's own investors under his real name and began posting job listings for a "new operation". Although BitClout was not originally intended to launch until mid-2021, its development was sped up due to "zeitgeist about decentralized social media" in January 2021. BitClout's first block was mined on 18 January 2021. Its next block was mined on 1 March 2021. === As BitClout (March - September 2021) === In early March 2021, about fifty investors received links to a password-protected website with the BitClout white paper. They were encouraged to explore the site and send the same link to "two or three other 'trusted contacts'". Within weeks users were spending millions of dollars per day on the platform. The platform's founders said they were "completely unprepared", having planned to have a "soft-launch". The leader went by the name "diamondhands" on the platform. On 24 March 2021, BitClout launched out of private beta. Investors include Sequoia Capital, Andreessen Horowitz, the venture capital firm Social Capital, Coinbase Ventures, Winklevoss Capital Management, Alexis Ohanian, Polychain, Pantera, and Digital Currency Group (CoinDesk's parent company). During its initial launch, BitClout's currency could be bought with bitcoin, but not sold except on Discord servers or Twitter threads. A single bitcoin wallet related to BitClout received more than $165M worth of deposits. In March 2021, law firm Anderson Kill P.C. sent Nader al-Naji, the presumed leader of the BitClout platform, a cease-and-desist letter, demanding the removal of Brandon Curtis's account and alleging that BitClout violated sections 1798 and 3344 of the California Civil Code by using Curtis's name and likeness without his consent. Curtis also tweeted, "Adopting Bitcoin's aesthetic to raise VC funding to carry out unethical and blatantly illegal schemes like BitClout: not cool". (However, Curtis's coin, despite not being listed on the official website, can still be bought by users searching for the original username.) Additionally, in April 2021, Lee Hsien Loong asked for his name and photograph to be removed from the site, stating that he has "nothing to do with the platform" and that "it is misleading and done without [his] permission". On 18 May 2021, diamondhands announced that 100% of the BitClout code went public. On 12 June 2021, the supply of BitClout was capped at around 11 million coins. On 18 July 2021, BitClout added the ability for users to mint and purchase NFTs within the platform. === As part of DeSo (September 2021 - July 2024) === On 21 September 2021, it was revealed that BitClout was a prototype built on DeSo, short for "Decentralized Social". As a part of this revelation, diamondhands confirmed his identity as Nader al-Naji. (As early as April 2021, it had been believed that diamondhands indeed was that person.)The Bitclout project raised $200M in funding, which went to setting up the DeSo Foundation. === End and aftermath (July 2024 - present) === In July 2024, al-Naji was arrested by the FBI and charged with wire fraud involving BitClout. He also faced civil charges of securities fraud and unregistered offers and sales of securities from the Securities and Exchange Commission. In response, the official "deso" account posted that al-Naji was "safe and at home" and "that this experience has only reinforced [his] commitment to DeSo". In February 2025, the Justice Department dropped its case against al-Naji. In March 2026, the SEC voluntarily dismissed the enforcement case with prejudice. == Design == BitClout is a social media platform. Its users can post short-form writings and photos (similarly to Twitter). They can award money to posts they particularly like by clicking a diamond icon (similarly to Twitch Bits). The prices of each account's "creator coin" goes up and down with the popularity of the celebrity behind it. For example, if someone says something negative, the value of their corresponding account may go down. This price is computed automatically according to the formula p r i c e _ i n _ b i t c l o u t = .003 ∗ c r e a t o r _ c o i n s _ i n _ c i r c u l a t i o n 2 {\displaystyle price\_in\_bitclout=.003creator\_coins\_in\_circulation^{2}} . At launch time, BitClout scraped 15,000 profiles of celebrities from Twitter to create "reserved" accounts in their names. To claim a reserved account, the account holder would need to tweet about it (which also serves as a marketing strategy). At least 80 such reserved profiles have been claimed. Proof of stake was introduced in March 2024.

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  Creator economy

  The creator economy, also known as influencer economy, is a platform-driven economy in which creators produce content, products, or services and distribute them directly to their audience through social media platforms and emerging technologies. This economic model is based on the ability of creators to build and maintain communities of users, monetizing their creative activity through multiple channels including advertising, sponsorships, product sales, crowdfunding, and subscription-based services. Creators include various professional categories such as social media influencers, YouTubers, bloggers, artists, online educators, podcasters, and independent professionals, who use platforms as infrastructure to reach their audience without necessarily relying on traditional intermediaries in the cultural and media industry. According to Goldman Sachs Research, the ongoing growth of the creator economy will likely benefit companies that possess a combination of factors, including a large global user base, access to substantial capital, robust AI-powered recommendation engines, versatile monetization tools, comprehensive data analytics, and integrated e-commerce options. Examples of creator economy software platforms include YouTube, TikTok, Instagram, Facebook, Twitch, Spotify, Substack, OnlyFans and Patreon. == History == The term "creator" was coined by YouTube in 2011 to be used instead of "YouTube star", an expression that at the time could only apply to famous individuals on the platform. The term has since become omnipresent and is used to describe anyone creating any form of online content. A number of platforms such as TikTok, Snapchat, YouTube, and Facebook have set up funds with which to pay creators. == Criticism == The large majority of content creators derive no monetary gain for their creations, with most of the benefits accruing to the platforms who can make significant revenues from their uploads. As few as 0.1% of creators are able to earn a living through their channels.

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  Extremely online

  An extremely online (often capitalized), terminally online, or chronically online person is someone who is closely engaged with Internet culture. People said to be extremely online often believe that online posts are very important. Events and phenomena can themselves be extremely online; while often used as a descriptive term, the phenomenon of extreme online usage has been described as "both a reformation of the delivery of ideas – shared through words and videos and memes and GIFs and copypasta – and the ideas themselves". Here, "online" is used to describe "a way of doing things, not [simply] the place they are done". == Criteria == While the term was in use as early as 2014, it gained popularity over the latter half of the 2010s in conjunction with the increasing prevalence and notability of Internet phenomena in all areas of life. Extremely online people, according to The Daily Dot, are interested in topics "no normal, healthy person could possibly care about", and have been analogized to "pop culture fandoms, just without the pop". Extremely online phenomena such as fan culture and reaction GIFs have been described as "swallowing democracy" by journalists such as Amanda Hess in The New York Times, who claimed that a "great convergence between politics and culture, values and aesthetics, citizenship and commercialism" had become "a dominant mode of experiencing politics". Vulture – formerly the pop culture section of New York magazine, now a stand-alone website – has a section for articles tagged "extremely online". == Historical background == In the 2010s, many categories and labels came into wide use from media outlets to describe Internet-mediated cultural trends, such as the alt-right, the dirtbag left, and doomerism. These ideological categories are often defined by their close association with online discourse. For example, the term "alt-right" was added to the Associated Press' stylebook in 2016 to describe the "digital presence" of far-right ideologies, the dirtbag left refers to a group of "underemployed and overly online millennials" who "have no time for the pieties of traditional political discourse", and the doomer's "blackpilled despair" is combined with spending "too much time on message boards in high school" to produce an eclectic "anti-socialism". Extreme onlineness transcends ideological boundaries. For example, right-wing figures like Alex Jones and Laura Loomer have been described as "extremely online", but so have those on the left like Alexandria Ocasio-Cortez and fans of the Chapo Trap House podcast. Extremely online phenomena can range from acts of offline violence (such as the 2019 Christchurch shootings) to "[going] on NPR to explain the anti-capitalist irony inherent in kids eating Tide Pods". United States President Donald Trump's posts on social media have been frequently cited as extremely online, during both his presidency and his 2020 presidential campaign; Vox claimed his approach to re-election veered into being "Too Online", and Reason questioned whether the final presidential debate was "incomprehensible to normies". While individual people are often given the description, being extremely online has also been posited as an overall cultural phenomenon, applying to trends like lifestyle movements suffixed with "-wave" and "-core" based heavily on Internet media, as well as an increasing expectation for digital social researchers to have an "online presence" to advance in their careers. == Participants and media coverage == One example of a phenomenon considered to be extremely online is the "wife guy" (a guy who posts about his wife); despite being a "stupid online thing" which spent several years as a piece of Internet slang, in 2019 it became the subject of five articles in leading U.S. media outlets. Like many extremely online phrases and phenomena, the "wife guy" has been attributed in part to the in-character Twitter account dril. The account frequently parodies how people behave on the Internet, and has been widely cited as influential on online culture. In one tweet, his character refuses to stop using the Internet, even when someone shouts outside his house that he should log off. Many of dril's other coinages have become ubiquitous parts of Internet slang. Throughout the 2010s, posters such as dril inspired commonly used terms like "corncobbing" (referring to someone losing an argument and failing to admit it); while originally a piece of obscure Internet slang used on sites like Twitter, use of the term (and controversy over its misinterpretation) became a subject of reporting from traditional publications, with some noting that keeping up with the rapid turnover of inside jokes, memes, and quotes online required daily attention to avoid embarrassment. Twitch has been described as "talk radio for the extremely online". Another example of an event cited as extremely online is No Nut November. Increasingly, researchers are expected to have more of an online presence, to advance in their careers, as networking and portfolios continue to transition to the digital world. In November 2020, an article in The Washington Post criticized the filter bubble theory of online discourse on the basis that it "overgeneralized" based on a "small subset of extremely online people". The 2021 storming of the United States Capitol was described as extremely online, with "pro-Trump internet personalities", such as Baked Alaska, and fans livestreaming and taking selfies. People who have been described as extremely online include Chrissy Teigen, Jon Ossoff, and Andrew Yang. In contrast, Joe Biden has been cited as the antithesis of extremely online—The New York Times wrote in 2019 that he had "zero meme energy".

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  PCPaint

  PCPaint was one of the first IBM PC-based mouse-driven GUI paint programs, released in 1984. It followed after Microsoft Doodle, released in 1983 with the Microsoft Mouse version 1 drivers for DOS, and around the same time as Digital Research’s Draw program. It was developed and created by John Bridges and Doug Wolfgram. It was later developed into Pictor Paint. The hardware manufacturer Mouse Systems bundled PCPaint with millions of computer mice that they sold, making PCPaint one of the best-selling DOS-based paint programs of the mid 1980s. == History == In 1983, Doug Wolfgram bought a Microsoft Mouse and decided to write a drawing program for it. They named it “Mouse Draw”. The interface was primitive but the program functioned well. Wolfgram traveled to SoftCon in New Orleans where he demonstrated the program to Mouse Systems. Mouse Systems was developing an optical mouse and they wanted to bundle a painting program so they agreed to publish Mouse Draw. The original program was written entirely in assembly language with primitive graphics routines developed by Wolfgram. John Bridges worked for an educational software company, Classroom Consortia Media, Inc., developing and writing Apple and IBM graphics libraries for CCM's software. Bridges and Wolfgram were friends who had been connected through a bulletin board system developed and run by Wolfgram. The two collaborated cross country via the BBS, Wolfram in California and Bridges in New York. Mouse Systems wanted the paint program to capture the look and feel of MacPaint. John Bridges and Doug Wolfgram started reworking Mouse Draw into what became PCPaint. The program was completely re-written using Bridge's graphics library and the top-level elements were written in C rather than assembly language. Bridges developed the core graphics code for the first version of PCPaint while Wolfgram worked on the user interface and top-level code. Mouse Systems signed an exclusive agreement with Wolfgram's company, Microtex Industries, Inc., to bundle PCPaint with every mouse they sold. They began publishing PCPaint with their mice in 1984. Microsoft responded in 1985 by bundling a competing product, PC Paintbrush, with version 4 of its DOS drivers for the Microsoft Mouse, replacing its in-house Microsoft Doodle program which it published with version 1 of the DOS drivers in mid-1983. Microsoft’s mouse began to outsell Mouse Systems mouse. In November 1985 Microsoft bundled a cut-down version of PC Paintbrush with Windows 1.0 (called Microsoft Paint), later bundling an updated version of PC Paintbrush with Windows 3.0 (as Paintbrush), impacting PCPaint’s marketshare. In early 1987, Mouse Systems decided that PCPaint wasn't helping to sell mice any longer so they discontinued the bundle deal and returned rights to the code to MicroTex Industries, but retained rights to the name, PCPaint. Wolfgram then combined the paint program with a new animation system he was developing (called GRASP) and Paul Mace Software bought publishing rights to the animation system and PCPaint, which was to be renamed Pictor. Bridges again got involved and took over programming responsibilities for GRASP as well as PCPaint while Wolfgram focused on more of the business details. In creating the first version of PCPaint, Doug had a dual-floppy machine with a Computer Innovations compiler on one disk and source code on the other. John had the "luxury" of a 10MB hard disk in his XT. Data was exchanged daily via 1200, then 2400 baud modems. === Authorship and Ownership === John Bridges and Wolfgram continued to work on PCPaint and GRASP on behalf of Paul Mace Software until 1990. Also in that year, Doug Wolfgram sold his remaining rights to PCPaint (and its animation system, GRASP) to John Bridges. In 1994, GRASP development stopped and so did development of Pictor Paint. John Bridges terminated his GRASP publishing contract with Paul Mace Software, and went off to create GLPro (the next generation of GRASP) with GMEDIA. Along with GLPro, came GLPaint, the successor to PCPaint and Pictor Paint. == Versions == In June 1984, Mouse Systems shipped PCPaint 1.0, the first GUI based Paint program for the IBM PC family of computers. John Bridges and Doug Wolfgram, were the co-authors of PCPaint 1.0. PCPaint 1.0 saved its graphics in a modified BSaved image format with the extension of ".PIC". The release of PCPaint Version 1.5 followed in late 1984, with the additions of graphics image compression for the .PIC format and support for "larger-than-screen" images. PCjr support was also added in this version after overcoming severe memory shortage problems getting PCPaint to run on the 128k PCjr. October 1985 saw the release of PCPaint 2.0. EGA support and publishing features were added to this version. The .PIC format was further refined, offering support for the rapidly expanding graphics capabilities of the PC and efficient image compression. PCPaint 3.1 was released in 1989. Unlike previous versions, it was not bundled with mice but was sold as a stand-alone software product. PCPaint 3.1 offered improved text and image handling, provided 36 types of flood and fill, worked with VGA adapters in hi-res 16-color and 256-color modes, allowed the user to save and retrieve files in a variety of intercompatible formats (.PIC, .GIF, .PCX, .IMG), and printed selected portions of images on color or black-and-white dot matrix, ink jet, and laser printers such as PostScript and HP Laser Jet. PCPaint 3.1 is still in use today by some users of DOS emulation programs like DOSBox and available for free download. Pictor Paint was an improved version, written by John Bridges, and bundled with GRASP GRaphical System for Presentation also written by John Bridges. It was also called "The Painter's Easel". GLPaint, released in 1995, was the last in this series of paint programs written by John Bridges. By 1998 version 7.0 provided support for TrueColor images and the Pictor PIC format was expanded to handle these. == Pictor PIC Image Format == PCPaint 1.0 saved its graphics in a modified BSAVE image format (which was popular at the time) with the file type (extension) of ".PIC". By PCPaint 1.5 this format was extended further to accommodate image compression. With the release of version 2.0 the PICtor PIC image format was developed almost to its present state, with no similarity to the BSAVE format used by earlier versions. Pictor Paint saved its files in a compressed format with the file extension PIC, which was the same format used by PCPaint.

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  Media Block

  A Media Block or Integrated Media Block (IMB) is a component in a digital cinema projection system. Its purpose is to convert the Digital Cinema Package (DCP) content into data that ultimately produces picture and sound in a theater in compliance with DCI anti-piracy encryption requirements. == Terminology == DCI specification allows for two different security system architectures. In the first the Media Block is outside of the projector. This design is simply referred to as a "Media Block" and is typically a device attached directly to the motherboard of a Digital Cinema server. The media block is usually connected to the projector by dual-link SDI cables. Such media block is limited to processing 2K output, downscaling 4K DCPs if necessary. The second architecture describes an "Integrated Media Block". This refers to a device attached and integrated directly into the projector, which receives image data from the server, usually via a cat6 Ethernet connection. They can process 2K and 4K output. Some hardware implementations integrate the entire server on a single board and are able to work both as a MB as well as an IMB. == Security features == All security functions are contained within a Secure Processing Block (SPB), a tamper-proof physical device. Upon ingestion into a DCP server, Key Delivery Messages (KDM) are stored on flash memory in the media block or IMB. A KDM is written to enable the playback of a specific DCP during a specific time window and on a specific media block or IMB, identified by its serial number during the authoring process. Media blocks and IMBs also contain a secure clock that is set in the factory cannot be altered by the end user, which the DCP servers to which they are attached use to determine showtimes. The secure clock prevents theaters from showing encrypted movies outside the times authorized by the KDM (e.g. after it has expired) by simply changing the date and time in the server's BIOS. Media blocks and IMBs also typically include anti-tamper devices, designed to self-destruct the unit if unauthorized modification of its hardware, software or secure clock is attempted.

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  Web content development

  Web content development is the process of researching, writing, gathering, organizing, and editing information for publication on websites. Website content may consist of prose, graphics, pictures, recordings, movies, or other digital assets that could be distributed by a hypertext transfer protocol server, and viewed by a web browser. == Web developers and content developers == When the World Wide Web began, web developers either developed online content themselves, or modified existing documents and coded them into hypertext markup language (HTML). In time, the field of website development came to encompass many technologies, so it became difficult for website developers to maintain so many different skills. Content developers are specialized website developers who have content generation skills such as graphic design, multimedia development, professional writing, and documentation. They can integrate content into new or existing websites without using information technology skills such as script language programming and database programming. Content developers or technical content developers can also be technical writers who produce technical documentation that helps people understand and use a product or service. This documentation includes online help, manuals, white papers, design specifications, developer guides, deployment guides, release notes, etc. == Search engine optimization == Content developers may also be search engine optimization specialists, or internet marketing professionals. High quality, unique content is what search engines are looking for. Content development specialists, therefore, have a very important role to play in the search engine optimization process. One issue currently plaguing the world of web content development is keyword-stuffed content which are prepared solely for the purpose of manipulating search engine rankings. The effect is that content is written to appeal to search engine (algorithms) rather than human readers. Search engine optimization specialists commonly submit content to article directories to build their website's authority on any given topic. Most article directories allow visitors to republish submitted content with the agreement that all links are maintained. This has become a method of search engine optimization for many websites today. If written according to SEO copywriting rules, the submitted content will bring benefits to the publisher (free SEO-friendly content for a webpage) as well as to the author (a hyperlink pointing to his/her website, placed on an SEO-friendly webpage). == New content types == Web content is no longer restricted to text. Search engines now index audio/visual media, including video, images, PDFs, and other elements of a web page. Website owners sometimes use content protection networks to scan for plagiarized content.

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