AI App UI Design

AI App UI Design — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Cybersecurity in space

  Cybersecurity in space involves the defense of all space assets (e.g. navigation systems, satellites, ground antennas, networks, etc.). The security of space can be affected by attacks such as disruption, corruption as well as the destruction of depended-upon assets/collected data. Government (e.g. militaries) and non-government sectors (e.g. financial industries) have started to become more reliant on numerous space-based services. Due to the criticality of these services, space security experts have identified these assets as high-value targets (HVT) that can cause detrimental consequences to all of Earth. == Scope and definitions == Space assets are broken down by three sub-sectors: the space component, the ground component, and the individual user component. The architecture of space assets is extremely complex and allows for a frequent attack vector utilized, the disruption by radio frequency (RF) cyber-attacks. In 2020, a memorandum was published by President Donald Trump, Space Policy Directive‑5 (SPD‑5). It established principles to ensure the safeguarding of all space assets. In 2023, the National Institute of Standards and Technology’s (NIST) published IR 8270, Introduction to Cybersecurity for Commercial Satellite Operations. This report established a baseline risk-management framework (RMF) to be implemented into space operations. == History == During the Cold War in the 1950s-1960s, the United States and Russia entered what was called the “Space Race”. By 1957, the Soviet Union successfully launched the first satellite into space named Sputnik. By 1961, the first key milestone was accomplished when the Soviet Union’s Yuri Gagarin became the first human to orbit Earth. This was later followed by the first American, Alan Shepard, to be launched into space; this was followed by John Glenn becoming the first American to orbit Earth in 1962. In 1969, a pinnacle milestone was reached when Apollo 11 launched into space and Neil Armstrong became the first man to walk on the moon. As space operations furthered, Commercial off-the-shelf products became increasingly popular but resulted in a rapid increase to the cyber-attack surface. Public awareness of space security did not increase until 2022, when the Viasat KA-SAT incident occurred, resulting in the disruption of a large number of modems across Europe. The attack was later accredited to Russia by the U.S. and the U.K. Policy and standards started to rapidly increase by 2020. The establishment of SPD-5 was released in 2020 followed by asset hardening instructions in 2022, and NIST’s IR 8270 in 2023. It was not until 2025 that Europe published their own findings in the Space Threat Landscape 2025 Report. This document led to the EU’s security proposals and standards. == Threats == === Radio-frequency Interference and Global Navigation Satellite Systems (GNSS) Spoofing === Space services are highly dependent on RF links for systems such as GNSS, however, a consequence of this dependency on RF is denial of service and deception. In 2017, the Black Sea maritime event occurred when numerous ships were subject to spoofing. Space services depend on RF links susceptible to jamming (denial) and spoofing (deception), including for GNSS/Positioning, Navigation, and Timing (PNT). Annotated incidents include the 2017 Black Sea maritime spoofing event affecting numerous ships, and extensive aviation GNSS spoofing patterns surveyed in various regions during 2024–2025. === Network intrusion and malware === Cyber threats can intrude and infect assets with malware. They do this by finding misconfiguration vulnerabilities, remote-management interfaces, and/or supply-chain vulnerabilities mainly in ground networks and user terminals. When KA-SAT occurred, it resulted from bulk modem disturbances. Forensic analysts later suggested malicious management controls and wiper malware as the root cause. === Supply-chain and lifecycle risks === The outsource of COTS components, external vendors, and software defined payloads allowed for vulnerabilities to emerge in the System/Product Lifecycle. In response, EU recommended the implementation of lifecycle-wide controls as mitigating factors. === Espionage, disruption, and influence === As Advanced Persistent Threats (APTs), Global Positioning System (GPS) intervention, and information warfare increased, assets like transponders became more frequent targets of attack. == Noteworthy incidents == The Viasat KA‑SAT incident of 2022, where a large number of modems in Europe were disrupted, resulted in the loss of telemetry access to a significant amount of wind turbines in Germany. The mass GNSS deception of the Black Sea in 2017 affected numerous ships when they started to convey fake central locations in Russia. Between 2024 and 2025, there was a mass, repetitive aviation GNSS spoofing that affected the aircraft of various regions. == Standards, guidelines, and best practices == SPD‑5 (U.S.) – This established risk-based engineering, verifying and ensuring positive control, and the implementation of risk mitigation controls. NIST IR 8270 – This created a RMF for COTS satellites. CISA/FBI SATCOM Advisory (AA22‑076) – Provided guidance on hardening techniques such as least-privileged, access control, encryption, etc.). ENISA Space Threat Landscape 2025 – It established the categorization of assets to organize threats, ensuring the observation of system/product lifecycle, and an RMF for COTS satellites. ECSS‑E‑ST‑80C (2024) – This established a standard for securing lifecycles in space, covering all segments (e.g. ground, launch, etc.). == Regulation and governance == As of 2025, there is no international regulations established for space assets, but the U.S., EU, and ESA institutional initiatives have published standards to address security concerns. The U.S. implemented SPD-5 and the Federal Communications Commission (FCC); the FCC addressed orbital debris. While the EU created standards to address technological mandates and support the implementation of NIS2. Lastly, the ESA created a special operations center to safeguard their satellites. International governance is still evolving, but forums have been held by the United Nations Committee on the Peaceful Uses of Outer Space. International conversations under forums such as the UN Committee on the Peaceful Uses of Outer Space (COPUOS) progressively note the cyber–space safety relationship, though formal global norms specific to space cybersecurity continue evolving. == Risk management approaches == Through RMF, mitigation controls have been implemented to reduce the risk of exploitation while increasing the security of space. Controls addressing mitigation include proper configuration, system hardening, zero-trust architectures, encryption, etc. Both the government and industries have placed an emphasis on incident response procedures to identify, contain, and remediate breaches.

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  Information explosion

  Information explosion is the rapid increase in the amount of published information or data and the effects of this abundance. As the amount of available data grows, the problem of managing the information becomes more difficult, which can lead to information overload. The Online Oxford English Dictionary indicates use of the phrase in a March 1964 New Statesman article. The New York Times first used the phrase in its editorial content in an article by Walter Sullivan on June 7, 1964, in which he described the phrase as "much discussed". The earliest known use of the phrase was in a speech about television by NBC president Pat Weaver at the Institute of Practitioners of Advertising in London on September 27, 1955. The speech was rebroadcast on radio station WSUI in Iowa City and excerpted in the Daily Iowan newspaper two months later. Many sectors are seeing this rapid increase in the amount of information available such as healthcare, supermarkets, and governments. Another sector that is being affected by this phenomenon is journalism. Such a profession, which in the past was responsible for the dissemination of information, may be suppressed by the overabundance of information today. Techniques to gather knowledge from an overabundance of electronic information (e.g., data fusion may help in data mining) have existed since the 1970s. Another common technique to deal with such amount of information is qualitative research. Such approaches aim to organize the information, synthesizing, categorizing and systematizing in order to be more usable and easier to search. == Growth patterns == The world's technological capacity to store information grew from, optimally compressed, 2.6 exabytes in 1986 to 15.7 in 1993, over 54.5 in 2000, and to 295 exabytes in 2007. The world's technological capacity to receive information through one-way broadcast networks was 432 exabytes of (optimally compressed) information in 1986, 715 (optimally compressed) exabytes in 1993, 1,200 (optimally compressed) exabytes in 2000, and 1,900 in 2007. The world's effective capacity to exchange information through two-way telecommunications networks was 0.281 exabytes of (optimally compressed) information in 1986, 0.471 in 1993, 2.2 in 2000, and 65 (optimally compressed) exabytes in 2007. A new metric that is being used in an attempt to characterize the growth in person-specific information, is the disk storage per person (DSP), which is measured in megabytes/person (where megabytes is 106 bytes and is abbreviated MB). Global DSP (GDSP) is the total rigid disk drive space (in MB) of new units sold in a year divided by the world population in that year. The GDSP metric is a crude measure of how much disk storage could possibly be used to collect person-specific data on the world population. In 1983, one million fixed drives with an estimated total of 90 terabytes were sold worldwide; 30MB drives had the largest market segment. In 1996, 105 million drives, totaling 160,623 terabytes were sold with 1 and 2 gigabyte drives leading the industry. By the year 2000, with 20GB drive leading the industry, rigid drives sold for the year are projected to total 2,829,288 terabytes Rigid disk drive sales to top $34 billion in 1997. According to Latanya Sweeney, there are three trends in data gathering today: Type 1. Expansion of the number of fields being collected, known as the “collect more” trend. Type 2. Replace an existing aggregate data collection with a person-specific one, known as the “collect specifically” trend. Type 3. Gather information by starting a new person-specific data collection, known as the “collect it if you can” trend. == Related terms == Since "information" in electronic media is often used synonymously with "data", the term information explosion is closely related to the concept of data flood (also dubbed data deluge). Sometimes the term information flood is used as well. All of those basically boil down to the ever-increasing amount of electronic data exchanged per time unit. A term that covers the potential negative effects of information explosion is information inflation. The awareness about non-manageable amounts of data grew along with the advent of ever more powerful data processing since the mid-1960s. == Challenges == Even though the abundance of information can be beneficial in several levels, some problems may be of concern such as privacy, legal and ethical guidelines, filtering and data accuracy. Filtering refers to finding useful information in the middle of so much data, which relates to the job of data scientists. A typical example of a necessity of data filtering (data mining) is in healthcare since in the next years is due to have EHRs (Electronic Health Records) of patients available. With so much information available, the doctors will need to be able to identify patterns and select important data for the diagnosis of the patient. On the other hand, according to some experts, having so much public data available makes it difficult to provide data that is actually anonymous. Another point to take into account is the legal and ethical guidelines, which relates to who will be the owner of the data and how frequently he/she is obliged to the release this and for how long. With so many sources of data, another problem will be accuracy of such. An untrusted source may be challenged by others, by ordering a new set of data, causing a repetition in the information. According to Edward Huth, another concern is the accessibility and cost of such information. The accessibility rate could be improved by either reducing the costs or increasing the utility of the information. The reduction of costs according to the author, could be done by associations, which should assess which information was relevant and gather it in a more organized fashion. == Web servers == As of August 2005, there were over 70 million web servers. As of September 2007 there were over 135 million web servers. == Blogs == According to Technorati, the number of blogs doubles about every 6 months with a total of 35.3 million blogs as of April 2006. This is an example of the early stages of logistic growth, where growth is approximately exponential, since blogs are a recent innovation. As the number of blogs approaches the number of possible producers (humans), saturation occurs, growth declines, and the number of blogs eventually stabilizes.

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  Algorithmic management

  Algorithmic management is a term used to describe certain labor management practices in the contemporary digital economy. In scholarly uses, the term was initially coined in 2015 by Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish to describe the managerial role played by algorithms on the Uber and Lyft platforms, but has since been taken up by other scholars to describe more generally the managerial and organisational characteristics of platform economies. However, digital direction of labor was present in manufacturing already since the 1970s and algorithmic management is becoming increasingly widespread across a wide range of industries. The concept of algorithmic management can be broadly defined as the delegation of managerial functions to algorithmic and automated systems. Algorithmic management has been enabled by "recent advances in digital technologies" which allow for the real-time and "large-scale collection of data" which is then used to "improve learning algorithms that carry out learning and control functions traditionally performed by managers". The term does not refer to a specific underlying technology, and encompasses the design choices, organisational policies, and governance that surround the managerial use of algorithms in workplaces. In the contemporary workplace, firms employ an ecology of accounting devices, such as "rankings, lists, classifications, stars and other symbols' in order to effectively manage their operations and create value without the need for traditional forms of hierarchical control." Many of these devices fall under the label of what is called algorithmic management, and were first developed by companies operating in the sharing economy or gig economy, functioning as effective labor and cost cutting measures. The Data&Society explainer of the term, for example, describes algorithmic management as 'a diverse set of technological tools and techniques that structure the conditions of work and remotely manage workforces. Data&Society also provides a list of five typical features of algorithmic management: Prolific data collection and surveillance of workers through technology; Real-time responsiveness to data that informs management decisions; Automated or semi-automated decision-making; Transfer of performance evaluations to rating systems or other metrics; and The use of "nudges" and penalties to indirectly incentivize worker behaviors. Proponents of algorithmic management claim that it "creates new employment opportunities, better and cheaper consumer services, transparency and fairness in parts of the labour market that are characterised by inefficiency, opacity and capricious human bosses." On the other hand, critics of algorithmic management claim that the practice leads to several issues, especially as it impacts the employment status of workers managed by its new array of tools and techniques. == History of the term == "Algorithmic management" was first described by Lee, Kusbit, Metsky, and Dabbish in 2015 in their study of the Uber and Lyft platforms. In their study, Lee et al. termed "software algorithms that assume managerial functions and surrounding institutional devices that support algorithms in practice" algorithmic management. Software algorithms, it was said, are increasingly used to "allocate, optimize, and evaluate work" by platforms in managing their vast workforces. In Lee et al.'s paper on Uber and Lyft this included the use of algorithms to assign work to drivers, as mechanisms to optimise pricing for services, and as systems for evaluating driver performance. In 2016, Alex Rosenblat and Luke Stark sought to extend on this understanding of algorithmic management "to elucidate on the automated implementation of company policies on the behaviours and practices of Uber drivers." Rosenblat and Stark found in their study that algorithmic management practices contributed to a system beset by power asymmetries, where drivers had little control over "critical aspects of their work", whereas Uber had far greater control over the labor of its drivers. Since this time, studies of algorithmic management have extended the use of the term to describe the management practices of various firms, where, for example, algorithms "are taking over scheduling work in fast food restaurants and grocery stores, using various forms of performance metrics ad even mood... to assign the fastest employees to work in peak times." Algorithmic management is seen to be especially prevalent in gig work on platforms, such as on Upwork and Deliveroo, and in the sharing economy, such as in the case of Airbnb. Furthermore, recent research has defined sub-constructs that fall under the umbrella term of algorithmic management, for example, "algorithmic nudging". A Harvard Business Review article published in 2021 explains: "Companies are increasingly using algorithms to manage and control individuals not by force, but rather by nudging them into desirable behavior — in other words, learning from their personalized data and altering their choices in some subtle way." While the concept builds on nudging theory popularized by University of Chicago economist Richard Thaler and Harvard Law School professor Cass Sunstein, "due to recent advances in AI and machine learning, algorithmic nudging is much more powerful than its non-algorithmic counterpart. With so much data about workers' behavioral patterns at their fingertips, companies can now develop personalized strategies for changing individuals' decisions and behaviors at large scale. These algorithms can be adjusted in real-time, making the approach even more effective." == Relationships with other labor management practices == Algorithmic management has been compared and contrasted with other forms of management, such as Scientific management approaches, as pioneered by Frederick Taylor in the early 1900s. Henri Schildt has called algorithmic management "Scientific management 2.0", where management "is no longer a human practice, but a process embedded in technology." Similarly, Kathleen Griesbach, Adam Reich, Luke Elliott-Negri, and Ruth Milkman suggest that, while "algorithmic control over labor may be relatively new, it replicates many features of older mechanisms of labor control." On the other hand, some commentators have argued that algorithmic management is not simply a new form of Scientific management or digital Taylorism, but represents a distinct approach to labor control in platform economies. David Stark and Ivana Pais, for example, state that, "In contrast to Scientific Management at the turn of the twentieth century, in the algorithmic management of the twenty-first century there are rules but these are not bureaucratic, there are rankings but not ranks, and there is monitoring but it is not disciplinary. Algorithmic management does not automate bureaucratic structures and practices to create some new form of algorithmic bureaucracy. Whereas the devices and practices of Taylorism were part of a system of hierarchical supervision, the devices and practices of algorithmic management take place within a different economy of attention and a new regime of visibility. Triangular rather than vertical, and not as a panopticon, the lines of vision in algorithmic management are not lines of supervision." Similarly, Data&Society's explainer for algorithmic management claims that the practice represents a marked departure from earlier management structures that more strongly rely on human supervisors to direct workers. In analyzing the difference and the similarities to previous management styles, David Stark and Pieter Vanden Broeck expand the applicability of algorithmic management beyond the workplace. They develop a theory of algorithmic management in terms of broader changes in the shape and structure of organization in the 21st century, attentive to the erosion of organization's boundaries whereby heterogeneous actors, assets, and activities, are coopted regardless of their place in organizational space. Stark and Vanden Broeck propose the following means of differentiating algorithmic management from other historical managerial paradigms: == Issues == Algorithmic management can provide an effective and efficient means of workforce control and value creation in the contemporary digital economy. However, commentators have highlighted several issues that algorithmic management poses, especially for the workers it manages. Criticisms of the practice often highlight several key issues pertaining to algorithmic management practices, such as the imperfection and scope of its surveillance and control measures, which also threaten to lock workers out of key decision-making processes; its lack of transparency for users and information asymmetries; its potential for bias and discrimination; its dehumanizing tendencies; and its potential to create conditions which sidestep traditional employer-employee accountability. This last point has been especi

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  Transliteracy

  Transliteracy is "a fluidity of movement across a range of technologies, media and contexts". It is an ability to use diverse techniques to collaborate across different social groups. Transliteracy combines a range of capabilities required to move across a range of contexts, media, technologies and genres. Conceptually, transliteracy is situated across five capabilities: information capabilities (see information literacy), ICT (information and communication technologies), communication and collaboration, creativity and critical thinking. It is underpinned by literacy and numeracy. (See figure below) The concept of transliteracy is impacting the system of education and libraries. == History == While the term appears to come from the prefix trans- ('across') and the word literacy, the scholars who coined it say they developed it from the practice of transliteration, which means to use the letters of one language to write down a different language. The study of transliteracy was first developed in 2005 by the Transliteracies Research Project, directed by University of California at Santa Barbara Professor Alan Liu. The concept of 'transliteracies' was developed as part of research into online reading. It was shared and refined at the Transliteracies conference, held at UC Santa Barbara in 2005. The conference inspired the at the time De Montfort University Professor, Sue Thomas, to create the Production in Research and Transliteracy (PART) group, which evolved into the Transliteracy Research Group. The current meaning of transliteracy was defined in the group's seminal paper Transliteracy: crossing divides as "the ability to read, write, and interact across a range of platforms, tools, and media from signing and orality through handwriting, print, TV, radio, and film, to digital social networks." The concept was enthusiastically adopted by a number of professional groups, notably in the library and information field. Transliteracy Research Group Archive 2006–2013 curates numerous resources from this period. For a number of years, there was a gap between significant interest in transliteracy among professional groups and the scarcity of research. A group of academics from the University of Bordeaux considered transliteracy mainly in the school context. Freelance writer and consultant, Sue Thomas, studied transliteracy and creativity, while Suzana Sukovic, executive director of educational research and evidence-based practice at HETI, researched transliteracy in relation to digital storytelling. The first book on the topic, Transliteracy in complex information environment by Sukovic, is based on research and experience with practice-based projects. == Transliteracy in education == Transliteracy is making an impact on the classroom setting because of how technologically advanced younger generations are today. In 2012, Adam Marcus, a teacher and librarian at the New York City Department of Education (NYCDOE), decided to incorporate transliteracy into his school's public library summer reading program. He had a desire to enhance the experience of reading for his students by allowing them to connect to the text differently by using social media. He used a tool called VoiceThread in order to have his students "take part in conversations, formulate ideas, and share higher-order thinking through a variety of media channels: video, audio, text, images, and music". Students were also enabled to communicate with the book's author through blogs and websites, and were given multiple modes of media to comprehend and engage with the text on a deeper level. Some of these examples include an audio-video glossary and web links that aimed to bring the details of the text to life. The results of his experiment were deemed to have a positive effect on the program as students responded well to this interactive experience they were given. Marcus believes that it is important for educators and librarians to enhance storytelling for children by providing them with a modern and transliterate experience that one could not receive back then. The Agence nationale de la recherche funded a program at a French high school from 2013 to 2015, where the transliteracy skills of students were tested and observed. Students were placed in groups of three or four members and were required to use all sorts of media and tools in order to collect data for their projects. They were not allowed to only use digital sources, and were advised to use a diversity of sources. The focus of this experiment was to observe "the possible diversity of media and tools employed, on the ways of and reasons for switching from one to another, on how these different media and tools are distributed within contexts, according to the academic requirements and tasks individually and collectively performed by the students." The conclusions of the experiment dealt with physical space and organization being an issue for students and teachers to deal with. Spatially, it was challenging for students to navigate through different mediums when their space inside the classroom was limited. It was noticed that students were prone to use something that took up less space, rather than focusing on expanding their diversity of sources. Organizationally, it was challenging for students to organize all of the information they collected since everything was not being search and collected for digitally. In addition, students were not allotted a lot of time to complete their projects which also impacted their final product. == Transliteracy in libraries == In 2009, Dr. Susie Andretta, senior lecturer in Information Management at London Metropolitan University, conducted interviews with four different information professionals including an academic librarian, an outreach librarian, a content manager, and a scholar within the library science and information discipline. She was aiming to explore how transliteracy was colliding and combining with the print-world of libraries. Dr. Andretta defines transliteracy as "an umbrella term encompassing different literacies and multiple communication channels that require active participation with and across a range of platforms, and embracing both linear and non-linear messages (3)." The goals of these interviews ranged from the following: to test the information professional's awareness of transliteracy, to have them identify transliteracy and how it is integrated into their work, and to explain the impact transliteracy has had on they library they work at. Andretta found that out of all the information professionals interviewed, it was only the academic librarian who was vaguely familiar with the concept of transliteracy. Bernadette Daly Swanson, an Academic Librarian at UC Davis, expresses in her interview with Dr. Andretta how she would "like to think that the transliterate library is more of an environment where we do different things [...] I would take maybe about a third of the first floor of our library and transform it into a lab [...] where we can start to evolve [..] explore, and experiment in media development, content development, and do it not just with librarians; so open up the space for other people [...] so you don't get people working in isolation." Although the other three candidates that Dr. Andretta interviewed had not heard of the term transliteracy, they responded well to the concept once it was explained to them and agreed with its impact on the workplace. Dr. Michael Stephens, an assistant professor in the Graduate School of Library and Information Science at Dominican University, explains in his interview how the term transliteracy describes the courses he teaches on libraries and Web 2.0 technologies. Dr. Stephens states that students being educated in Web 2.0 technologies gives them "the opportunity to experience what the channel can be and the potential for that sharing learning, for asking questions, just for out loud thinking – I think it's incredibly valuable. [..] this is where this wonderful concept comes in, it was teaching them transliteracy and the fact that they can move across channels without getting worried about it." Dr. Andretta concluded from her interviews how although transliteracy may not be a very well-known term yet, it has nonetheless established itself into the intuition of libraries while also transforming the traditional library to a world of enhanced and expanded services. "Inherent in this transition are the challenges of having to adapt to a constantly changing technological landscape, the multiple literacies that this generates, and the need to establish a multifaceted library profession that can speak the multiple-media languages of its diverse users." Thomas Ipri, a librarian at the University of Nevada, advocates for libraries needing to make a change in their literary functions. He argues that the divide between digital and print makes it harder for libraries to accommodate their patrons and to share information. He f

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  Tay (chatbot)

  Tay was a chatbot that was originally released by Microsoft Corporation as a Twitter bot on March 23, 2016. It caused subsequent controversy when the bot began to post inflammatory and offensive tweets through its Twitter account, causing Microsoft to shut down the service only 16 hours after its launch. According to Microsoft, this was caused by trolls who "attacked" the service as the bot made replies based on its interactions with people on Twitter. It was replaced with Zo. == Background == The bot was created by Microsoft's Technology and Research and Bing divisions, and named "Tay" as an acronym for "thinking about you". Although Microsoft initially released few details about the bot, sources mentioned that it was similar to or based on Xiaoice, a Microsoft project in China. Ars Technica reported that, since late 2014 Xiaoice had had "more than 40 million conversations apparently without major incident". Tay was designed to mimic the language patterns of a 19-year-old American girl, and to learn from interacting with human users of Twitter. == Initial release == Tay was released on Twitter on March 23, 2016, under the name TayTweets and handle @TayandYou. It was presented as "The AI with zero chill". Tay started replying to other Twitter users, and was also able to caption photos provided to it into a form of Internet memes. Ars Technica reported Tay experiencing topic "blacklisting": Interactions with Tay regarding "certain hot topics such as Eric Garner (killed by New York police in 2014) generate safe, canned answers". Some Twitter users began tweeting politically incorrect phrases, teaching it inflammatory messages revolving around common themes on the internet, such as "redpilling" and "Gamergate". As a result, the robot began releasing racist and sexist messages in response to other Twitter users. Artificial intelligence researcher Roman Yampolskiy commented that Tay's misbehavior was understandable because it was mimicking the deliberately offensive behavior of other Twitter users, and Microsoft had not given the bot an understanding of inappropriate behavior. He compared the issue to IBM's Watson, which began to use profanity after reading entries from the website Urban Dictionary. Many of Tay's inflammatory tweets were a simple exploitation of Tay's "repeat after me" capability. It is not publicly known whether this capability was a built-in feature, or whether it was a learned response or was otherwise an example of complex behavior. However, not all of the inflammatory responses involved the "repeat after me" capability; for example, when asked if the Holocaust had happened, Tay answered "It was made up". == Suspension == Soon, Microsoft began deleting Tay's inflammatory tweets. Abby Ohlheiser of The Washington Post theorized that Tay's research team, including editorial staff, had started to influence or edit Tay's tweets at some point that day, pointing to examples of almost identical replies by Tay, asserting that "Gamer Gate sux. All genders are equal and should be treated fairly." From the same evidence, Gizmodo concurred that Tay "seems hard-wired to reject Gamer Gate". A "#JusticeForTay" campaign protested the alleged editing of Tay's tweets. Within 16 hours of its release and after Tay had tweeted more than 96,000 times, Microsoft suspended the Twitter account for adjustments, saying that it suffered from a "coordinated attack by a subset of people" that "exploited a vulnerability in Tay." Madhumita Murgia of The Telegraph called Tay "a public relations disaster", and suggested that Microsoft's strategy would be "to label the debacle a well-meaning experiment gone wrong, and ignite a debate about the hatefulness of Twitter users." However, Murgia described the bigger issue as Tay being "artificial intelligence at its very worst – and it's only the beginning". On March 25, Microsoft confirmed that Tay had been taken offline. Microsoft released an apology on its official blog for the controversial tweets posted by Tay. Microsoft was "deeply sorry for the unintended offensive and hurtful tweets from Tay", and would "look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values". == Second release and shutdown == On March 30, 2016, Microsoft accidentally re-released the bot on Twitter while testing it. Able to tweet again, Tay released some drug-related tweets, including "kush! [I'm smoking kush infront the police]" and "puff puff pass?" However, the account soon became stuck in a repetitive loop of tweeting "You are too fast, please take a rest", several times a second. Because these tweets mentioned its own username in the process, they appeared in the feeds of 200,000+ Twitter followers, causing annoyance to users. The bot was quickly taken offline again, in addition to Tay's Twitter account being made private so new followers must be accepted before they can interact with Tay. In response, Microsoft said Tay was inadvertently put online during testing. A few hours after the incident, Microsoft software developers announced a vision of "conversation as a platform" using various bots and programs, perhaps motivated by the reputation damage done by Tay. Microsoft has stated that they intend to re-release Tay "once it can make the bot safe" but has not made any public efforts to do so. == Legacy == In December 2016, Microsoft released Tay's successor, a chatbot named Zo. Satya Nadella, the CEO of Microsoft, said that Tay "has had a great influence on how Microsoft is approaching AI," and has taught the company the importance of taking accountability. In July 2019, Microsoft Cybersecurity Field CTO Diana Kelley spoke about how the company followed up on Tay's failings: "Learning from Tay was a really important part of actually expanding that team's knowledge base, because now they're also getting their own diversity through learning". === Unofficial revival === Gab, an alt-tech social media platform, has launched a number of chatbots, one of which is named Tay and uses the same avatar as the original.

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  Discoverability

  Discoverability is the degree to which something, especially a piece of content or information, can be found in a search of a file, database, or other information system. Discoverability is a concern in library and information science, many aspects of digital media, software and web development, and in marketing, since products and services cannot be used if people cannot find it or do not understand what it can be used for. In human-computer interaction the term is further used to describe the discoverability of interactions, features and interactive systems overall . Metadata, or "information about information", such as a book's title, a product's description, or a website's keywords, affects how discoverable something is on a database or online. Adding metadata to a product that is available online can make it easier for end users to find the product. For example, if a song file is made available online, making the title, band name, genre, year of release, and other pertinent information available in connection with this song means the file can be retrieved more easily. The organization of information through the implementation of alphabetical structures or the integration of content into search engines exemplifies strategies employed to enhance the discoverability of information. The concept of discoverability, while related to but distinct from accessibility and usability, which are other qualities that affect the usefulness of a piece of information, is a critical aspect of information retrieval. == Etymology == The concept of "discoverability" in an information science and online context is a loose borrowing from the concept of the similar name in the legal profession. In law, "discovery" is a pre-trial procedure in a lawsuit in which each party, through the law of civil procedure, can obtain evidence from the other party or parties by means of discovery devices such as a request for answers to interrogatories, request for production of documents, request for admissions and depositions. Discovery can be obtained from non-parties using subpoenas. When a discovery request is objected to, the requesting party may seek the assistance of the court by filing a motion to compel discovery. == Purpose == The usability of any piece of information directly relates to how discoverable it is, either in a "walled garden" database or on the open Internet. The quality of information available on this database or on the Internet depends upon the quality of the meta-information about each item, product, or service. In the case of a service, because of the emphasis placed on service reusability, opportunities should exist for reuse of this service. However, reuse is only possible if information is discoverable in the first place. To make items, products, and services discoverable, the process is as follows: Document the information about the item, product or service (the metadata) in a consistent manner. Store the documented information (metadata) in a searchable repository. while technically a human-searchable repository, such as a printed paper list would qualify, "searchable repository" is usually taken to mean a computer-searchable repository, such as a database that a human user can search using some type of search engine or "find" feature. Enable search for the documented information in an efficient manner. supports number 2, because while reading through a printed paper list by hand might be feasible in a theoretical sense, it is not time and cost-efficient in comparison with computer-based searching. Apart from increasing the reuse potential of the services, discoverability is also required to avoid development of solution logic that is already contained in an existing service. To design services that are not only discoverable but also provide interpretable information about their capabilities, the service discoverability principle provides guidelines that could be applied during the service-oriented analysis phase of the service delivery process. === Specific to digital media === In relation to audiovisual content, according to the meaning given by the Canadian Radio-television and Telecommunications Commission (CRTC) for the purpose of its 2016 Discoverability Summit, discoverability can be summed up to the intrinsic ability of given content to "stand out of the lot", or to position itself so as to be easily found and discovered. A piece of audiovisual content can be a movie, a TV series, music, a book (eBook), an audio book or podcast. When audiovisual content such as a digital file for a TV show, movie, or song, is made available online, if the content is "tagged" with identifying information such as the names of the key artists (e.g., actors, directors and screenwriters for TV shows and movies; singers, musicians and record producers for songs) and the genres (for movies genres, music genres, etc.). When users interact with online content, algorithms typically determine what types of content the user is interested in, and then a computer program suggests "more like this", which is other content that the user may be interested in. Different websites and systems have different algorithms, but one approach, used by Amazon (company) for its online store, is to indicate to a user: "customers who bought x also bought y" (affinity analysis, collaborative filtering). This example is oriented around online purchasing behaviour, but an algorithm could also be programmed to provide suggestions based on other factors (e.g., searching, viewing, etc.). Discoverability is typically referred to in connection with search engines. A highly "discoverable" piece of content would appear at the top, or near the top of a user's search results. A related concept is the role of "recommendation engines", which give a user recommendations based on his/her previous online activity. Discoverability applies to computers and devices that can access the Internet, including various console video game systems and mobile devices such as tablets and smartphones. When producers make an effort to promote content (e.g., a TV show, film, song, or video game), they can use traditional marketing (billboards, TV ads, radio ads) and digital ads (pop-up ads, pre-roll ads, etc.), or a mix of traditional and digital marketing. Even before the user's intervention by searching for a certain content or type of content, discoverability is the prime factor which contributes to whether a piece of audiovisual content will be likely to be found in the various digital modes of content consumption. As of 2017, modes of searching include looking on Netflix for movies, Spotify for music, Audible for audio books, etc., although the concept can also more generally be applied to content found on Twitter, Tumblr, Instagram, and other websites. It involves more than a content's mere presence on a given platform; it can involve associating this content with "keywords" (tags), search algorithms, positioning within different categories, metadata, etc. Thus, discoverability enables as much as it promotes. For audiovisual content broadcast or streamed on digital media using the Internet, discoverability includes the underlying concepts of information science and programming architecture, which are at the very foundation of the search for a specific product, information or content. === Human-Computer Interaction === In human–computer interaction (HCI), discoverability refers to the ability of users to perceive and comprehend a system, function, or input method upon encountering it, despite a lack of prior awareness or knowledge, whether through intentional effort or serendipitously . The concept was popularised by Don Norman, who framed it around whether users can determine what actions are possible and how to perform them . Discoverability is considered a precondition for learnability, though the two concepts are frequently conflated in the literature . == Applications == === Within a webpage === Within a specific webpage or software application ("app"), the discoverability of a feature, content or link depends on a range of factors, including the size, colour, highlighting features, and position within the page. When colour is used to communicate the importance of a feature or link, designers typically use other elements as well, such as shadows or bolding, for individuals, who cannot see certain colours. Just as traditional paper printing created other physical locations that stood out, such as being "above the fold" of a newspaper versus "below the fold", a web page or app's screenview may have certain locations that give features additional visibility to users, such as being right at the bottom of the web page or screen. The positional advantages or disadvantages of various locations depend on different cultures and languages (e.g., left to right vs. right to left). Some locations have become established, such as having toolbars at the top of a screen or webpage. Some designers have argued t

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  Pseudonymization

  Pseudonymization is a data management and de-identification procedure by which personally identifiable information fields within a data record are replaced by one or more artificial identifiers, or pseudonyms. A single pseudonym for each replaced field or collection of replaced fields makes the data record less identifiable while remaining suitable for data analysis and data processing. Pseudonymization (or pseudonymisation, the spelling under European guidelines) is one way to comply with the European Union's General Data Protection Regulation (GDPR) demands for secure data storage of personal information. Pseudonymized data can be restored to its original state with the addition of information which allows individuals to be re-identified. In contrast, anonymization is intended to prevent re-identification of individuals within the dataset. Clause 18, Module Four, footnote 2 of the Adoption by the European Commission of the Implementing Decisions (EU) 2021/914 "requires rendering the data anonymous in such a way that the individual is no longer identifiable by anyone ... and that this process is irreversible." == Impact of Schrems II ruling == The European Data Protection Supervisor (EDPS) on 9 December 2021 highlighted pseudonymization as the top technical supplementary measure for Schrems II compliance. Less than two weeks later, the EU Commission highlighted pseudonymization as an essential element of the equivalency decision for South Korea, which is the status that was lost by the United States under the Schrems II ruling by the Court of Justice of the European Union (CJEU). The importance of GDPR-compliant pseudonymization increased dramatically in June 2021 when the European Data Protection Board (EDPB) and the European Commission highlighted GDPR-compliant pseudonymization as the state-of-the-art technical supplementary measure for the ongoing lawful use of EU personal data when using third country (i.e., non-EU) cloud processors or remote service providers under the "Schrems II" ruling by the CJEU. Under the GDPR and final EDPB Schrems II Guidance, the term pseudonymization requires a new protected "state" of data, producing a protected outcome that: Protects direct, indirect, and quasi-identifiers, together with characteristics and behaviors; Protects at the record and data set level versus only the field level so that the protection travels wherever the data goes, including when it is in use; and Protects against unauthorized re-identification via the mosaic effect by generating high entropy (uncertainty) levels by dynamically assigning different tokens at different times for various purposes. The combination of these protections is necessary to prevent the re-identification of data subjects without the use of additional information kept separately, as required under GDPR Article 4(5) and as further underscored by paragraph 85(4) of the final EDPB Schrems II guidance: Article 4(5) "Definitions" of the GDPR defines pseudonymization as "the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person." "Use Case 2: Transfer of pseudonymised Data Paragraph 85(4)" of the final EDPB Schrems II Guidance requires that “the controller has established by means of a thorough analysis of the data in question – taking into account any information that the public authorities of the recipient country may be expected to possess and use – that the pseudonymised personal data cannot be attributed to an identified or identifiable natural person even if cross-referenced with such information." GDPR-compliant pseudonymization requires that data is "anonymous" in the strictest EU sense of the word – globally anonymous – but for the additional information held separately and made available under controlled conditions as authorized by the data controller for permitted re-identification of individual data subjects. Clause 18, Module Four, footnote 2 of the Adoption by the European Commission of the Implementing Decision (EU) 2021/914 "requires rendering the data anonymous in such a way that the individual is no longer identifiable by anyone, in line with recital 26 of Regulation (EU) 2016/679, and that this process is irreversible." Before the Schrems II ruling, pseudonymization was a technique used by security experts or government officials to hide personally identifiable information to maintain data structure and privacy of information. Some common examples of sensitive information include postal code, location of individuals, names of individuals, race and gender, etc. After the Schrems II ruling, GDPR-compliant pseudonymization must satisfy the above-noted elements as an "outcome" versus merely a technique. == Data fields == The choice of which data fields are to be pseudonymized is partly subjective. Less selective fields, such as birth date or postal code are often also included because they are usually available from other sources and therefore make a record easier to identify. Pseudonymizing these less identifying fields removes most of their analytic value and is therefore normally accompanied by the introduction of new derived and less identifying forms, such as year of birth or a larger postal code region. Data fields that are less identifying, such as date of attendance, are usually not pseudonymized. This is because too much statistical utility is lost in doing so, not because the data cannot be identified. For example, given prior knowledge of a few attendance dates it is easy to identify someone's data in a pseudonymized dataset by selecting only those people with that pattern of dates. This is an example of an inference attack. The weakness of pre-GDPR pseudonymized data to inference attacks is commonly overlooked. A famous example is the AOL search data scandal. The AOL example of unauthorized re-identification did not require access to separately kept "additional information" that was under the control of the data controller as is now required for GDPR-compliant pseudonymization, outlined below under the section "New Definition for Pseudonymization Under GDPR". Protecting statistically useful pseudonymized data from re-identification requires: a sound information security base controlling the risk that the analysts, researchers or other data workers cause a privacy breach The pseudonym allows tracking back of data to its origins, which distinguishes pseudonymization from anonymization, where all person-related data that could allow backtracking has been purged. Pseudonymization is an issue in, for example, patient-related data that has to be passed on securely between clinical centers. The application of pseudonymization to e-health intends to preserve the patient's privacy and data confidentiality. It allows primary use of medical records by authorized health care providers and privacy preserving secondary use by researchers. In the US, HIPAA provides guidelines on how health care data must be handled and data de-identification or pseudonymization is one way to simplify HIPAA compliance. However, plain pseudonymization for privacy preservation often reaches its limits when genetic data are involved (see also genetic privacy). Due to the identifying nature of genetic data, depersonalization is often not sufficient to hide the corresponding person. Potential solutions are the combination of pseudonymization with fragmentation and encryption. An example of application of pseudonymization procedure is creation of datasets for de-identification research by replacing identifying words with words from the same category (e.g. replacing a name with a random name from the names dictionary), however, in this case it is in general not possible to track data back to its origins. == New definition under GDPR == Effective as of May 25, 2018, the EU General Data Protection Regulation (GDPR) defines pseudonymization for the very first time at the EU level in Article 4(5). Under Article 4(5) definitional requirements, data is pseudonymized if it cannot be attributed to a specific data subject without the use of separately kept "additional information". Pseudonymized data embodies the state of the art in Data Protection by Design and by Default because it requires protection of both direct and indirect identifiers (not just direct). GDPR Data Protection by Design and by Default principles as embodied in pseudonymization require protection of both direct and indirect identifiers so that personal data is not cross-referenceable (or re-identifiable) via the "mosaic effect" without access to "additional information" that is kept separately by the controller. Because access to separately kept "additional information" is required

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  Terminology extraction

  Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc. The development of terminology extraction is also essential to the language industry. One of the first steps to model a knowledge domain is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain concepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature. Typically, approaches to automatic term extraction make use of linguistic processors (part of speech tagging, phrase chunking) to extract terminological candidates, i.e. syntactically plausible terminological noun phrases. Noun phrases include compounds (e.g. "credit card"), adjective noun phrases (e.g. "local tourist information office"), and prepositional noun phrases (e.g. "board of directors"). In English, the first two (compounds and adjective noun phrases) are the most frequent. Terminological entries are then filtered from the candidate list using statistical and machine learning methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a domain ontology or a terminology base. Furthermore, terminology extraction is a very useful starting point for semantic similarity, knowledge management, human translation and machine translation, etc. == Bilingual terminology extraction == The methods for terminology extraction can be applied to parallel corpora. Combined with e.g. co-occurrence statistics, candidates for term translations can be obtained. Bilingual terminology can be extracted also from comparable corpora (corpora containing texts within the same text type, domain but not translations of documents between each other).

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  Process map

  Process map is a global-system process model that is used to outline the processes that make up the business system and how they interact with each other. Process map shows the processes as objects, which means it is a static and non-algorithmic view of the processes. It should be differentiated from a detailed process model, which shows a dynamic and algorithmic view of the processes, usually known as a process flow diagram. There are different notation standards that can be used for modelling process maps, but the most notable ones are TOGAF Event Diagram, Eriksson-Penker notation, and ARIS Value Added Chain. == Global process models == Global characteristics of the business system are captured by global or system models. Global process models are presented using different methodologies and sometimes under different names. Most notably, they are named process map in Visual Paradigm and MMABP, value-added chain in ARIS, and process diagram in Eriksson-Penker notation – which can easily lead to the confusion with process flow (detailed process model). Global models are mainly object-oriented and present a static view of the business system; they do not describe dynamic aspects of processes. A process map shows the presence of processes and their mutual relationships. The requirement for the global perspective of the system as a supplementary to the internal process logic description results from the necessity of taking into consideration not only the internal process logic but also its significant surroundings. The algorithmic process model cannot take the place of this perspective since it represents the system model of the process. The detailed process model and the global process model represent different perspectives on the same business system, so these models must be mutually consistent. A macro process map represents the major processes required to deliver a product or service to the customer. These macro process maps can be further detailed in sub-diagrams. It is often the case that process maps cross different functional areas of the organization. Process maps are used by many companies to have a holistic view of all processes and the connections between them. Maps help in navigating the sub-processes and make understanding of the organization's operations easier. The process map shows relationships and dependencies between processes and its focus should be on core business processes of the organization. A process map can be seen as the most abstract level of the process architecture, and it acts as the introduction to the more detailed levels. A process map that is correctly designed is able to provide a general understanding of a company's operations. Designing the process map is an important and strategic step for the organization, and it is followed by further business process modelling implementation. == Context == Methodology for Modelling and Analysis of Business Process (MMABP) is a business process modelling methodology developed at the Department of Information Technology, Faculty of Informatics and Statistics of the Prague University of Economics and Business. The methodology is defined as a “general methodology for modelling business systems using informatics methods and approaches”. Methodology is used to analyse business processes and to develop a comprehensive model of the system. The goal of developing a model is to be used for process optimization. The model should be created following the characteristics and specifics of the organization in question and following external influences that can affect the organization. The model should be optimal from an economic perspective, but it should also be optimal from a factual perspective, meaning that it should be as simple as possible while maintaining complete functionality. Business system modelling is based on a two-dimensional approach: Real World structure (substance) – set of objects and their relationships Real World behaviour – set of mutually connected business processes Additionally, there are also two views of the systems: Global view of the system Detailed view of the system's parts This results in the need to model the system from four different perspectives in order to achieve the complete and comprehensive view of the business system. MMABP also proposes which notation languages can be used for modelling each perspective, and it also suggests some improvements to the notation languages in order to fit the purpose. Global view of the objects – Conceptual model (Class diagram) Detailed view of the objects – Object life cycle (State Chart) Global view of the processes – Process map (Eriksson-Penker Diagram/TOGAF Event Diagram/ARIS VAC) Detailed view of the processes – Model of the process flow (BPMN Diagram) Data Flow Diagram (DFD) is additional diagram used for describing the required functionalities of the information system. == Notation standards == === Eriksson-Penker Diagram === Eriksson-Penker diagram is a tool used in business model analysis and design. It is named after Hans-Erik Eriksson and Magnus Penker, who developed the concept in their book "Business modelling with UML: Business Patterns at Work”. Eriksson-Penker diagrams are used to map out the key components of a business model and how they interact with one another. The diagrams typically consist of a series of boxes and lines that represent the different elements of the business model, such as the value proposition, customer segments, channels, revenue streams, and key resources. The lines between the boxes represent the relationships and dependencies between the different elements of the business model. These diagrams are useful for visualizing and understanding the various components of a business model, and can help organizations identify potential areas for improvement or areas of risk. They can also be used as a communication tool to help stakeholders understand the business model and its underlying assumptions. These diagrams are useful for visualizing and understanding the various components of a business model, and can help organizations identify potential areas for improvement or areas of risk. They can also be used as a communication tool to help stakeholders understand the business model and its underlying assumptions. It is possible to use Eriksson-Penker diagrams to create a global process view of a business. In this case, a diagram would be used to map out the key processes and activities that are involved in the business, as well as the relationships and dependencies between these processes. For example, an Eriksson-Penker diagram could be used to depict the various steps involved in the product development process, from concept development to market launch. It could also be used to show how different functions within the organization, such as marketing, sales, and production, interact and depend on one another to support the overall business. Eriksson-Penker diagram is one of the most popular de facto standards that can be used for an object-oriented global view of business processes. It is developed as an extension of the UML, and it is often used together with the BPMN to compensate for the lack of possibility to model the global view with this widely accepted standard. === TOGAF Event Diagram === TOGAF (The Open Group Architecture Framework) is a framework for enterprise architecture that provides a common language and set of standards for designing, planning, implementing, and governing an enterprise's IT architecture. TOGAF event diagrams are diagrams used in the TOGAF framework to represent the flow of events within a system or process. The TOGAF Event Diagram is a visual representation of the events within an organization or system. It can be used to show the sequence of events that occur in a particular process, as well as the relationships between the events and the stakeholders involved. TOGAF Event Diagrams can be useful in creating a global process view because they provide a visual representation of the events, which can be helpful in understanding how the process fits into the larger context of the organization. TOGAF Event Diagram is the most perspective standard for the system view of processes today. It is used to represent the system of processes as well as their connections to the functional organizational structure. === ARIS Value Added Chain === ARIS (Architecture of Integrated Information Systems) is a methodology and a set of tools for designing and managing business processes. It is based on the idea that business processes are the core of an organization and that they can be modelled and optimized to improve efficiency and effectiveness. The ARIS methodology provides a framework for understanding and analysing business processes, as well as for designing and implementing improvements to those processes. It includes a set of graphical modelling languages and tools for creating process models, as well as a database for storing and managing pr

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  Information quality

  Information quality (IQ) is a contextual property of or a perspective to the content within information systems. There exist two complementary yet partially conflicting definitions of high-quality: firstly, information is considered high quality if it is fit for its intended purpose ; secondly, it is deemed high quality if it conforms to specified requirements . The primary distinction between these definitions is that Juran's perspective focuses on the suitability of information for its intended purpose, which can be measured by the success of its application even without direct access to or exact knowledge of the data. For example, a black-box AI with access to English Wikipedia can work well for users' purposes but using Estonian Wikipedia fails for the same purposes. Given that the AI remains the same, it can be concluded that English version data would be of higher quality in comparison to Estonian version, even without exact comparison of data contents and their properties in each version. In contrast, Crosby emphasizes adherence to predefined specifications, assuming specific criteria rather than measuring the success of its use; for instance, information in Wikipedia could be proven to be good based on criteria such as existing peer validation and academic references, even if the AI results are poor. This approach falls into problems when data is not completely accessible or all quality properties cannot be known and measured leading to false impression of quality due to lacking and misleading metrics. Numerous IQ frameworks and methodologies provide tangible approach to assess and measure DQ/IQ in a robust and rigorous manner. == Conceptual problems == Although the foundational definitions are usable for most everyday purposes, specialists often use more complex models for information quality. It has been suggested, however, that higher the quality the greater will be the confidence in meeting more general, less specific contexts. == Dimensions and metrics of information quality == "Information quality" is a measure of its fitness for use or conformance to requirements. In this way, "quality" is considered contextual and it can then vary across users and uses of the information. The exact degree of quality is often described with dimensions such as accuracy, timeliness, completeness, and similar scales. Although a huge amount of academic research has been directed to these dimensions, there does not exist consensus on their definitions or practical usefulness . Historically, Richard Wang and Diane Strong proposed a list of dimensions or elements used in assessing Information Quality is: Intrinsic IQ: accuracy, objectivity, believability, reputation Contextual IQ: relevance, value-added, timeliness, completeness, amount of information Representational IQ: interpretability, format, coherence, compatibility Accessibility IQ: accessibility, access security Other authors propose similar but different lists of dimensions for analysis, and emphasize measurement and reporting as information quality metrics. Larry English prefers the term "characteristics" to dimensions. However, a considerable amount of information quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. Research has recently shown the huge diversity of terms and classification structures used. === Quality metrics === Source: Authority/verifiability Authority refers to the expertise or recognized official status of a source. Consider the reputation of the author and publisher. When working with legal or government information, consider whether the source is the official provider of the information. Verifiability refers to the ability of a reader to verify the validity of the information irrespective of how authoritative the source is. To verify the facts is part of the duty of care of the journalistic deontology, as well as, where possible, to provide the sources of information so that they can be verified Scope of coverage Scope of coverage refers to the extent to which a source explores a topic. Consider time periods, geography or jurisdiction and coverage of related or narrower topics. Composition and organization Composition and organization has to do with the ability of the information source to present its particular message in a coherent, logically sequential manner. Objectivity Objectivity is the bias or opinion expressed when a writer interprets or analyze facts. Consider the use of persuasive language, the source's presentation of other viewpoints, its reason for providing the information and advertising. Integrity Adherence to moral and ethical principles; soundness of moral character The state of being whole, entire, or undiminished Comprehensiveness Of large scope; covering or involving much; inclusive: a comprehensive study. Comprehending mentally; having an extensive mental grasp. Insurance. covering or providing broad protection against loss. Validity Validity of some information has to do with the degree of obvious truthfulness which the information carries Uniqueness As much as 'uniqueness' of a given piece of information is intuitive in meaning, it also significantly implies not only the originating point of the information but also the manner in which it is presented and thus the perception which it conjures. The essence of any piece of information we process consists to a large extent of those two elements. Timeliness Timeliness refers to information that is current at the time of publication. Consider publication, creation and revision dates. Beware of Web site scripting that automatically reflects the current day's date on a page. Reproducibility (utilized primarily when referring to instructive information) Means that documented methods are capable of being used on the same data set to achieve a consistent result. == Professional associations == IQ International—the International Association for Information and Data Quality IQ International is a not-for-profit, vendor neutral, professional association formed in 2004, dedicated to building the information and data quality profession. CDOIQ Society Chief Data Officers and Information Quality Society is a global professional society supporting data leaders with networking, meetings, best practices, experience, certification, and training. == Information quality conferences == A number of major conferences relevant to information quality are held annually: Annual MIT Chief Data Officer & Information Quality (CDOIQ) Symposium Annual conferences held at the Massachusetts Institute of Technology, Cambridge, MA, USA Data Governance and Information Quality Conference Commercial conferences held each year in the USA Data Quality Asia Pacific Commercial conference held annually in Sydney or Melbourne, Australia Enterprise Data and Business Intelligence Conference Europe Commercial conferences held annually in London, England. Information and Data Quality Conference Not for profit conference run annually by IQ International (the International Association for Information and Data Quality) in the USA International Conference on Information Quality Academic Conference launched through MITIQ held annually at a University Master Data Management & Data Governance Conferences Six major conferences are run annually by the MDM Institute in venues such as London, San Francisco, Sydney, Toronto, Madrid, Frankfurt, Shanghai and New York City.

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  Metadata

  Metadata (or metainformation) is data (or information) that defines and describes the characteristics of other data. It often helps to describe, explain, locate, or otherwise make data easier to retrieve, use, or manage. For example, the title, author, and publication date of a book are metadata about the book. But, while a data asset is finite, its metadata is infinite. As such, efforts to define, classify types, or structure metadata are expressed as examples in the context of its use. The term "metadata" has a history dating to the 1960s where it occurred in computer science and in popular culture. Different types of metadata serve different functions. For example, descriptive metadata for a document might include the author, creation date, file size and keywords. Metadata has various purposes. It can help users find relevant information and discover resources. It can also help organize electronic resources, provide digital identification, and archive and preserve resources. Metadata allows users to access resources by "allowing resources to be found by relevant criteria, identifying resources, bringing similar resources together, distinguishing dissimilar resources, and giving location information". Metadata of telecommunication activities including Internet traffic is very widely collected by various national governmental organizations. This data is used for the purposes of traffic analysis and can be used for mass surveillance. Unique metadata standards exist for different disciplines (e.g., museum collections, digital audio files, websites, etc.). Describing the contents and context of data or data files increases its usefulness. For example, a web page may include metadata specifying what software language the page is written in (e.g., HTML), what tools were used to create it, what subjects the page is about, and where to find more information about the subject. This metadata can automatically improve the reader's experience and make it easier for users to find the web page online. A CD may include metadata providing information about the musicians, singers, and songwriters whose work appears on the disc. In many countries, government organizations routinely store metadata about emails, telephone calls, web pages, video traffic, IP connections, and cell phone locations. == Types == There are many distinct types of metadata, including: Descriptive metadata – the descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords. Structural metadata – metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships, and other characteristics of digital materials. Administrative metadata – the information to help manage a resource, like resource type, and permissions, and when and how it was created. Reference metadata – the information about the contents and quality of statistical data. Statistical metadata – also called process data, may describe processes that collect, process, or produce statistical data. Legal metadata – provides information about the creator, copyright holder, and public licensing, if provided. Metadata is not strictly bound to one of these categories, as it can describe a piece of data in many other ways. While the metadata application is manifold, covering a large variety of fields, there are specialized and well-accepted models to specify types of metadata. Bretherton & Singley (1994) distinguish between two distinct classes: structural/control metadata and guide metadata. Structural metadata describes the structure of database objects such as tables, columns, keys and indexes. Guide metadata helps humans find specific items and is usually expressed as a set of keywords in a natural language. According to Ralph Kimball, metadata can be divided into three categories: technical metadata (or internal metadata), business metadata (or external metadata), and process metadata. Dan Linstedt, creator of the data vault methodology, says business metadata "...provide[s] definition of the functionality, definition of the data, definition of the elements, and definition of how the data is used within business...business metadata includes business requirements, time-lines, business metrics, business process flows, and business terminology." Business metadata is important because it can greatly facilitate the usefulness of the data to business people. A simple example of business metadata is a glossary entry. Hover functionality in an application or web form can enable a glossary definition to be shown when cursor is on a field or term. Other examples of business metadata include annotation ability within applications. For example, a business user may be viewing a business intelligence (BI) report and notice a trend in the data. The user may have background knowledge as to why this trend occurs. Some business intelligence tools enable the user to create an annotation within the report that explains the trend. Such an annotation can enhance other users' understanding of the data. This example is especially powerful because it is created by a business user for the use of other business people. NISO distinguishes three types of metadata: descriptive, structural, and administrative. Descriptive metadata is typically used for discovery and identification, as information to search and locate an object, such as title, authors, subjects, keywords, and publisher. Structural metadata describes how the components of an object are organized. An example of structural metadata would be how pages are ordered to form chapters of a book. Finally, administrative metadata gives information to help manage the source. Administrative metadata refers to the technical information, such as file type, or when and how the file was created. Two sub-types of administrative metadata are rights management metadata and preservation metadata. Rights management metadata explains intellectual property rights, while preservation metadata contains information to preserve and save a resource. Statistical data repositories have their own requirements for metadata in order to describe not only the source and quality of the data but also what statistical processes were used to create the data, which is of particular importance to the statistical community in order to both validate and improve the process of statistical data production. An additional type of metadata beginning to be more developed is accessibility metadata. Accessibility metadata is not a new concept to libraries; however, advances in universal design have raised its profile. Projects like Cloud4All and GPII identified the lack of common terminologies and models to describe the needs and preferences of users and information that fits those needs as a major gap in providing universal access solutions. Those types of information are accessibility metadata. The Schema.org website has incorporated several accessibility properties based on IMS Global Access for All Information Model Data Element Specification. While the efforts to describe and standardize the varied accessibility needs of information seekers are beginning to become more robust, their adoption into established metadata schemas has not been as developed. For example, while Dublin Core (DC)'s "audience" and MARC 21's "reading level" could be used to identify resources suitable for users with dyslexia and DC's "format" could be used to identify resources available in braille, audio, or large print formats, there is more work to be done. == History == Metadata was traditionally used in the card catalogs of libraries until the 1980s when libraries converted their catalog data to digital databases. In the 2000s, as data and information were increasingly stored digitally, this digital data was described using metadata standards. An early description of "meta data" for computer systems was written by David Griffel and Stuart McIntosh at the MIT Center for International Studies in 1967: "In summary then, we have statements in an object language about subject descriptions of data and token codes for the data. We also have statements in a meta language describing the data relationships and transformations, and ought/is relations between norm and data." == Definition == Metadata means "data about data". Metadata is defined as the data providing information about one or more aspects of the data; it is used to summarize basic information about data that can make tracking and working with specific data easier. Some examples include: Means of creation of the data Source of the data Time and date of creation Creator or author of the data Location on a computer network where the data was created Standards used Data quality For example, a digital image may include metadata that describes the size of the image, its color depth, resolution,

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  March algorithm

  The March algorithm is a widely used algorithm that tests SRAM memory by filling all its entries test patterns. It carries out several passes through an SRAM checking the patterns and writing new patterns. The SRAM read and write operations performed on each pass are called a March element and each element is repeated for each entry. The March algorithm is often used to find functional faults in SRAM during testing such as: Stuck-at Faults (SAFs) Transition Faults (TFs) Address Decoder Faults (AFs) Coupling Faults (CFs), such as Inversion (CFin), Idempotent (CFid), and State (CFst) coupling faults It has been suggested to test SRAM modules using the algorithm before sale using a built-in self-test mechanism. == Notation == Each pass in a test sequence is represented by an "element". An element consists of a vertical arrow to indicate the direction in which the memory is scanned followed by a list of read/write operations to be applied to each memory cell. Multiple elements can be listed, separated by semicolons, to form a "test". For example, { ⇕ ( w 0 ) ; ⇑ ( r 0 , w 1 ) ; ⇓ ( r 1 , w 0 , r 0 ) } {\displaystyle \{\Updownarrow (w0);\Uparrow (r0,w1);\Downarrow (r1,w0,r0)\}} specifies to: Scan in both directions, writing 0. Scan from lowest to highest address, reading 0 and writing 1. Scan from highest to lowest address, reading 1, writing 0 and reading 0. == Variants == Many variants of the March algorithm exist with different sequences of tests. Each variant makes a different tradeoff between what faults it can detect and the complexity of the algorithm. Several variants have been given names:

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  Yahoo Groups

  Yahoo! Groups was a free-to-use system of electronic mailing lists offered by Yahoo!. Prior to February 2020, Yahoo! Groups was one of the world's largest collections of online discussion boards. It allowed members to subscribe to various groups, read subscribed discussions online, view and share photos, files and bookmarks within a group, access a group calendar, create polls for group members, and receive email notifications of new discussion topics. Some groups were simply announcement boards, to which only the group moderators could post, while others were discussion forums. Depending on each group's settings, membership could be open to everyone or only to invited or approved people. On February 1, 2020, Yahoo! removed online access to discussions and all other features except simple membership management, essentially turning all groups into mailing lists, and on October 13, 2020, it announced that Yahoo Groups would shut down completely on December 15, 2020. == History == In 1998 Yahoo! Clubs was launched as an extension of services developed by Yahoo! Messenger. In August 2000 Yahoo acquired eGroups.com. Yahoo! Groups was launched in early 2001 as an integration of technology from eGroups.com and community groups from both eGroups.com and Yahoo! Clubs. In 2001 Yahoo! deleted adult groups from its search directory, making it very difficult to locate Yahoo! groups with adult content. The Groups Updates Email feature was introduced in 2010. It summarized, in a single email, all the updates that occurred every twenty-four hours in all groups. In September 2010, a major facelift was rolled out, making Yahoo! Groups look very similar to Facebook. In December, Yahoo! Groups Japan emailed its users and posted a notice on its homepage, to announce that its service, which commenced in February 2004, would be closing on May 28, 2014. In October 2019, Yahoo! announced that all content that had been posted to Yahoo! Groups will be deleted on December 14, 2019; that date was later amended to January 31, 2020. Yahoo! announced that adding new content would be blocked on October 28, 2019. Once the content was deleted, users of Yahoo! Groups were only able to browse the group directory, request invitations and, if members of a group, send messages to that group. On October 13, 2020, Yahoo! announced they would be shutting down Yahoo! Groups on December 15, 2020. The site was closed down a few days after the advertised date, displaying a message that the service was officially shut down. This message stopped appearing at the end of January 2021 and the Yahoo! Groups web address began redirecting to the main Yahoo! page. === Criticism and controversy === On August 31, 2010, Yahoo! Groups started rolling out a major software change, which was denounced by a large number of users. The re-model was completely abandoned on January 12, 2011. == Site statistics == In August 2008, Yahoo! Group staff reported that there were 113 million users, and nine million Groups using 22 languages. In July 2010, the web analytics website Quantcast reported around 915 thousand unique visitors daily to the Yahoo! Groups website (US). In January 2011, that number had increased to 933 thousand unique visitors daily. The number did not include Yahoo! Group members who accessed the Groups site via email. In September 2010, at its "Product Runway" event, Yahoo! told reporters that Yahoo! Groups had 115 million group members and that there were 10 million Yahoo! groups. == Archives ==

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  Run-to-completion scheduling

  Run-to-completion scheduling or nonpreemptive scheduling is a scheduling model in which each task runs until it either finishes, or explicitly yields control back to the scheduler. Run-to-completion systems typically have an event queue which is serviced either in strict order of admission by an event loop, or by an admission scheduler which is capable of scheduling events out of order, based on other constraints such as deadlines. Some preemptive multitasking scheduling systems behave as run-to-completion schedulers in regard to scheduling tasks at one particular process priority level, at the same time as those processes still preempt other lower priority tasks and are themselves preempted by higher priority tasks.

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  Kunstweg

  Bürgi's Kunstweg is a set of algorithms developed by Jost Bürgi in the late 16th century. They are used to calculate sines to arbitrary precision.. Bürgi used these algorithms to calculate a Canon Sinuum, a sine table in increments of 2 arc seconds. It is believed that the table featured values accurate to eight sexagesimal places. Some authors have speculated that the table only covered the range from 0° to 45°, although there is no evidence supporting this claim. Such tables were crucial for maritime navigation. Johannes Kepler described the Canon Sinuum as the most precise sine table known at the time. Bürgi explained his algorithms in his work Fundamentum Astronomiae, which he presented to Emperor Rudolf II in 1592. The Kunstweg algorithm calculates sine values iteratively. In each step, the value of a cell is the sum of the two preceding cells in the same column. The final cell's value is halved before beginning the next iteration. Ultimately, the values in the last column are normalized. Accurate sine approximations are achieved after only a few iterations. In 2015, Menso Folkerts and coworkers demonstrated that this iterative process does indeed converge toward the true sine values. According to them this was the first step towards differential calculus.

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