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Cloud-to-Cloud Integration ( C2I ) allows users to connect disparate cloud computing platforms. While Paas (Platform as a service) and Saas (Software as a service) continue to gain momentum, different vendors have different implementations for cloud computing, e.g. Database, REST, SOAP API. Another name for Cloud-to-Cloud Integration is Cloud-Surfing. See also Cloud-based integration
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Cardiovascular health awareness in Nepal has improved in recent times. It has emerged as a cost-effective tool for prevention of heart disease in recent years. == Background == Cardiovascular diseases in Nepal is responsible for maximum number of deaths and rapid urbanization possess extra threat in this situation emphasizing need for widespread awareness campaigns. Efforts to enhance cardiovascular health awareness among the general public, particularly through social media platforms, have proven pivotal in motivating individuals to adopt healthier lifestyle choices and improve heart health in Nepal in recent years. These awareness initiatives, often organized on special occasions such as World Heart Day of World Heart Federation, World Hypertension Day, and World Health Day of World Health Organization, have garnered active participation from the public, reflecting a growing interest in health-related information. Notably, various hospitals, organizations, and health professionals actively contribute to cardiovascular health awareness in Nepal. The prevalence of heart health-related content on social media platforms indicates a substantial dissemination of information. The engagement of a large number of followers underscores the audience's keen interest in health matters, evident in the significant engagement on various social media accounts and pages. During an event of cardiovascular health awareness a Guinness World Record was achieved on World Heart Day 2023. A Facebook live awareness programme on heart health prevention attracted 11,212 viewers during the 30-minute presentation and was awarded the "Most viewers for a cardiovascular health awareness live stream on Facebook" by Guinness World Record signifying depth of public involvement and importance of social media use in health awareness. In 2014, a 11-day heart camp was conducted in Nepal to raise awareness . In 2015, a is a heart health education program " Mission to Save Heart" was conducted to empower health professionals to manage heart attacks and reduce heart disease deaths.
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Anonymous social media is a subcategory of social media wherein the main social function is to share and interact around content and information anonymously on mobile and web-based platforms. Another key aspect of anonymous social media is that content or information posted is not connected with particular online identities or profiles. == Background == Appearing very early on the web as mostly anonymous-confession websites, this genre of social media has evolved into various types and formats of anonymous self-expression. One of the earliest anonymous social media forums was 2channel, which was first introduced online on May 30, 1999, as a Japanese text board forum. With the way digital content is consumed and created continuously changing, the trending shift from web to mobile applications is also affecting anonymous social media. This can be seen as anonymous blogging, or various other format based content platforms such as nameless question and answer online platforms like Ask.fm introduced mobile versions of their services. The number of new networks joining the anonymous social sharing scene continues to grow rapidly. == Degrees of anonymity == Across different forms of anonymous social media there are varying degrees of anonymity. Some applications, such as Librex, require users to sign up for an account, even though their profile is not linked to their posts. While these applications remain anonymous, some of these sites can sync up with the user's contact list or location to develop a context within the social community and help personalize the user's experience, such as Yik Yak or Secret. Other sites, such as 4chan and 2channel, allow for a purer form of anonymity as users are not required to create an account, and posts default to the username of "Anonymous". While users can still be traced through their IP address, there are anonymizing services like I2P or various proxy server services that encrypt a user's identity online by running it through different routers. Secret users must provide a phone number or email when signing up for the service, and their information is encrypted into their posts. Stylometry poses a risk to the anonymity or pseudonymity of social media users, who may be identifiable by writing style; in turn, they may use adversarial stylometry to resist such identification. == Controversy == Apps such as Formspring, Ask, Sarahah, Whisper, and Secret have elicited discussion around the rising popularity of anonymity apps, including debate and anticipation about this social sharing class. As more and more platforms join the league of anonymous social media, there is growing concern about the ethics and morals of anonymous social networking as cases of cyber-bullying, and personal defamation occurs. Formspring, also known as spring.me, and Ask.fm have both been associated with teen suicides as a result of cyberbullying on the sites. Formspring has been associated with at least three teen suicides and Ask.fm with at least five. For instance, the app Secret got shut down due to its escalated use of cyberbullying. The app Yik Yak has also helped to contribute to more cyberbullying situations and, in turn, was blocked on some school networks. Their privacy policy meant that users could not be identified without a subpoena, search warrant, or court order. Another app called After School also sparked controversy for its app design that lets students post any anonymous content. Due to these multiple controversies, the app has been removed from both Apple and Google app stores. As the number of people using these platforms multiplies, unintended uses of the apps have increased, urging popular networks to enact in-app warnings and prohibit the use for middle and high school students. 70% of teens admit to making an effort to conceal their online behavior from their parents. Even Snapchat has some relation to the health of children after using social media. This is an app that is meant to be quick and simple but in many ways it can be overwhelming. A person can post something, and it will be gone in seconds. Oftentimes, the post that was made was inappropriate and harmful to another person. It's a never-ending cycle. Some of these apps have also been criticized for causing chaos in American schools, such as lockdowns and evacuations. In order to limit the havoc caused, anonymous apps are currently removing all abusive and harmful posts. Apps such as Yik Yak, Secret, and Whisper are removing these posts by outsourcing the job of content supervision to oversea surveillance companies. These companies hire a team of individuals to inspect and remove any harmful or abusive posts. Furthermore, algorithms are also used to detect and remove any abusive posts the individuals may have missed. Another method used by the anonymous app named Cloaq to reduce the number of harmful and abusive posts is to limit the number of users that can register during a certain period. Under this system, all contents are still available to the public, but only registered users can post. Other websites such as YouTube have gone on to create new policies regarding anonymity. YouTube now does not allow anonymous comments on videos. Users must have a Google account to like, dislike, comment or reply to comments on videos. Once a sign-in user "likes" a video, it will be added to that user's 'Liked video playlist'. YouTube changed their "Liked video playlist" policy in December 2019, allowing a signed-in user to keep their "Liked video playlist" private. Historically, these controversies and the rise of cyberbullying have been blamed on the anonymous aspect of many social media platforms, but about half of US adult online harassment cases do not involve anonymity, and researchers have found that if targeted harassment exists offline it will also be found online, because online harassment is a reflection of existing prejudices. == As platforms for anonymous discussion == Anonymous social media can be used for political discussion in countries where political opinions opposed to the government are normally suppressed, and allow persons of different genders to communicate freely in cultures where such communication is not generally accepted. In the United States, the 2016 presidential election led to an increase in the use of anonymous social media websites to express political stances. Moreover, anonymous social media can also provide authentic connection to complete anonymous communication. There have been cases where these anonymous platforms have saved individuals from life-threatening situation or spread news about a social cause. Additionally, anonymous social websites also allow internet users to communicate while also safeguarding personal information from criminal actors and corporations that sell users' data. A study in 2017 on the content posted to 4chan's /pol/ board found that the majority of the content was unique, including 70% of the 1 million images included in the studied data set. == Revenue generated by anonymous social media == === Anonymous apps === Generating revenue from anonymous apps has been a discussion for investors. Since little information is collected about the users, it is difficult for anonymous apps to advertise to users. However some apps, such as Whisper, have found a method to overcome this obstacle. They have developed a "keyword-based" approach, where advertisements are shown to users depending on certain words they type. The app Yik Yak has been able to capitalize on the features they provide. Anonymous apps such a Chrends take the approach of using anonymity to provide freedom of speech. Telephony app Burner has regularly been a top grossing utilities app in the iOS and Android app stores using its phone number generation technology. Despite the success of some anonymous apps, there are also apps, such as Secret, which have yet to find a way to generate revenue. The idea of an anonymous app has also caused mixed opinions within investors. Some investors have invested a large sum of money because they see the potential revenue generated within these apps. Other investors have stayed away from investing these apps because they feel these apps bring more harm than good. === Anonymous sites === There are several sources to generate revenue for anonymous social media sites. One source of revenue is by implementing programs such as a premium membership or a gift-exchanging program. Another source of revenue is by merchandising goods and specific usernames to users. In addition, sites such as FMyLife, have implemented a policy where the anonymous site will receive 50% of profit from apps that makes money off it. In terms of advertisements, some anonymous sites have had troubles implementing or attracting them. There are several reasons for this problem. Anonymous sites, such as 4chan, have received few advertisement offers due to some of the contents it generates. Other anonymous sites, such as Reddit, have been ca
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1tik, pronounced Antik (Arabic: أنتيك; lit. "Everything is going well") is a fully Algerian instant messaging, social media and mobile payment app. designed, developed and built locally by the Algerian start-up, INTAJ Digital, with backing from the state-owned company ATM Mobilis (who's the company's main sponsor). It is described as Algeria's first super-app that is entirely designed and built by local developers. == Etymology == The name "1tik" (Arabic: أنتيك) is drawn from the popular Algerian vernacular (Antik), the neologism, which appeared several years ago, means "everything is going well" or "it's all good". == History == 1tik was officially launched and announced the 20th December 2025 by INTAJ Digital's founder Youcef Toulaib and a team of 50 employees, making it the first ever Algerian instant messaging, social media and mobile payment app, rivaling with the growing influence of Yassir in Algeria. it grew in popularity after the presidency of Algeria and several other state-owned companies, medias, and ministries opened official accounts on the app.
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In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. Drift detection and drift adaptation are of paramount importance in the fields that involve dynamically changing data and data models. == Predictive model decay == In machine learning and predictive analytics this drift phenomenon is called concept drift. In machine learning, a common element of a data model are the statistical properties, such as probability distribution of the actual data. If they deviate from the statistical properties of the training data set, then the learned predictions may become invalid, if the drift is not addressed. == Data configuration decay == Another important area is software engineering, where three types of data drift affecting data fidelity may be recognized. Changes in the software environment ("infrastructure drift") may invalidate software infrastructure configuration. "Structural drift" happens when the data schema changes, which may invalidate databases. "Semantic drift" is changes in the meaning of data while the structure does not change. In many cases this may happen in complicated applications when many independent developers introduce changes without proper awareness of the effects of their changes in other areas of the software system. For many application systems, the nature of data on which they operate are subject to changes for various reasons, e.g., due to changes in business model, system updates, or switching the platform on which the system operates. In the case of cloud computing, infrastructure drift that may affect the applications running on cloud may be caused by the updates of cloud software. There are several types of detrimental effects of data drift on data fidelity. Data corrosion is passing the drifted data into the system undetected. Data loss happens when valid data are ignored due to non-conformance with the applied schema. Squandering is the phenomenon when new data fields are introduced upstream in the data processing pipeline, but somewhere downstream these data fields are absent. == Inconsistent data == "Data drift" may refer to the phenomenon when database records fail to match the real-world data due to the changes in the latter over time. This is a common problem with databases involving people, such as customers, employees, citizens, residents, etc. Human data drift may be caused by unrecorded changes in personal data, such as place of residence or name, as well as due to errors during data input. "Data drift" may also refer to inconsistency of data elements between several replicas of a database. The reasons can be difficult to identify. A simple drift detection is to run checksum regularly. However the remedy may be not so easy. == Examples == The behavior of the customers in an online shop may change over time. For example, if weekly merchandise sales are to be predicted, and a predictive model has been developed that works satisfactorily. The model may use inputs such as the amount of money spent on advertising, promotions being run, and other metrics that may affect sales. The model is likely to become less and less accurate over time – this is concept drift. In the merchandise sales application, one reason for concept drift may be seasonality, which means that shopping behavior changes seasonally. Perhaps there will be higher sales in the winter holiday season than during the summer, for example. Concept drift generally occurs when the covariates that comprise the data set begin to explain the variation of your target set less accurately — there may be some confounding variables that have emerged, and that one simply cannot account for, which renders the model accuracy to progressively decrease with time. Generally, it is advised to perform health checks as part of the post-production analysis and to re-train the model with new assumptions upon signs of concept drift. == Possible remedies == To prevent deterioration in prediction accuracy because of concept drift, reactive and tracking solutions can be adopted. Reactive solutions retrain the model in reaction to a triggering mechanism, such as a change-detection test or control charts from statistical process control, to explicitly detect concept drift as a change in the statistics of the data-generating process. When concept drift is detected, the current model is no longer up-to-date and must be replaced by a new one to restore prediction accuracy. A shortcoming of reactive approaches is that performance may decay until the change is detected. Tracking solutions seek to track the changes in the concept by continually updating the model. Methods for achieving this include online machine learning, frequent retraining on the most recently observed samples, and maintaining an ensemble of classifiers where one new classifier is trained on the most recent batch of examples and replaces the oldest classifier in the ensemble. Contextual information, when available, can be used to better explain the causes of the concept drift: for instance, in the sales prediction application, concept drift might be compensated by adding information about the season to the model. By providing information about the time of the year, the rate of deterioration of your model is likely to decrease, but concept drift is unlikely to be eliminated altogether. This is because actual shopping behavior does not follow any static, finite model. New factors may arise at any time that influence shopping behavior, the influence of the known factors or their interactions may change. Concept drift cannot be avoided for complex phenomena that are not governed by fixed laws of nature. All processes that arise from human activity, such as socioeconomic processes, and biological processes are likely to experience concept drift. Therefore, periodic retraining, also known as refreshing, of any model is necessary. === Remedy methods === DDM (Drift Detection Method): detects drift by monitoring the model's error rate over time. When the error rate passes a set threshold, it enters a warning phase, and if it passes another threshold, it enters a drift phase. EDDM (Early Drift Detection Method): improves DDM's detection rate by tracking the average distance between two errors instead of only the error rate. ADWIN (Adaptive Windowing): dynamically stores a window of recent data and warns the user if it detects a significant change between the statistics of the window's earlier data compared to more recent data. KSWIN (Kolmogorov–Smirnov Windowing): detects drift based on the Kolmogorov-Smirnov statistical test. DDM and EDDM: Concept Drift Detection online supervised methods that rely on sequential error monitoring to estimate the evolving error rate. ADWIN and KSWIN: Windowing maintain a "window", a subset of the most recent data, of the data stream, which it checks for statistical differences across the window. == Applications in security == Concept drift is a recurring issue in security analytics, especially in malware and intrusion detection. In these systems, models are often trained on past logs, binaries or network traces, but the behaviour of attackers changes over time as new malware families, obfuscation techniques and campaigns appear. When the data no longer resemble the training set, the decision boundaries learned by classifiers or anomaly detectors can become misaligned with the current threat landscape and detection performance can drop unless the models are updated or replaced. Several studies on Windows malware model detection as an evolving data stream and track how performance changes as time passes. They show that classifiers trained on a fixed time window can perform well on nearby data but deteriorate quickly when evaluated on samples collected months or years later, even when large amounts of training data are available. In order to keep up with this, security systems often use sliding or adaptive windows, which restrict training to the most recent portion of the data so that older, less relevant examples are gradually discarded. They also employ drift detectors such as ADWIN and KSWIN that monitor error rates or changes in the distribution of recent observations and signal when the statistics of the incoming stream differ significantly from the past, prompting retraining or model replacement. Related problems appear in spam filtering, fraud detection and intrusion detection, where adversaries change content, patterns of activity or network behavior to evade models trained on historical data. In these settings drift can be gradual, as new types of spam or fraud emerge, or abrupt, after a sudden shift in attack techniques. Common strategies to remain eff
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A hardware compatibility list (HCL) is a list of computer hardware (typically including many types of peripheral devices) that is compatible with a particular operating system or device management software. The list contains both whole computer systems and specific hardware elements including motherboards, sound cards, and video cards. In today's world, there is a vast amount of computer hardware in circulation, and many operating systems too. A hardware compatibility list is a database of hardware models and their compatibility with a certain operating system. HCLs can be centrally controlled (one person or team keeps the list of hardware maintained) or user-driven (users submit reviews on hardware they have used). There are many HCLs. Usually, each operating system will have an official HCL on its website.
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Digital entertainment Industry includes, but is not restricted to, any combination of the following industries (that themselves have a considerable degree of overlap): digital media new media video on demand video games interactive entertainment online gambling mobile entertainment social media streaming services "Digital entertainment", largely a hard to define marketing term, rests upon entertainment technology and ultimately on the enabling basic technologies computers, Internet/World Wide Web, digital rights management, multimedia and streaming media. Apart from pure entertainment, the term rests upon the observation that already in 2011 in the UK, for example, "nearly half of people’s waking hours are spent using media content and communications services" ("screen time"). Digital entertainment is inextricably connected with digital marketing. People who follow influencers on social media for entertainment will receive a fair share of advertising at the same time. Digital merchandise is distributed with every computer game and popup ads or similar are ubiquitous in the online (gaming) world.
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The following outline is provided as an overview of and topical guide to web design and web development, two very related fields: Web design – field that encompasses many different skills and disciplines in the production and maintenance of websites. The different areas of web design include web graphic design; interface design; authoring, including standardized code and proprietary software; user experience design; and search engine optimization. Often many individuals will work in teams covering different aspects of the design process, although some designers will cover them all. The term web design is normally used to describe the design process relating to the front-end (client side) design of a website including writing markup. Web design partially overlaps web engineering in the broader scope of web development. Web designers are expected to have an awareness of usability and if their role involves creating markup then they are also expected to be up to date with web accessibility guidelines. Web development – work involved in developing a web site for the Internet (World Wide Web) or an intranet (a private network). Web development can range from developing a simple single static page of plain text to complex web-based internet applications (web apps), electronic businesses, and social network services. A more comprehensive list of tasks to which web development commonly refers, may include web engineering, web design, web content development, client liaison, client-side/server-side scripting, web server and network security configuration, and e-commerce development. Among web professionals, "web development" usually refers to the main non-design aspects of building web sites: writing markup and coding. Web development may use content management systems (CMS) to make content changes easier and available with basic technical skills. For larger organizations and businesses, web development teams can consist of hundreds of people (web developers) and follow standard methods like Agile methodologies while developing websites. Smaller organizations may only require a single permanent or contracting developer, or secondary assignment to related job positions such as a graphic designer or information systems technician. Web development may be a collaborative effort between departments rather than the domain of a designated department. There are three kinds of web developer specialization: front-end developer, back-end developer, and full-stack developer. Front-end developers are responsible for behaviour and visuals that run in the user browser, back-end developers deal with the servers and full-stack developers are responsible for both. Currently, the demand for React and Node.JS developers are very high all over the world. == Web design == Graphic design Typography Page layout User experience design (UX design) User interface design (UI design) Web Design techniques Responsive web design (RWD) Adaptive web design (AWD) Progressive enhancement Tableless web design Software Adobe Photoshop Adobe Illustrator Adobe XD Figma Sketch (software) Affinity Designer Inkscape == Web development == Front-end web development – the practice of converting data to a graphical interface, through the use of HTML, CSS, and JavaScript, so that users can view and interact with that data. HyperText Markup Language (HTML) (.html) Cascading Style Sheets (CSS) (.css) CSS framework JavaScript (.js) Package managers for JavaScript npm (originally short for Node Package Manager) Server-side scripting (also known as "Server-side (web) development" or "Back-end (web) development") ASP (.asp) ASP.NET Web Forms (.aspx) ASP.NET Web Pages (.cshtml, .vbhtml) ColdFusion Markup Language (.cfm) Go (.go) Google Apps Script (.gs) Hack (.php) Haskell (.hs) (example: Yesod) Java (.jsp) via JavaServer Pages JavaScript or TypeScript using Server-side JavaScript (.ssjs, .js, .ts) (example: Node.js) Lasso (.lasso) Lua (.lp .op .lua) Node.js (.node) Parser (.p) Perl via the CGI.pm module (.cgi, .ipl, .pl) PHP (.php, .php3, .php4, .phtml) Progress WebSpeed (.r,.w) Python (.py) (examples: Pyramid, Flask, Django) R (.rhtml) – (example: rApache) React (.jsx, .tsx) Ruby (.rb, .rbw) (example: Ruby on Rails) SMX (.smx) Tcl (.tcl) Full stack web development – involves both front-end and back-end (server-side) development Web framework Types of framework architectures Model–view–controller Three-tier architecture Software Atom IntelliJ IDEA Sublime Text Visual Studio Code
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Security Controls for Computer Systems, commonly called the Ware report, is a 1970 text by Willis Ware that was foundational in the field of computer security. == Development == A defense contractor in St. Louis, Missouri, had bought an IBM mainframe computer, which it was using for classified work on a fighter aircraft. To provide additional income, the contractor asked the Department of Defense (DoD) for permission to sell computer time on the mainframe to local businesses via remote terminals, while the classified work continued. At the time, the DoD did not have a policy to cover this. The DoD's Advanced Research Projects Agency (DARPA) asked Ware - a RAND employee - to chair a committee to examine and report on the feasibility of security controls for computer systems. The committee's report was a classified document given in January 1970 to the Defense Science Board (DSB), which had taken over the project from ARPA. After declassification, the report was published by RAND in October 1979. == Influence == The IEEE Computer Society said the report was widely circulated, and the IEEE Annals of the History of Computing said that it, together with Ware's 1967 Spring Joint Computer Conference session, marked the start of the field of computer security. The report influenced security certification standards and processes, especially in the banking and defense industries, where the report was instrumental in creating the Orange Book.
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Digital anthropology is the anthropological study of the relationship between humans and digital-era technology. The field is new, and thus has a variety of names with a variety of emphases. These include techno-anthropology, digital ethnography, cyberanthropology, and virtual anthropology. == Definition and scope == Most anthropologists who use the phrase "digital anthropology" are specifically referring to online and Internet technology. The study of humans' relationship to a broader range of technology may fall under other subfields of anthropological study, such as cyborg anthropology. The Digital Anthropology Group (DANG) is classified as an interest group in the American Anthropological Association. DANG's mission includes promoting the use of digital technology as a tool of anthropological research, encouraging anthropologists to share research using digital platforms, and outlining ways for anthropologists to study digital communities. Cyberspace or the "virtual world" itself can serve as a "field" site for anthropologists, allowing the observation, analysis, and interpretation of the sociocultural phenomena springing up and taking place in any interactive space. National and transnational communities, enabled by digital technology, establish a set of social norms, practices, traditions, storied history and associated collective memory, migration periods, internal and external conflicts, potentially subconscious language features and memetic dialects comparable to those of traditional, geographically confined communities. This includes the various communities built around free and open-source software, online platforms such as Facebook, Twitter/X, Instagram, 4chan and Reddit and their respective sub-sites, and politically motivated groups like Anonymous, WikiLeaks, or the Occupy movement. A number of academic anthropologists have conducted traditional ethnographies of virtual worlds, such as Bonnie Nardi's study of World of Warcraft or Tom Boellstorff's study of Second Life. Academic Gabriella Coleman has done ethnographic work on the Debian software community and the Anonymous hacktivist network. Theorist Nancy Mauro-Flude conducts ethnographic field work on computing arts and computer subcultures such as systerserver.net a part of the communities of feminist web servers and the Feminist Internet network. Eitan Y. Wilf examines the intersection of artists' creativity and digital technology and artificial intelligence. Yongming Zhou studied how in China the internet is used to participate in politics. Eve M. Zucker and colleagues study the shift to digital memorialization of mass atrocities and the emergent role of artificial intelligence in these processes. Victoria Bernal conducted ethnographic research on the themes of nationalism and citizenship among Eritreans participating in online political engagement with their homeland. Anthropological research can help designers adapt and improve technology. Australian anthropologist Genevieve Bell did extensive user experience research at Intel that informed the company's approach to its technology, users, and market. == Methodology == === Digital fieldwork === Many digital anthropologists who study online communities use traditional methods of anthropological research. They participate in online communities in order to learn about their customs and worldviews, and back their observations with private interviews, historical research, and quantitative data. Their product is an ethnography, a qualitative description of their experience and analyses. Other anthropologists and social scientists have conducted research that emphasizes data gathered by websites and servers. However, academics often have trouble accessing user data on the same scale as social media corporations like Facebook and data mining companies like Acxiom. In terms of method, there is a disagreement in whether it is possible to conduct research exclusively online or if research will only be complete when the subjects are studied holistically, both online and offline. Tom Boellstorff, who conducted a three-year research as an avatar in the virtual world Second Life, defends the first approach, stating that it is not just possible, but necessary to engage with subjects “in their own terms”. Others, such as Daniel Miller, have argued that an ethnographic research should not exclude learning about the subject's life outside the internet. === Digital technology as a tool of anthropology === The American Anthropological Association offers an online guide for students using digital technology to store and share data. Data can be uploaded to digital databases to be stored, shared, and interpreted. Text and numerical analysis software can help produce metadata, while a codebook may help organize data. == Ethics == Online fieldwork offers new ethical challenges. According to the American Anthropological Association's ethics guidelines, anthropologists researching a community must make sure that all members of that community know they are being studied and have access to data the anthropologist produces. However, many online communities' interactions are publicly available for anyone to read, and may be preserved online for years. Digital anthropologists debate the extent to which lurking in online communities and sifting through public archives is ethical. The Association also asserts that anthropologists' ability to collect and store data at all is "a privilege", and researchers have an ethical duty to store digital data responsibly. This means protecting the identity of participants, sharing data with other anthropologists, and making backup copies of all data. == Prominent figures == Genevieve Bell is an Australian cultural anthropologist credited for pioneering the User Experience field. During her time working for Intel Corporation, Bell studied how various cultures from around the world interacted with and experienced technology. Researching and improving user experience allows companies and designers to gather data regarding how users utilize their digital products and what requires improvement or expansion. Tom Boellstorff is an anthropologist known for Coming of Age in Second Life: An Anthropologist Explores the Virtually Human where he conducted research on how engaging in virtual worlds affects the player’s sense of self. Gabriella Coleman is an American anthropologist concerned with the politics, ethics, and culture of hacking and online activism. Coleman’s most notable ethnography features the hacktivist collective Anonymous, where she argues that various genres of hacking exist according to the social conditions at play. Coleman is dedicated to making her ethnography accessible to a diverse audience, including academics and non-academics. Diana E. Forsythe was an American anthropologist of science and technology and the author of the essays featured in Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. She asked relevant questions such as how should humans interact with computers and how gender roles are maintained in technology-oriented occupations. Heather Horst is a sociocultural anthropologist interested in the relationship between digital social relations and material culture. Nancy Mauro-Flude is a design anthropologist whose work explores the tacit relations between embodied cognition, computational materiality, maker culture, self-hosted webserver cooperatives, creative practice, and artistic research in digital infrastructure and Internet publishing. Mizuko Ito is a Japanese cultural anthropologist specializing in technology use and the intersection between computers and the social sciences. Her primary interest is in how young people utilize media technology and how it can be used to engage students in education. Daniel Miller is an anthropologist with a concentration in digital anthropology. His research includes the smartphone and perpetual opportunism, the intent and consequences of posting on social media in various geographical locations, and how hospice patients use media to socialize in the last stage of their lives. Mike Wesch is a cultural anthropologist interested in how people share their lives, cultures, and beliefs through digital media.
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Multichannel Audio Digital Interface (MADI) standardized as AES10 by the Audio Engineering Society (AES) defines the data format and electrical characteristics of an interface that carries multiple channels of digital audio. The AES first documented the MADI standard in AES10-1991 and updated it in AES10-2003 and AES10-2008. The MADI standard includes a bit-level description and has features in common with the two-channel AES3 interface. MADI supports serial digital transmission over coaxial cable or fibre-optic lines of 28, 56, 32, or 64 channels; and sampling rates to 96 kHz and beyond with an audio bit depth of up to 24 bits per channel. Like AES3 and ADAT Lightpipe, it is a unidirectional interface from one sender to one receiver. == Development and applications == MADI was developed by AMS Neve, Solid State Logic, Sony and Mitsubishi and is widely used in the audio industry, especially in the professional audio sector. It provides advantages over other audio digital interface protocols and standards such as AES3, ADAT Lightpipe, TDIF (Tascam Digital Interface), and S/PDIF (Sony/Philips Digital Interface). These advantages include: Support for a greater number of channels per line Use of coaxial and optical fiber media that support transmission of audio signals over 100 meters, up to 3000 meters over multi-mode and 40,000 meters over single-mode optical fiber The original specification (AES10-1991) defined the MADI link as a 56-channel transport for linking large-format mixing consoles to digital multitrack recording devices. Large broadcast studios also adopted it for routing multi-channel audio throughout their facilities. The 2003 revision (AES10-2003) adds a 64-channel capability by removing varispeed operation and supports 96 kHz sampling frequency with reduced channel capacity. The latest AES10-2008 standard includes minor clarifications and updates to correspond to the current AES3 standard. Audio over Ethernet of various types is the primary alternative to MADI for transport of many channels of professional digital audio. == Transmission format == MADI links use a transmission format similar to Fiber Distributed Data Interface (FDDI) networking. Since MADI is most often transmitted on copper links via 75-ohm coaxial cables, it more closely compares to the FDDI specification for copper-based links, called CDDI. AES10-2003 recommends using BNC connectors with coaxial cables and SC connectors with optic fibers. MADI over fibre can support a range of up to 2 km. The basic data rate is 100 Mbit/s of data using 4B5B encoding to produce a 125 MHz physical baud rate. Unlike AES3, this clock is not synchronized to the audio sample rate, and the audio data payload is padded using JK sync symbols. Sync symbols may be inserted at any subframe boundary, and must occur at least once per frame. Though the standard disassociates the transmission clock from the audio sample rate, and thus requires a separate word clock connection to maintain synchronization, some vendors do give the option of locking to parts of the transmission timing information for purposes of deriving a word clock. The audio data is almost identical to the AES3 payload, though with more channels. Rather than letters, MADI assigns channel numbers from 0–63. Frame synchronization is provided by sync symbols outside the data itself, rather than an embedded preamble sequence, and the first four time slots of each sub-channel are encoded as normal data, used for sub-channel identification: Bit 0: Set to 1 to mark channel 0, the first channel in each frame Bit 1: Set to 1 to indicate that this channel is active (contains interesting data) Bit 2: notA/B channel marker, used to mark left (0) and right (1) channels. Generally, even channels are A and odd channels are B. Bit 3: Set to 1 to mark the beginning of a 192-sample data block == Sampling frequency == The original AES10-1991 specification allowed 56 channels at sample rates from 32 to 48 kHz with an additional vari-speed range of ± 12.5%. This leads to a total range of 28 to 54 kHz. At the highest frequency, this produces a total of 56 × 32 × 54 = 96768 kbit/s, leaving 3.232% of the channel for synchronization marks and transmit clock error. The 2003 revision specifies different relations between sampling frequency and number of channels. 32 kHz to 48 kHz ± 12.5%, 56 channels; 32 kHz to 48 kHz nominal, 64 channels; 64 kHz to 96 kHz ± 12.5%, 28 channels. With a 48 kHz sampling frequency, 64 channels take 64 × 32 × 48000 = 98.304 Mbit/s. Adding the minimum 8 × 58 kbit/s of framing produces 98688 bit/s, leaving 1.312% free for timing variation and other overhead. Both versions of the standard accommodate higher sampling frequencies (for example, 96 kHz or 192 kHz) by using two or more channels per audio sample on the link.
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A group (often termed as a community, e-group or club) is a feature in many social networking services which allows users to create, post, comment to and read from their own interest- and niche-specific forums, often within the realm of virtual communities. Groups, which may allow for open or closed access, invitation and/or joining by other users outside the group, are formed to provide mini-networks within the larger, more diverse social network service. Much like electronic mailing lists, they are also owned and maintained by owners, moderators, or managers, who can edit posts to discussion threads and regulate member behavior within the group. However, unlike traditional Internet forums and mailing lists, groups in social networking services allow owners and moderators alike to share account credentials between groups without having to log in to every group. == History == The rise of the World Wide Web resulted in an expansion of the varieties of methods for communication on the Internet, much of which was limited in the 1980s to discussion in newsgroups, BBS and chat rooms. While the initial rise of web-based mass communication took place in the form of early Internet forums in the mid-1990s, a few services such as MSN Groups, Yahoo! Groups and eGroups pioneered the combination of web-based mailing list archives with user profiles; by 2000, such services doubled as full-fledged mailing lists and Internet forums, allowing users to create an extremely large variety of discussion and networking mediums with comparatively sparse thresholds of complexity. Further features included chat rooms (often Java-based), image and video galleries, and group calendars. The second spurt of bullecalbel networking, one which was less dependent upon mailing list-related features and more upon Internet forum features, began in the early- to mid-2000s in the form of such services as LiveJournal, Friendster, MySpace and Facebook. These services continued the evolution of the web-based e-group as a discussion and organization medium. In the late 2000s, services such as Yammer and Micromobs further advanced e-group communication by taking advantage of microblog-style activity streams. == In virtual worlds == In Second Life, groups are centered less around discussion forums (as such, an asynchronous conferencing feature is not built into the Second Life network as of 2009) and common interest, and are more centered on maintenance of a particular geographic location inside the network. Such groups are often created by the owners of areas such as buildings, plots of land or whole islands in order to cater to the most frequent visitors and patrons of the regions. With the limited asynchronous messaging capability of Second Life, groups are also a means of mass-emailing announcements pertinent to the group, but are not completely capable of hosting discussion or deliberation of such announcement messages. == The importance of online social networking groups == Before people expanded their social life to the internet, they had small circles. These included the networks gained from rural areas or villages, such as family, friends and neighbors, and community groups such as churches. These networks represented a social safety net to support individuals. Since we have moved a huge part of our social life to the internet, online social networking groups have become a way to maintain a structure in social life. Online networking is made up by clusters of people, bounding themselves together on the World Wide Web. To be able to sort out the many different clusters we belong to we use online groups to helps us arrange and make sense of all our contacts. This sense-making is rooted within us, we sort and put people into compartments or sort by categories to make sense and try to understand our relationships to the people around us. Online social networking groups therefore enables us to do the same thing online. Online social networks have a huge impact on people’s lives. Since the social network revolution has offered people with more loose ties and diversity in their relationships, it creates both stress and opportunities. Furthermore, the Internet revolution has transformed the contact point from a household to the individual. In addition, people are in constant communication with each other due to the mobile revolution. All in all, the mentioned revolutions created a new social operating system: "networked individualism". The way that people currently connect, communicate and exchange information can be described as a form of operating system because of the similarities between the structure of computer systems and the networked individualism that has taken form in society. These structures consist of unwritten rules, norms, constraints and opportunities which are apparent for those who are part of a specific network. == Concerns == There is some research claiming that fake news is infiltrating online social networking. A recent study claimed that people exposed to fake news generally revert to their original opinion even after finding out the information they were given was false.
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Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy description logics, and each description logic features a different balance between expressive power and reasoning complexity by supporting different sets of mathematical constructors. DLs are used in artificial intelligence to describe and reason about the relevant concepts of an application domain (known as terminological knowledge). It is of particular importance in providing a logical formalism for ontologies and the Semantic Web: the Web Ontology Language (OWL) and its profiles are based on DLs. A major area of application of DLs and OWL is in biomedical informatics, where they assist in the codification of biomedical knowledge. DLs and OWL are also applied in other domains, including defense, climate modeling, and large-scale industrial knowledge graphs. == Introduction == A DL models concepts, roles and individuals, and their relationships. The fundamental modeling concept of a DL is the axiom—a logical statement relating roles and/or concepts. This is a key difference from the frames paradigm where a frame specification declares and completely defines a class. == Nomenclature == === Terminology compared to FOL and OWL === The description logic community uses different terminology than the first-order logic (FOL) community for operationally equivalent notions; some examples are given below. The Web Ontology Language (OWL) uses again a different terminology, also given in the table below. === Naming convention === There are many varieties of description logics and there is an informal naming convention, roughly describing the operators allowed. The expressivity is encoded in the label for a logic starting with one of the following basic logics: Followed by any of the following extensions: ==== Exceptions ==== Some canonical DLs that do not exactly fit this convention are: ==== Examples ==== As an example, A L C {\displaystyle {\mathcal {ALC}}} is a centrally important description logic from which comparisons with other varieties can be made. A L C {\displaystyle {\mathcal {ALC}}} is simply A L {\displaystyle {\mathcal {AL}}} with complement of any concept allowed, not just atomic concepts. A L C {\displaystyle {\mathcal {ALC}}} is used instead of the equivalent A L U E {\displaystyle {\mathcal {ALUE}}} . A further example, the description logic S H I Q {\displaystyle {\mathcal {SHIQ}}} is the logic A L C {\displaystyle {\mathcal {ALC}}} plus extended cardinality restrictions, and transitive and inverse roles. The naming conventions aren't purely systematic so that the logic A L C O I N {\displaystyle {\mathcal {ALCOIN}}} might be referred to as A L C N I O {\displaystyle {\mathcal {ALCNIO}}} and other abbreviations are also made where possible. The Protégé ontology editor supports S H O I N ( D ) {\displaystyle {\mathcal {SHOIN}}^{\mathcal {(D)}}} . Three major biomedical informatics terminology bases, SNOMED CT, GALEN, and GO, are expressible in E L {\displaystyle {\mathcal {EL}}} (with additional role properties). OWL 2 provides the expressiveness of S R O I Q ( D ) {\displaystyle {\mathcal {SROIQ}}^{\mathcal {(D)}}} , OWL-DL is based on S H O I N ( D ) {\displaystyle {\mathcal {SHOIN}}^{\mathcal {(D)}}} , and for OWL-Lite it is S H I F ( D ) {\displaystyle {\mathcal {SHIF}}^{\mathcal {(D)}}} . == History == Description logic was given its current name in the 1980s. Previous to this it was called (chronologically): terminological systems, and concept languages. === Knowledge representation === Frames and semantic networks lack formal (logic-based) semantics. DL was first introduced into knowledge representation (KR) systems to overcome this deficiency. The first DL-based KR system was KL-ONE (by Ronald J. Brachman and Schmolze, 1985). During the '80s other DL-based systems using structural subsumption algorithms were developed including KRYPTON (1983), LOOM (1987), BACK (1988), K-REP (1991) and CLASSIC (1991). This approach featured DL with limited expressiveness but relatively efficient (polynomial time) reasoning. In the early '90s, the introduction of a new tableau based algorithm paradigm allowed efficient reasoning on more expressive DL. DL-based systems using these algorithms — such as KRIS (1991) — show acceptable reasoning performance on typical inference problems even though the worst case complexity is no longer polynomial. From the mid '90s, reasoners were created with good practical performance on very expressive DL with high worst case complexity. Examples from this period include FaCT, RACER (2001), CEL (2005), and KAON 2 (2005). DL reasoners, such as FaCT, FaCT++, RACER, DLP and Pellet, implement the method of analytic tableaux. KAON2 is implemented by algorithms which reduce a SHIQ(D) knowledge base to a disjunctive datalog program. === Semantic web === The DARPA Agent Markup Language (DAML) and Ontology Inference Layer (OIL) ontology languages for the Semantic Web can be viewed as syntactic variants of DL. In particular, the formal semantics and reasoning in OIL use the S H I Q {\displaystyle {\mathcal {SHIQ}}} DL. The DAML+OIL DL was developed as a submission to—and formed the starting point of—the World Wide Web Consortium (W3C) Web Ontology Working Group. In 2004, the Web Ontology Working Group completed its work by issuing the OWL recommendation. The design of OWL is based on the S H {\displaystyle {\mathcal {SH}}} family of DL with OWL DL and OWL Lite based on S H O I N ( D ) {\displaystyle {\mathcal {SHOIN}}^{\mathcal {(D)}}} and S H I F ( D ) {\displaystyle {\mathcal {SHIF}}^{\mathcal {(D)}}} respectively. The W3C OWL Working Group began work in 2007 on a refinement of - and extension to - OWL. In 2009, this was completed by the issuance of the OWL2 recommendation. OWL2 is based on the description logic S R O I Q ( D ) {\displaystyle {\mathcal {SROIQ}}^{\mathcal {(D)}}} . Practical experience demonstrated that OWL DL lacked several key features necessary to model complex domains. == Modeling == === TBox vs Abox === In DL, a distinction is drawn between the so-called TBox (terminological box) and the ABox (assertional box). In general, the TBox contains sentences describing concept hierarchies (i.e., relations between concepts) while the ABox contains ground sentences stating where in the hierarchy, individuals belong (i.e., relations between individuals and concepts). For example, the statement: belongs in the TBox, while the statement: belongs in the ABox. Note that the TBox/ABox distinction is not significant, in the same sense that the two "kinds" of sentences are not treated differently in first-order logic (which subsumes most DL). When translated into first-order logic, a subsumption axiom like (1) is simply a conditional restriction to unary predicates (concepts) with only variables appearing in it. Clearly, a sentence of this form is not privileged or special over sentences in which only constants ("grounded" values) appear like (2). === Motivation for having Tbox and Abox === So why was the distinction introduced? The primary reason is that the separation can be useful when describing and formulating decision-procedures for various DL. For example, a reasoner might process the TBox and ABox separately, in part because certain key inference problems are tied to one but not the other one ('classification' is related to the TBox, 'instance checking' to the ABox). Another example is that the complexity of the TBox can greatly affect the performance of a given decision-procedure for a certain DL, independently of the ABox. Thus, it is useful to have a way to talk about that specific part of the knowledge base. The secondary reason is that the distinction can make sense from the knowledge base modeler's perspective. It is plausible to distinguish between our conception of terms/concepts in the world (class axioms in the TBox) and particular manifestations of those terms/concepts (instance assertions in the ABox). In the above example: when the hierarchy within a company is the same in every branch but the assignment to employees is different in every department (because there are other people working there), it makes sense to reuse the TBox for different branches that do not use the same ABox. There are two features of description logic that are not shared by most other data description formalisms: DL does not make the unique name assumption (UNA) or the closed-world assumption (CWA). Not having UNA means that two concepts with different names may be allowed by some inference to be shown to be equivalent. Not having CWA, or rather having the open world assumption (OWA) means that
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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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A distributed operating system is system software over a collection of independent software, networked, communicating, and physically separate computational nodes. They handle jobs which are serviced by multiple CPUs. Each individual node holds a specific software subset of the global aggregate operating system. Each subset is a composite of two distinct service provisioners. The first is a ubiquitous minimal kernel, or microkernel, that directly controls that node's hardware. Second is a higher-level collection of system management components that coordinate the node's individual and collaborative activities. These components abstract microkernel functions and support user applications. The microkernel and the management components collection work together. They support the system's goal of integrating multiple resources and processing functionality into an efficient and stable system. This seamless integration of individual nodes into a global system is referred to as transparency, or single system image; describing the illusion provided to users of the global system's appearance as a single computational entity. == Description == A distributed OS provides the essential services and functionality required of an OS but adds attributes and particular configurations to allow it to support additional requirements such as increased scale and availability. To a user, a distributed OS works in a manner similar to a single-node, monolithic operating system. That is, although it consists of multiple nodes, it appears to users and applications as a single-node. Separating minimal system-level functionality from additional user-level modular services provides a "separation of mechanism and policy". Mechanism and policy can be simply interpreted as "what something is done" versus "how something is done," respectively. This separation increases flexibility and scalability. == Overview == === The kernel === At each locale (typically a node), the kernel provides a minimally complete set of node-level utilities necessary for operating a node's underlying hardware and resources. These mechanisms include allocation, management, and disposition of a node's resources, processes, communication, and input/output management support functions. Within the kernel, the communications sub-system is of foremost importance for a distributed OS. In a distributed OS, the kernel often supports a minimal set of functions, including low-level address space management, thread management, and inter-process communication (IPC). A kernel of this design is referred to as a microkernel. Its modular nature enhances reliability and security, essential features for a distributed OS. === System management === System management components are software processes that define the node's policies. These components are the part of the OS outside the kernel. These components provide higher-level communication, process and resource management, reliability, performance and security. The components match the functions of a single-entity system, adding the transparency required in a distributed environment. The distributed nature of the OS requires additional services to support a node's responsibilities to the global system. In addition, the system management components accept the "defensive" responsibilities of reliability, availability, and persistence. These responsibilities can conflict with each other. A consistent approach, balanced perspective, and a deep understanding of the overall system can assist in identifying diminishing returns. Separation of policy and mechanism mitigates such conflicts. === Working together as an operating system === The architecture and design of a distributed operating system must realize both individual node and global system goals. Architecture and design must be approached in a manner consistent with separating policy and mechanism. In doing so, a distributed operating system attempts to provide an efficient and reliable distributed computing framework allowing for an absolute minimal user awareness of the underlying command and control efforts. The multi-level collaboration between a kernel and the system management components, and in turn between the distinct nodes in a distributed operating system is the functional challenge of the distributed operating system. This is the point in the system that must maintain a perfect harmony of purpose, and simultaneously maintain a complete disconnect of intent from implementation. This challenge is the distributed operating system's opportunity to produce the foundation and framework for a reliable, efficient, available, robust, extensible, and scalable system. However, this opportunity comes at a very high cost in complexity. === The price of complexity === In a distributed operating system, the exceptional degree of inherent complexity could easily render the entire system an anathema to any user. As such, the logical price of realizing a distributed operation system must be calculated in terms of overcoming vast amounts of complexity in many areas, and on many levels. This calculation includes the depth, breadth, and range of design investment and architectural planning required in achieving even the most modest implementation. These design and development considerations are critical and unforgiving. For instance, a deep understanding of a distributed operating system's overall architectural and design detail is required at an exceptionally early point. An exhausting array of design considerations are inherent in the development of a distributed operating system. Each of these design considerations can potentially affect many of the others to a significant degree. This leads to a massive effort in balanced approach, in terms of the individual design considerations, and many of their permutations. As an aid in this effort, most rely on documented experience and research in distributed computing power. == History == Research and experimentation efforts began in earnest in the 1970s and continued through the 1990s, with focused interest peaking in the late 1980s. A number of distributed operating systems were introduced during this period; however, very few of these implementations achieved even modest commercial success. Fundamental and pioneering implementations of primitive distributed operating system component concepts date to the early 1950s. Some of these individual steps were not focused directly on distributed computing, and at the time, many may not have realized their important impact. These pioneering efforts laid important groundwork, and inspired continued research in areas related to distributed computing. In the mid-1970s, research produced important advances in distributed computing. These breakthroughs provided a solid, stable foundation for efforts that continued through the 1990s. The accelerating proliferation of multi-processor and multi-core processor systems research led to a resurgence of the distributed OS concept. === The DYSEAC === One of the first efforts was the DYSEAC, a general-purpose synchronous computer. In one of the earliest publications of the Association for Computing Machinery, in April 1954, a researcher at the National Bureau of Standards – now the National Institute of Standards and Technology (NIST) – presented a detailed specification of the DYSEAC. The introduction focused upon the requirements of the intended applications, including flexible communications, but also mentioned other computers: Finally, the external devices could even include other full-scale computers employing the same digital language as the DYSEAC. For example, the SEAC or other computers similar to it could be harnessed to the DYSEAC and by use of coordinated programs could be made to work together in mutual cooperation on a common task… Consequently[,] the computer can be used to coordinate the diverse activities of all the external devices into an effective ensemble operation. The specification discussed the architecture of multi-computer systems, preferring peer-to-peer rather than master-slave. Each member of such an interconnected group of separate computers is free at any time to initiate and dispatch special control orders to any of its partners in the system. As a consequence, the supervisory control over the common task may initially be loosely distributed throughout the system and then temporarily concentrated in one computer, or even passed rapidly from one machine to the other as the need arises. …the various interruption facilities which have been described are based on mutual cooperation between the computer and the external devices subsidiary to it, and do not reflect merely a simple master-slave relationship. This is one of the earliest examples of a computer with distributed control. The Dept. of the Army reports certified it reliable and that it passed all acceptance tests in April 1954. It was completed and delivered on time, in May 1954. This was a "portable comput
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