AI Detector Check

AI Detector Check — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Army Chief Information Officer/G-6

  In September 2020, the Army realigned the previously consolidated CIO/G-6 function into two separate roles, Office of the Chief Information Officer and Deputy Chief of Staff, G-6, that report to the secretary of the Army and chief of staff of the Army, respectively. The realignment came after several months of planning and coordination. Lt. Gen. John Morrison was nominated to the Senate for promotion and assignment as the G-6 and confirmed, assuming that position in August 2020. Subsequently, the Secretary of the Army, Ryan McCarthy appointed Dr. Raj G. Iyer as the first civilian Chief Information Officer, a career Senior Executive Service position in November 2020. == G-6 == Advise chief of staff of the Army and the Chief Information Officer on planning, fielding, and execution of C4IT worldwide Army operations Develop and execute the plan for the Unified Network Implement Army information assurance Supervise C4IT, Signal support, Information security, Force structure and equipping activities in support of warfighting operations Oversee management of the Signal forces == Planned realignment == On June 11, 2020, the Army announced that the two roles of CIO and Deputy Chief of Staff, G-6 (DCS, G-6) would be realigned no later than August 31, 2020, with separate individuals responsible for each position. With the realignment: CIO core functions will be policy, governance, and oversight. Focus areas include: Information Environment, Cybersecurity, Enterprise Architecture, and Data Policy/Oversight/Governance, Enterprise Architecture, Enterprise Cloud Management and IT Spend/Category Management. DCS, G-6 core functions will be planning, strategy, and implementation. Focus areas include: Information Environment/Network, Planning and Integration, Theater Synchronization, Architecture Integration, Enterprise Information Environment (EIE) Mission Area Portfolio Management and Mission Decision Packet Management. In order to support multi-domain operations, the Army will have to connect Enterprise networks and tactical networks. —LTG Morrison, DCS, G-6 DCS G-6 released the Army Unified Network Plan under the Army Digital Transformation Strategy, to help the Army to establish a Multi-Domain Operations capable force by 2028. The Unified Network will enable Army formations, as part of the Joint Force, to operate in highly contested and congested operational environments with the speed and global range to achieve decision dominance and maintain overmatch. The plan shapes, synchronizes, integrates and governs Unified Network efforts and aligns the personnel, organizational structure and capabilities required to enable MDO at all echelons. == Chief signal officers and their successors == Chief signal officers (1860–1964) Maj. Albert J. Myer 1860–1863 Lt. Col. William J. L. Nicodemus 1863–1864 Col. Benjamin F. Fisher 1864–1866 Col. Albert J. Myer 1866–1880 (promoted to brigadier general 16 June 1880) Brig. Gen. William B. Hazen 1880–1887 Brig. Gen. Adolphus W. Greely 1887–1906 Brig. Gen. James Allen 1906–1913 Brig. Gen. George P. Scriven 1913–1917 Brig. Gen. George O. Squier 1917–1923 (promoted to major general 6 October 1917) Maj. Gen. Charles McK. Saltzman 1924–1928 Maj. Gen. George Sabin Gibbs 1928–1931 Maj. Gen. Irving J. Carr 1931–1934 Maj. Gen. James B. Allison 1935–1937 Maj. Gen. Joseph O. Mauborgne 1937–1941 Maj. Gen. Dawson Olmstead 1941–1943 Maj. Gen. Harry C. Ingles 1943–1947 Maj. Gen. Spencer B. Akin 1947–1951 Maj. Gen. George I. Back 1951–1955 Lt. Gen. James D. O’Connell 1955–1959 Maj. Gen. Ralph T. Nelson 1959–1962 Maj. Gen. Earle F. Cook 1962–1963 Maj. Gen. David Parker Gibbs 1963–1964 Chiefs of communications-electronics (1964–1967) Maj. Gen. David Parker Gibbs 1964–1966 Maj. Gen. Walter E. Lotz, Jr. 1966–1967 Assistant chiefs of staff for communications-electronics (1967–1974) Maj. Gen. Walter E. Lotz, Jr. 1967–1968 Maj. Gen. George E. Pickett 1968–1972 Lt. Gen. Thomas Rienzi 1972–1974 Directors of telecommunications and command and control (1974–1978) (a directorate of ODCSOPS) Lt. Gen. Thomas Rienzi 1974–1977 Lt. Gen. Charles R. Myer 1977–1978 Assistant chiefs of staff for automation and communications (1978–1981) Lt. Gen. Charles R. Myer 1978–1979 Maj. Gen. Clay T. Buckingham 1979–1981 Assistant deputy chiefs of staff for operations and plans (command, control, communications, and computers) (1981–1984) Maj. Gen. Clay T. Buckingham 1981–1982 Maj. Gen. James M. Rockwell 1982–1984 Assistant chiefs of staff for information management (1984–1987) Lt. Gen. David K. Doyle 1984–1986 Lt. Gen. Thurman D. Rodgers 1986–1987 Directors of information systems for command, control, communications, and computers Lt. Gen. Thurman D. Rodgers 1987–1988 Lt. Gen. Bruce R. Harris 1988–1990 Lt. Gen. Jerome B. Hilmes 1990–1992 Lt. Gen. Peter A. Kind 1992–1994 Lt. Gen. Otto J. Guenther 1995–1997 Lt. Gen. William H. Campbell Chief Information Officer, Military Deputy to the Army Acquisition Executive, and Director of Information Systems for Command, Control, Communications and Computers Lt. Gen. William H. Campbell 1997–2000

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  Myrinet

  Myrinet, ANSI/VITA 26-1998, is a high-speed local area networking system designed by the company Myricom to be used as an interconnect between multiple machines to form computer clusters. == Description == Myrinet was promoted as having lower protocol overhead than standards such as Ethernet, and therefore better throughput, less interference, and lower latency while using the host CPU. Although it can be used as a traditional networking system, Myrinet is often used directly by programs that "know" about it, thereby bypassing a call into the operating system. Earlier versions of Myrinet used a variety of media and connectors: Generation 2 used copper media with DC-37 (Myrinet-LAN, M2L- controllers and switches) or microribbon (Myrinet-SAN, M2M-) connectors. Generation 3 used copper media with HSSDC (Myrinet-Serial, M3S-) or microribbon (Myrinet-SAN, M3M-) connectors, or fiber with LC-connectors (Myrinet-Fiber, M3F-). The later versions of Myrinet physically consist of two fibre optic cables, upstream and downstream, connected to the host computers with a single connector. Machines are connected via low-overhead routers and switches, as opposed to connecting one machine directly to another. Myrinet includes a number of fault-tolerance features, mostly backed by the switches. These include flow control, error control, and "heartbeat" monitoring on every link. The "fourth-generation" Myrinet, called Myri-10G, supported a 10 Gbit/s data rate and can use 10 Gigabit Ethernet on PHY, the physical layer (cables, connectors, distances, signaling). Myri-10G started shipping at the end of 2005. Myrinet was approved in 1998 by the American National Standards Institute for use on the VMEbus as ANSI/VITA 26-1998. One of the earliest publications on Myrinet is a 1995 IEEE article. === Performance === Myrinet is a lightweight protocol with little overhead that allows it to operate with throughput close to the basic signaling speed of the physical layer. For supercomputing, the low latency of Myrinet is even more important than its throughput performance, since, according to Amdahl's law, a high-performance parallel system tends to be bottlenecked by its slowest sequential process, which in all but the most embarrassingly parallel supercomputer workloads is often the latency of message transmission across the network. === Deployment === According to Myricom, 141 (28.2%) of the June 2005 TOP500 supercomputers used Myrinet technology. In the November 2005 TOP500, the number of supercomputers using Myrinet was down to 101 computers, or 20.2%, in November 2006, 79 (15.8%), and by November 2007, 18 (3.6%), a long way behind gigabit Ethernet at 54% and InfiniBand at 24.2%. In the June 2014 TOP500 list, the number of supercomputers using Myrinet interconnect was 1 (0.2%). In November 2013, the assets of Myricom (including the Myrinet technology) were acquired by CSP Inc. In 2016, it was reported that Google had also offered to buy the company.

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  Social Media (Age-Restricted Users) Bill

  The Social Media (Age-Restricted Users) Bill is a member's bill by National Party Member of Parliament Catherine Wedd that seeks to ban children under the age of 16 years from accessing social media by forcing social media companies to implement age verification measures. It is modelled after the Australian government's Online Safety Amendment. In mid October 2025, the New Zealand Parliament confirmed plans to introduce the social media age restriction bill. == Background == In late November 2024, the Albanese government of Australia, with support from the opposition Coalition parties, passed the Online Safety Amendment creating a world-first age verification regime targeting social media platforms operating in the country. The ban targets several social media platforms including Facebook, Instagram, Kick, Reddit, Snapchat, Threads, TikTok, Twitch, X (formerly Twitter) and YouTube. These platforms were required to implement age verification systems and to remove under-age users by 10 December 2025, when the law change came into effect. == Draft provisions == The draft Social Media (Age-Restricted Users) Bill defines social media platforms as electronic platforms that enable social media interactions between two or more end-users, facilitates communication between multiple end-users and allows users to post content on the platform. The proposed bill requires social media companies to take action to prevent users under the age of 16 from creating accounts on their platforms. It also creates a framework for courts to impose fines on platforms that fail to take reasonable steps to prevent underaged users from accessing the platform. == Legislative history == === Draft legislation === On 6 May 2025, Wedd announced a private member's bill called the "Social Media (Age-Restricted Users) Bill" that would bar access to social media platforms for people under the age of 16 years. She said that she was motivated as the mother of four children to support families, parents and teachers' efforts to manage their children's online exposure and the passage of the Australian Online Safety Amendment legislation in December 2024. Since National's coalition partner ACT New Zealand had refused to support the bill, the Sixth National Government announce it as a member's bill rather than a government bill. Prime Minister Christopher Luxon has confirmed that National would seek cross-party support for the legislation. ACT MP and the Minister of Internal Affairs Brooke van Velden said that the Government would watch the implementation of the Australian social media age restriction policy. In October 2025, Wedd's bill was drawn from the parliamentary ballot. In addition, Labour Reuben Davidson drafted a similar member's bill that would hold social media providers responsible for restricting "harmful content" and imposed NZ$50,000 fines for non-compliance. In November 2025, Luxon reiterated his support for social media age restriction legislation and said the New Zealand government would introduce a bill in 2026 before the 2026 New Zealand general election. He also confirmed that Education Minister Erica Stanford was leading an investigation into what lessons could be learnt from the Australian legislation. At the request of ACT MP Parmjeet Parmar, Parliament's Education and Workforce Committee held an inquiry into a proposed social media ban in early October 2025. The committee was led by National MP Carl Bates and received 430 submissions from 400 groups and individuals. The committee also heard from 87 in-person submissions. On 10 December 2025, the committee made 12 recommendations including restricting social media access to persons under the age of 16, re-evaluating existing legislation such as the Films, Videos, and Publications Classification Act and the Harmful Digital Communications Act 2015, and regulating online platforms and Internet service providers. The ACT party released a dissenting view disagreeing with the need for a law restricting social media access to under-16 year olds. In mid-May 2026, the Government confirmed that work on the proposed bill to ban under-16 year olds from social media had been paused. The New Zealand Parliament held a debate on the proposed bill on 13 May following a select committee inquiry into the harms caused by social media platforms. While the opposition Labour Party has agreed to support the member's bill, the ACT and Green parties opposed the proposed bill on the grounds that the rules were easy to circumvent, that at-risk groups could become more isolated, and that social media also harmed other age groups. == Responses == === Academia and civil society === In late July 2025, the New Zealand Council for Civil Liberties (NZCCL) expressed concern that the proposed social media age restriction could infringe upon the New Zealand Bill of Rights Act 1990, the Privacy Act 2020 and the United Nations' Convention on the Rights of the Child. The NZCCL also questioned the practicality of age verification software, a social media age limit and whether it would fulfil its stated goal of combating online harm. In August 2025, University of Auckland criminologist and senior lecturer Claire Meehan expressed concern that the social media age restriction legislation would cut children from their friendship and support networks. She also said that children and young people were digital natives who could use VPNs to circumvent the ban. Similar sentiments were echoed by Victoria University of Wellington media and communications lecturer Alex Beattie and "Ocean Today" Instagram social media influencer "Charlie." In October 2025, New Zealand Initiative representative Dr Eric Crampton expressed concern that a social media age restriction would involve the introduction of digital IDs. He argued that a new law was unnecessary and said that parents could limit their children's exposure to social media via Google's Family Link and Apple's equivalent. Similarly, Institute of Economic Affairs public policy fellow Matthew Lesh and the British Free Speech Union expressed concerns that young people could use VPNs to circumvent a social media ban, citing the spike in VPN usage in the United Kingdom following the passage of the Online Safety Act 2023. The advocacy group B416's co-chair Anna Curzon advocated for a social media ban on underage users, stating that social media apps "are made to be addictive" and made it difficult for parents to relate with their children. In late November 2025, B416's co-founder Anna Mowbray expressed support for the Government's social media age restriction bill but expressed disappointment that Luxon had not timed his announcement with the launch of the group's campaign. Generation-Z Aotearoa co-founder Lola Fisher has called on the New Zealand Government to consult with young people on the development of the legislation. === Government agencies and departments === In early October 2025, Privacy Commissioner Michael Webster expressed concern that social media platforms requiring users to prove their age via digital IDs could raise privacy concerns. Webster suggested that age verification systems could relay on various documents including passports. He said that age estimation technologies had high error rates and that age inference technologies relied on data mining. === Political parties === In early May 2025, the National Party government expressed support for a social media age restriction legislation. By contrast, its coalition partner ACT has opposed such legislation. ACT leader David Seymour described the ban as hasty and unworkable since it did not involve parents. Meanwhile, New Zealand First leader Winston Peters expressed support for a social media age restriction but said the bill should be subject to a select committee inquiry. The opposition Labour Party leader Chris Hipkins has expressed interest in a social media age restriction legislation but emphasised the need for consensus. Meanwhile, Green Party co-leader Chlöe Swarbrick said she wanted to learn more about the bill but described it as simplistic. Fellow Greens co-leader Marama Davidson said that the proposed bill would punish children and young people for the harm caused by big tech platforms. === Tech companies === In early October 2025, representatives of TikTok and Meta Platforms cautioned against proposed social media ban on under-16 years olds. During a one-day parliamentary inquiry, Ella Woods-Joyce, TikTok's public policy lead for Australia and New Zealand, and Mia Garlick, Meta's regional director of policy, expressed concern that the social media age restriction could send children and young people to less regulated online spaces. Woods-Joyce highlighted TikTok's policy of closing down accounts belonging to users under the age of 13 years while Garlick highlighted Meta's policy of placing users under the age of 16 in private accounts by default. In early February 2026 Meta's vice president and global head of safety, Antigone Da

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  Social bot

  A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media. == Uses == Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement. Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff. Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends. Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user. Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms. == History == Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions. With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular. Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. == Legal regulation == Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns. In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots. The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior. == Detection == The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are: cartoon figures as user pictures sometimes also random real user pictures are captured (identity fraud) reposting rate temporal patterns sentiment expression followers-to-friends ratio length of user names variability in (re)posted messages engagement rate (like/followers rate) analysis of the time series of social media posts Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further a

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  PerfKitBenchmarker

  PerfKit Benchmarker is an open source benchmarking tool used to measure and compare cloud offerings. PerfKit Benchmarker is licensed under the Apache 2 license terms. PerfKit Benchmarker is a community effort involving over 500 participants including researchers, academic institutions and companies together with the originator, Google. == General == PerfKit Benchmarker (PKB) is a community effort to deliver a repeatable, consistent, and open way of measuring Cloud Performance. It supports a growing list of cloud providers including: Alibaba Cloud, Amazon Web Services, CloudStack, DigitalOcean, Google Cloud Platform, Kubernetes, Microsoft Azure, OpenStack, Rackspace, IBM Bluemix (Softlayer). In addition to Cloud Providers to supports container orchestration including Kubernetes [1] and Mesos [2] and local "static" workstations and clusters of computers [3]. The goal is to create an open source living benchmark [framework] that represents how Cloud developers are building applications, evaluating Cloud alternatives, learning how to architect applications for each cloud. Living because it will change and morph quickly as developers change. PerfKit Benchmarker measures the end to end time to provision resources in the cloud, in addition to reporting on the most standard metrics of peak performance, e.g.: latency, throughput, time-to-complete, IOPS. PerfKit Benchmarker reduces the complexity in running benchmarks on supported cloud providers by unified and simple commands. It's designed to operate via vendor provided command line tools. PerfKit Benchmarker contains a canonical set of public benchmarks. All benchmarks are running with default/initial state and configuration (Not tuned to in favor of any providers). This provides a way to benchmark across cloud platforms, while getting a transparent view of application throughput, latency, variance, and overhead. == History == PerfKit Benchmarker (PKB) was started by Anthony F. Voellm, Alain Hamel, and Eric Hankland at Google in 2014. Once an initial "alpha" was in place Anthony F. Voellm and Ivan Santa Maria Filho built a community including ARM, Broadcom, Canonical, CenturyLink, Cisco, CloudHarmony, CloudSpectator, EcoCloud@EPFL, Intel, Mellanox, Microsoft, Qualcomm Technologies, Inc., Rackspace, Red Hat, Tradeworx Inc., and Thesys Technologies LLC. This community worked together behind the scenes in a private GitHub project to create an open way to measure cloud performance. This community released the first public "beta" was released on February 11, 2015, and announced in a blog post at which point the GitHub project was open to everyone. After almost a year and with large adaption (600+ participants on GitHub) the V1.0.0 was released along with a detailed architectural design on December 10, 2015. == Benchmarks == A list of available benchmarks from PerfKitBenchmarker: (The latest set of benchmarks can be found at GitHub readme file.) == Industry participants == Since Google open sourced the PerfKitBenchmarker, it became a community effort from over 30 leading researchers, academic schools and industry companies. Those organizations include: ARM, Broadcom, Canonical, CenturyLink, Cisco, CloudHarmony, Cloud Spectator, EcoCloud@EPFL, Intel, Mellanox, Microsoft, Qualcomm Technologies, Rackspace, Red Hat, and Thesys Technologies. In addition, Stanford and MIT are leading quarterly discussions on default benchmarks and settings proposed by the community. EcoCloud@EPFL is integrating CloudSuite into PerfKit Benchmarker. == Example runs == On Google Cloud Platform On AWS On Azure On Rackspace On a local machine

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  Social employee

  A social employee is a worker operating within a social business model. Following an organization's social computing guidelines, social employees use social media tools both for internal workflow and collaboration purposes and for external engagement with customers, prospects and stakeholders through a combination of social media marketing, content marketing, social marketing, and social selling. Social employee programs are considered to be as much about culture and engagement as they are about business processes and best practices. In addition to increased leads and sales, social employee best practices are said to improve business outcomes important to social media marketing, such as increased connections and web traffic, improved brand identification and "chatter", and better customer advocacy. == Overview == The term "social employee" was first introduced to describe those exhibiting the emerging characteristics of workers operating under a social business model. The term is often used interchangeably with similar designations like "employee advocate" or "social employee advocate". Crucial to the perceived value of the social employee is the concept of the digital footprint. While organizations are able to generate large bases of followers through social media, research shows that brand marketing and engagement efforts through these networks are not as effective as those of individual employees. In fact, some research indicates that employee experts are more trusted than any other member of an organization. Because of this, social employee programs are designed to train, empower, and support employee engagement efforts in the hopes of authentically engaging larger communities, increasing the frequency of shares, reviews, and other forms of "earned media" and expanding the brand's presence on the web. == The personal or employee brand == A foundational concept of the social employee is the idea of the personal or employee brand. This concept first gained popular attention in a 1997 FastCompany article by business leader Tom Peters titled "The Brand Called You". In the article, Peters argued that the premium placed on branding impacted workers' lives to such an extent that creating and cultivating a distinct personal brand had become a professional necessity. According to Peters, doing so built trust, loyalty, visibility, influence, and employability. With increased adoption of social media tools by both businesses and consumers in the early 21st century, many business leaders became increasingly concerned with social engagement, both internally among employees and externally with customers and other stakeholders. While many in the business community acknowledged the potential social tools had for improved collaboration, productivity, and brand messaging, the concern that employees would misrepresent their brand, disclose proprietary information, or otherwise damage their company's reputation or ability to conduct business persisted. As a result, many began to advocate for employee branding as a solution to this problem. This helped give new meaning to the concept of brand ambassadorship, positioning everyday employees in public, and potentially high-profile, engagement roles. == Characteristics == === Engaged === Because social employee advocacy is dependent on the perceived authenticity of the employee, engagement is highly valued in social organizations. Further, data show the measurable impact of employee engagement on organizational productivity and profitability: Happy employees were found to be 12 percent more productive. In one study, engaged employees were found to be 38 percent more likely to produce at above-average rates. In another, organizations with engaged employees had a 19 percent higher than average shareholder return, while organizations with disengaged employees experienced shareholder return that was 44 percent below average. Engaged companies were found to outperform disengaged companies by up to 202 percent. Companies with strong focus on culture were found to have an average 13.9 percent turnover rate, while those with a low focus experience were found to have a 48.4 percent turnover rate. === Flexible job environment and work–life balance === The number of professionals working mobile or remote has risen considerably since 2010. While estimates vary, one study found that number of organizations with mobile or remote employees is expected to rise from 24 percent in 2012 to 89 percent by 2020. Other research has estimated that by 2020, 105.4 million professionals will work remotely in America, about 72.3 percent of the total workforce. This change has been linked to a rise in social technologies, including biometrics, wearables, near-field communications, and augmented reality. Social employees have also put a greater emphasis on work–life balance, with many believing that advances in technology can directly support efforts in this area. Purported benefits of this shift include a more flexible workforce, reduced business costs, and greater organizational leverage in attracting and retaining top talent. === Buys into the brand's story === In 2009, thought leader Simon Sinek presented a speech called "How Great Leaders Inspire Action" at a TEDxPugetSound event. Sinek's central argument in this speech was, "People don't buy what you do. They buy why you do it." This concept—that the story behind a business or product offering is a more compelling sales tool than the product itself—is frequently cited in social media marketing as a way to build authentic connections with stakeholders. However, others have argued that for employees to share a brand's story authentically, they must be engaged in that story themselves, and as a result, many companies have made storytelling part of their culture programs. === Collaborative === An implicit tenet in social business is that social technologies aren't a barrier to productivity, but rather a path to increased connectivity. The shift in enterprise software systems like IBM Connections to incorporate social communication models, such as mentions, wikis, and newsfeeds, reflects the changing communication dynamics within business. With an increase in diversity and sophistication in collaborative software platforms, social organizations have sought to find new creative ways to utilize these tools and secure employee buy-in around them. Crowdsourcing has also become popular in social businesses. Examples include AT&T's program The Innovation Pipeline (TIP), begun in 2009, which has generated over 28,000 ideas that have led to over 75 projects with funding exceeding $44 million. IBM has also put considerable resources into such processes, producing its social computing guidelines through employee crowdsourcing, as well as its Connections platform through the Technology Adoption Program (TAP), a more formalized crowdsourcing initiative. Another popular form of internal collaboration is the hack day, or hackathon. Organizations such as Netflix, Facebook, and IBM use hack days to pull employees out of their day-to-day work environments and encourage them to collaborate in nontraditional ways in an attempt to drive disruptive innovation. Social employees are often encouraged to seek external collaboration opportunities with customers and prospects. For example, Procter & Gamble introduced the Live Well Collaborative to connect with external stakeholders and develop products and services for the 50+ demographic. === Social listener === A social listener is someone who engages in social listening, or social media monitoring, for professional means. Social employees can use social media monitoring for a variety of reasons, including professional development, industry news and trends, and gauging market sentiment. Some have argued that social listening is one of the most important components of social business, as it enables organizations to collect rich market data, make more informed strategic decisions, and respond to customer needs more authentically. === Customer-centric === Advocates of customer-centricity in social business argue that social media has changed the dynamic from one-way brand messaging to shared interactions between brand and customer. Brand and customer engagement is seen as a means of creating more lasting connections with customers and prospects and empowering them to become brand promoters. Customer-centric interactions are seen to have distinct value to brands, as research shows that prospects are far more likely to trust brand-related messaging from a friend or family member than they are from a brand. As a means of building social employees, some social advocates have also called for a broader definition of customer to include the employees themselves. In the book The Pursuit of Social Business Excellence, authors Vala Afshar and Brad Martin made the following argument: A social business operates with the guiding principle that each employee's responsi

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  Social television

  Social television is the union of television and social media. Millions of people now share their TV experience with other viewers on social media such as Twitter and Facebook using smartphones and tablets. TV networks and rights holders are increasingly sharing video clips on social platforms to monetise engagement and drive tune-in. The social TV market covers the technologies that support communication and social interaction around TV as well as companies that study television-related social behavior and measure social media activities tied to specific TV broadcasts – many of which have attracted significant investment from established media and technology companies. The market is also seeing numerous tie-ups between broadcasters and social networking players such as Twitter and Facebook. The market is expected to be worth $256bn by 2017. Social TV was named one of the 10 most important emerging technologies by the MIT Technology Review on Social TV in 2010. And in 2011, David Rowan, the editor of Wired magazine, named Social TV at number three of six in his peek into 2011 and what tech trends to expect to get traction. Ynon Kreiz, CEO of the Endemol Group told the audience at the Digital Life Design (DLD) conference in January 2011: "Everyone says that social television will be big. I think it's not going to be big—it's going to be huge". Much of the investment in the earlier years of social TV went into standalone social TV apps. The industry believed these apps would provide an appealing and complimentary consumer experience which could then be monetized with ads. These apps featured TV listings, check-ins, stickers and synchronised second-screen content but struggled to attract users away from Twitter and Facebook. Most of these companies have since gone out of business or been acquired amid a wave of consolidation and the market has instead focused on the activities of the social media channels themselves – such as Twitter Amplify, Facebook Suggested Videos and Snapchat Discover – and the technologies that support them. == Twitter == Twitter and Facebook are both helping users connect around media, which can provoke strong debate and engagement. Both social platforms want to be the 'digital watercooler' and host conversation around TV because the engagement and data about what media people consume can then be used to generate advertising revenue. As an open platform, conversation on Twitter is closely aligned with real-time events. In May 2013, it launched Twitter Amplify – an advertising product for media and consumer brands. With Amplify, Twitter runs video highlights from major live broadcasts, with advertisers' names and messages playing before the clip. By February 2014, all four major U.S. TV networks had signed up to the Amplify program, bringing a variety of premium TV content onto the social platform in the form of in-tweet real-time video clips. In June 2014, Twitter acquired its Twitter Amplify partner in the U.S. SnappyTV, a company that was helping broadcasters and rights holders to share video content both organically across social and via Twitter's Amplify program. Twitter continues to rely on Grabyo, which has also struck numerous deals with some of the largest broadcasters and rights holders in Europe and North America to share video content across Facebook and Twitter. == Facebook == Facebook made significant changes to its platform in 2014 including updates to its algorithm to enhance how it serves video in users' feeds. It also launched video autoplay to get users to watch the videos in their feeds. It rapidly surpassed Twitter and by the end of 2014 it was enjoying three billion video views a day on its platform and had announced a partnership with the NFL, one of Twitter's most active Twitter Amplify partners. In April 2015, at its F8 Developer Conference, it revealed it was working with Grabyo among other technology partners to bring video onto its platform. Then in July it announced it would be launching Facebook Suggested Videos, bringing related videos and ads to anyone that clicks on a video – a move that not only competed with Twitter's commercial video offering but also put it in direct competition with YouTube. == TV Time == TV Time is a television dedicated social network that allows users to keep track of the television series they watch, as well as films. It also allows them to express their reaction to the media they have seen with episode specific voting for favorite characters and emotional reaction to episodes, as well as commenting in episode restrictive pages. This way users are able to avoid spoilers while also finding a precise audience and community for each of their interactions, as opposed to bigger, non-television dedicated social medias such as Facebook and Twitter where the likelihood of unintentionally reading spoilers is much higher. TV Time offers an analytics service called "TVLytics" where the votes and reactions collected from users can be studied for research and television production purposes. == Advertising == According to Businessinsider.com, there are variety of applications for social TV, including support for TV ad sales, optimizing TV ad buys, making ad buys more efficient, as a complement to audience measurement, and eventually, audience forecasting and real-time optimization. Social TV data can ease access to focus groups and may create a positive feedback loop for generating ultra-sticky TV programming and multi-screen ad campaigns. == In numbers == Viewers share their TV experience on social media in real-time as events unfold: between 88-100m Facebook users login to the platform during the primetime hours of 8pm – 11pm in the US. The volume of social media engagement in TV is also rising – according to Nielsen SocialGuide, there was a 38% increase in tweets about TV in 2013 to 263m. For the 2014 Super Bowl, Twitter reported that a record 24.9 million tweets about the game were sent during the telecast, peaking at 381,605 tweets per minute. Facebook reported that 50 million people discussed the Super Bowl, generating 185 million interactions. The 2014 Oscars generated 5m tweets, viewed by an audience of 37m unique Twitter users and delivering 3.3bn impressions globally as conversation and key moments were shared virally across the platform. In 2014 the All England Lawn Tennis Club (AELTC), hosts of Wimbledon, used Grabyo to share video content across social. The videos were viewed 3.5 million times across Facebook and Twitter. In partnered with Grabyo again in 2015 and the videos generated over 48 million views across Facebook and Twitter. == Television shows with social integration == Here are some examples of how TV executives are integrating social elements with TV shows: C-SPAN streamed tweets from US Senators and Representatives during the quorum call The Voice had the judges of the program tweet during the show and the posts scrolls on the bottom of the screen. The use of Twitter also led to an increase in viewers. "Glee" Entertainment Weekly created a second screen viewing platform for the Glee season 3 premiere. == Related publications == Erika Jonietz. "Making TV Social, Virtually" MIT Technology Review. (January 11, 2010) AmigoTV (Alcatel-Lucent; Coppens et al.) – 2004 www.ist-ipmedianet.org/Alcatel_EuroiTV2004_AmigoTV_short_paper_S4-2.pdf Nextream (MIT Media Lab, Martin et al.) – 2010 Social Interactive Television: Immersive Shared Experiences and Perspectives (P. Cesar, D. Geerts, and K. Chorianopoulos (eds.)) – 2009 Social TV and the Emergence of Interactive TV – Multimedia Research Group – November 2010 Interactive Social TV on Service Oriented Environments: Challenges and Enablers (May 2011) == Systems == Boxee – acquired by Samsung GetGlue – acquired by i.TV Grabyo KIT digital Miso TV Tank Top TV WiO Xbox Live

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  Ultra (cryptography)

  Ultra was the designation adopted by British military intelligence in June 1941 for wartime signals intelligence obtained by breaking high-level encrypted enemy radio and teleprinter communications at the Government Code and Cypher School (GC&CS) at Bletchley Park. Ultra eventually became the standard designation among the western Allies for all such intelligence. The name arose because the intelligence obtained was considered more important than that designated by the highest British security classification then used (Most Secret) and so was regarded as being Ultra Secret. Several other cryptonyms had been used for such intelligence. The code name "Boniface" was used as a cover name for Ultra. In order to ensure that the successful code-breaking did not become apparent to the Germans, British intelligence created a fictional MI6 master spy, Boniface, who controlled a fictional series of agents throughout Germany. Information obtained through code-breaking was often attributed to the human intelligence from the Boniface network. The U.S. used the codename Magic for its decrypts from Japanese sources, including the "Purple" cipher. Much of the German cipher traffic was encrypted on the Enigma machine. Used properly, the German military Enigma would have been virtually unbreakable; in practice, shortcomings in operation allowed it to be broken. The term "Ultra" has often been used almost synonymously with "Enigma decrypts". However, Ultra also encompassed decrypts of the German Lorenz SZ 40/42 machines that were used by the German High Command, and the Hagelin machine. Many observers, at the time and later, regarded Ultra as immensely valuable to the Allies. Winston Churchill was reported to have told King George VI, when presenting to him Stewart Menzies (head of the Secret Intelligence Service and the person who controlled distribution of Ultra decrypts to the government): "It is thanks to the secret weapon of General Menzies, put into use on all the fronts, that we won the war!" F. W. Winterbotham quoted the western Supreme Allied Commander, Dwight D. Eisenhower, at war's end describing Ultra as having been "decisive" to Allied victory. Sir Harry Hinsley, Bletchley Park veteran and official historian of British Intelligence in World War II, made a similar assessment of Ultra, saying that while the Allies would have won the war without it, "the war would have been something like two years longer, perhaps three years longer, possibly four years longer than it was." However, Hinsley and others have emphasized the difficulties of counterfactual history in attempting such conclusions, and some historians, such as John Keegan, have said the shortening might have been as little as the three months it took the United States to deploy the atomic bomb. == Sources of intelligence == Most Ultra intelligence was derived from reading radio messages that had been encrypted with cipher machines, complemented by material from radio communications using traffic analysis and direction finding. In the early phases of the war, particularly during the eight-month Phoney War, the Germans could transmit most of their messages using land lines and so had no need to use radio. This meant that those at Bletchley Park had some time to build up experience of collecting and starting to decrypt messages on the various radio networks. German Enigma messages were the main source, with those of the German air force (the Luftwaffe) predominating, as they used radio more and their operators were particularly ill-disciplined. === German === ==== Enigma ==== "Enigma" refers to a family of electro-mechanical rotor cipher machines. These produced a polyalphabetic substitution cipher and were widely thought to be unbreakable in the 1920s, when a variant of the commercial Model D was first used by the Reichswehr. The German Army (Heer), Navy, Air Force, Nazi party, Gestapo and German diplomats used Enigma machines in several variants. Abwehr (German military intelligence) used a four-rotor machine without a plugboard and Naval Enigma used different key management from that of the army or air force, making its traffic far more difficult to cryptanalyse; each variant required different cryptanalytic treatment. The commercial versions were not as secure and Dilly Knox of GC&CS is said to have broken one before the war. German military Enigma was first broken in December 1932 by Marian Rejewski and the Polish Cipher Bureau, using a combination of brilliant mathematics, the services of a spy in the German office responsible for administering encrypted communications, and good luck. The Poles read Enigma to the outbreak of World War II and beyond, in France. At the turn of 1939, the Germans made the systems ten times more complex, which required a tenfold increase in Polish decryption equipment, which they could not meet. On 25 July 1939, the Polish Cipher Bureau handed reconstructed Enigma machines and their techniques for decrypting ciphers to the French and British. Gordon Welchman wrote, Ultra would never have got off the ground if we had not learned from the Poles, in the nick of time, the details both of the German military Enigma machine, and of the operating procedures that were in use. At Bletchley Park, some of the key people responsible for success against Enigma included mathematicians Alan Turing and Hugh Alexander and, at the British Tabulating Machine Company, chief engineer Harold Keen. After the war, interrogation of German cryptographic personnel led to the conclusion that German cryptanalysts understood that cryptanalytic attacks against Enigma were possible but were thought to require impracticable amounts of effort and investment. The Poles' early start at breaking Enigma and the continuity of their success gave the Allies an advantage when World War II began. ==== Lorenz cipher ==== In June 1941, the Germans started to introduce on-line stream cipher teleprinter systems for strategic point-to-point radio links, to which the British gave the code-name Fish. Several systems were used, principally the Lorenz SZ 40/42 (codenamed "Tunny" by the British) and Geheimfernschreiber ("Sturgeon"). These cipher systems were cryptanalysed, particularly Tunny, which the British thoroughly penetrated. It was eventually attacked using Colossus machines, which were the first digital programme-controlled electronic computers. In many respects the Tunny work was more difficult than for the Enigma, since the British codebreakers had no knowledge of the machine producing it and no head-start such as that the Poles had given them against Enigma. Although the volume of intelligence derived from this system was much smaller than that from Enigma, its importance was often far higher because it produced primarily high-level, strategic intelligence that was sent between Wehrmacht high command (Oberkommando der Wehrmacht, OKW). The eventual bulk decryption of Lorenz-enciphered messages contributed significantly, and perhaps decisively, to the defeat of Nazi Germany. Nevertheless, the Tunny story has become much less well known among the public than the Enigma one. At Bletchley Park, some of the key people responsible for success in the Tunny effort included mathematicians W. T. "Bill" Tutte and Max Newman and electrical engineer Tommy Flowers. === Italian === In June 1940, the Italians were using book codes for most of their military messages, except for the Italian Navy, which in early 1941 had started using a version of the Hagelin rotor-based cipher machine C-38. This was broken from June 1941 onwards by the Italian subsection of GC&CS at Bletchley Park. === Japanese === In the Pacific theatre, a Japanese cipher machine, called "Purple" by the Americans, was used for highest-level Japanese diplomatic traffic. It produced a polyalphabetic substitution cipher, but unlike Enigma, was not a rotor machine, being built around electrical stepping switches. It was broken by the US Army Signal Intelligence Service and disseminated as Magic. Detailed reports by the Japanese ambassador to Germany were encrypted on the Purple machine. His reports included reviews of German assessments of the military situation, reviews of strategy and intentions, reports on direct inspections by the ambassador (in one case, of Normandy beach defences), and reports of long interviews with Hitler. The Japanese are said to have obtained an Enigma machine in 1937, although it is debated whether they were given it by the Germans or bought a commercial version, which, apart from the plugboard and internal wiring, was the German Heer/Luftwaffe machine. Having developed a similar machine, the Japanese did not use the Enigma machine for their most secret communications. The chief fleet communications code system used by the Imperial Japanese Navy was called JN-25 by the Americans, and by early 1942 the US Navy had made considerable progress in decrypting Japanese naval messages. The US Army also made progress on the

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  Seq2seq

  Seq2seq is a family of machine learning approaches used for natural language processing. Originally developed by Lê Viết Quốc, a Vietnamese computer scientist and a machine learning pioneer at Google Brain, this framework has become foundational in many modern AI systems. Applications include language translation, image captioning, conversational models, speech recognition, and text summarization. Seq2seq uses sequence transformation: it turns one sequence into another sequence. == History == One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: 'This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode. seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a special case of communication. This viewpoint was elaborated, for example, in the noisy channel model of machine translation. In practice, seq2seq maps an input sequence into a real-numerical vector by using a neural network (the encoder), and then maps it back to an output sequence using another neural network (the decoder). The idea of encoder-decoder sequence transduction had been developed in the early 2010s. The papers most commonly cited as the originators that produced seq2seq are two papers from 2014. In the seq2seq as proposed by them, both the encoder and the decoder were LSTMs. This had the "bottleneck" problem, since the encoding vector has a fixed size, so for long input sequences, information would tend to be lost, as they are difficult to fit into the fixed-length encoding vector. The attention mechanism, proposed in 2014, resolved the bottleneck problem. They called their model RNNsearch, as it "emulates searching through a source sentence during decoding a translation". A problem with seq2seq models at this point was that recurrent neural networks are difficult to parallelize. The 2017 publication of Transformers resolved the problem by replacing the encoding RNN with self-attention Transformer blocks ("encoder blocks"), and the decoding RNN with cross-attention causally-masked Transformer blocks ("decoder blocks"). === Priority dispute === One of the papers cited as the originator for seq2seq is (Sutskever et al 2014), published at Google Brain while they were on Google's machine translation project. The research allowed Google to overhaul Google Translate into Google Neural Machine Translation in 2016. Tomáš Mikolov claims to have developed the idea (before joining Google Brain) of using a "neural language model on pairs of sentences... and then [generating] translation after seeing the first sentence"—which he equates with seq2seq machine translation, and to have mentioned the idea to Ilya Sutskever and Quoc Le (while at Google Brain), who failed to acknowledge him in their paper. Mikolov had worked on RNNLM (using RNN for language modelling) for his PhD thesis, and is more notable for developing word2vec. == Architecture == The main reference for this section is. === Encoder === The encoder is responsible for processing the input sequence and capturing its essential information, which is stored as the hidden state of the network and, in a model with attention mechanism, a context vector. The context vector is the weighted sum of the input hidden states and is generated for every time instance in the output sequences. === Decoder === The decoder takes the context vector and hidden states from the encoder and generates the final output sequence. The decoder operates in an autoregressive manner, producing one element of the output sequence at a time. At each step, it considers the previously generated elements, the context vector, and the input sequence information to make predictions for the next element in the output sequence. Specifically, in a model with attention mechanism, the context vector and the hidden state are concatenated together to form an attention hidden vector, which is used as an input for the decoder. The seq2seq method developed in the early 2010s uses two neural networks: an encoder network converts an input sentence into numerical vectors, and a decoder network converts those vectors to sentences in the target language. The Attention mechanism was grafted onto this structure in 2014 and is shown below. Later it was refined into the encoder-decoder Transformer architecture of 2017. === Training vs prediction === There is a subtle difference between training and prediction. During training time, both the input and the output sequences are known. During prediction time, only the input sequence is known, and the output sequence must be decoded by the network itself. Specifically, consider an input sequence x 1 : n {\displaystyle x_{1:n}} and output sequence y 1 : m {\displaystyle y_{1:m}} . The encoder would process the input x 1 : n {\displaystyle x_{1:n}} step by step. After that, the decoder would take the output from the encoder, as well as the as input, and produce a prediction y ^ 1 {\displaystyle {\hat {y}}_{1}} . Now, the question is: what should be input to the decoder in the next step? A standard method for training is "teacher forcing". In teacher forcing, no matter what is output by the decoder, the next input to the decoder is always the reference. That is, even if y ^ 1 ≠ y 1 {\displaystyle {\hat {y}}_{1}\neq y_{1}} , the next input to the decoder is still y 1 {\displaystyle y_{1}} , and so on. During prediction time, the "teacher" y 1 : m {\displaystyle y_{1:m}} would be unavailable. Therefore, the input to the decoder must be y ^ 1 {\displaystyle {\hat {y}}_{1}} , then y ^ 2 {\displaystyle {\hat {y}}_{2}} , and so on. It is found that if a model is trained purely by teacher forcing, its performance would degrade during prediction time, since generation based on the model's own output is different from generation based on the teacher's output. This is called exposure bias or a train/test distribution shift. A 2015 paper recommends that, during training, randomly switch between teacher forcing and no teacher forcing. === Attention for seq2seq === The attention mechanism is an enhancement introduced by Bahdanau et al. in 2014 to address limitations in the basic Seq2Seq architecture where a longer input sequence results in the hidden state output of the encoder becoming irrelevant for the decoder. It enables the model to selectively focus on different parts of the input sequence during the decoding process. At each decoder step, an alignment model calculates the attention score using the current decoder state and all of the attention hidden vectors as input. An alignment model is another neural network model that is trained jointly with the seq2seq model used to calculate how well an input, represented by the hidden state, matches with the previous output, represented by attention hidden state. A softmax function is then applied to the attention score to get the attention weight. In some models, the encoder states are directly fed into an activation function, removing the need for alignment model. An activation function receives one decoder state and one encoder state and returns a scalar value of their relevance. Consider the seq2seq language English-to-French translation task. To be concrete, let us consider the translation of "the zone of international control ", which should translate to "la zone de contrôle international ". Here, we use the special token as a control character to delimit the end of input for both the encoder and the decoder. An input sequence of text x 0 , x 1 , … {\displaystyle x_{0},x_{1},\dots } is processed by a neural network (which can be an LSTM, a Transformer encoder, or some other network) into a sequence of real-valued vectors h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } , where h {\displaystyle h} stands for "hidden vector". After the encoder has finished processing, the decoder starts operating over the hidden vectors, to produce an output sequence y 0 , y 1 , … {\displaystyle y_{0},y_{1},\dots } , autoregressively. That is, it always takes as input both the hidden vectors produced by the encoder, and what the decoder itself has produced before, to produce the next output word: ( h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } , "") → "la" ( h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } , " la") → "la zone" ( h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } , " la zone") → "la zone de" ... ( h 0 , h 1 , … {\displaystyle h_{0},h_{1},\dots } , " la zone de contrôle international") → "la zone de contrôle international " Here, we use the special token as a control character to delimit the start of input for the decoder. The decoding terminates as soon as "" appears in the decoder output. ==

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  Internet

  The Internet (or internet) is the global system of interconnected computer networks that uses the Internet protocol suite (TCP/IP) to communicate between networks and devices. It is a network of networks that comprises private, public, academic, business, and government networks of local to global scope, linked by electronic, wireless, and optical networking technologies. The Internet carries a vast range of information services and resources, such as the interlinked hypertext documents and applications of the World Wide Web (WWW), electronic mail, discussion groups, internet telephony, streaming media and file sharing. Most traditional communication media, including telephone, radio, television, paper mail, newspapers, and print publishing, have been transformed by the Internet, giving rise to new media such as email, online music, digital newspapers, news aggregators, and audio and video streaming websites. The Internet has enabled and accelerated new forms of personal interaction through instant messaging, Internet forums, and social networking services. Online shopping has also grown to occupy a significant market across industries, enabling firms to extend brick and mortar presences to serve larger markets. Business-to-business and financial services on the Internet affect supply chains across entire industries. The origins of the Internet date back to research that enabled the time-sharing of computer resources, the development of packet switching, and the design of computer networks for data communication. The set of communication protocols to enable internetworking on the Internet arose from research and development commissioned in the 1970s by the Defense Advanced Research Projects Agency (DARPA) of the United States Department of Defense in collaboration with universities and researchers across the United States, United Kingdom and France. The Internet has no single centralized governance in either technological implementation or policies for access and usage. Each constituent network sets its own policies. The overarching definitions of the two principal name spaces on the Internet, the Internet Protocol address (IP address) space and the Domain Name System (DNS), are directed by a maintainer organization, the Internet Corporation for Assigned Names and Numbers (ICANN). The technical underpinning and standardization of the core protocols is an activity of the non-profit Internet Engineering Task Force (IETF). == Terminology == The word internetted was used as early as 1849, meaning interconnected or interwoven. The word Internet was used in 1945 by the United States War Department in a radio operator's manual, and in 1974 as the shorthand form of Internetwork. Today, the term Internet most commonly refers to the global system of interconnected computer networks, though it may also refer to any group of smaller networks. The word Internet may be capitalized as a proper noun, although this is becoming less common. This reflects the tendency in English to capitalize new terms and move them to lowercase as they become familiar. The word is sometimes still capitalized to distinguish the global internet from smaller networks, though many publications, including the AP Stylebook since 2016, recommend the lowercase form in every case. In 2016, the Oxford English Dictionary found that, based on a study of around 2.5 billion printed and online sources, "Internet" was capitalized in 54% of cases. The terms Internet and World Wide Web are often used interchangeably; it is common to speak of "going on the Internet" when using a web browser to view web pages. However, the World Wide Web, or the Web, is only one of a large number of Internet services. It is the global collection of web pages, documents and other web resources linked by hyperlinks and URLs. == History == === 1960s === In the 1960s, computer scientists began developing systems for time-sharing of computer resources. J. C. R. Licklider proposed the idea of a universal network while working at Bolt Beranek & Newman and, later, leading the Information Processing Techniques Office at the Advanced Research Projects Agency (ARPA) of the United States Department of Defense. Research into packet switching, one of the fundamental Internet technologies, started in the work of Paul Baran at RAND in the early 1960s and, independently, Donald Davies at the United Kingdom's National Physical Laboratory in 1965. After the Symposium on Operating Systems Principles in 1967, packet switching from the proposed NPL network was incorporated into the design of the ARPANET, an experimental resource sharing network proposed by ARPA. ARPANET development began with two network nodes which were interconnected between the University of California, Los Angeles and the Stanford Research Institute on 29 October 1969. The third site was at the University of California, Santa Barbara, followed by the University of Utah. === 1970s === By the end of 1971, 15 sites were connected to the young ARPANET. Thereafter, the ARPANET gradually developed into a decentralized communications network, connecting remote centers and military bases in the United States. Other user networks and research networks, such as the Merit Network and CYCLADES, were developed in the late 1960s and early 1970s. Early international collaborations for the ARPANET were rare. Connections were made in 1973 to Norway (NORSAR and, later, NDRE) and to Peter Kirstein's research group at University College London, which provided a gateway to British academic networks, the first internetwork for resource sharing. ARPA projects, the International Network Working Group and commercial initiatives led to the development of various protocols and standards by which multiple separate networks could become a single network, or a network of networks. In 1974, Vint Cerf at Stanford University and Bob Kahn at DARPA published a proposal for "A Protocol for Packet Network Intercommunication". Cerf and his graduate students used the term internet as a shorthand for internetwork in RFC 675. The Internet Experiment Notes and later RFCs repeated this use. The work of Louis Pouzin and Robert Metcalfe had important influences on the resulting TCP/IP design. National PTTs and commercial providers developed the X.25 standard and deployed it on public data networks. === 1980s === The ARPANET initially served as a backbone for the interconnection of regional academic and military networks in the United States to enable resource sharing. Access to the ARPANET was expanded in 1981 when the National Science Foundation (NSF) funded the Computer Science Network (CSNET). In 1982, the Internet Protocol Suite (TCP/IP) was standardized, which facilitated worldwide proliferation of interconnected networks. TCP/IP network access expanded again in 1986 when the National Science Foundation Network (NSFNet) provided access to supercomputer sites in the United States for researchers, first at speeds of 56 kbit/s and later at 1.5 Mbit/s and 45 Mbit/s. The NSFNet expanded into academic and research organizations in Europe, Australia, New Zealand and Japan in 1988–89. Although other network protocols such as UUCP and PTT public data networks had global reach well before this time, this marked the beginning of the Internet as an intercontinental network. Commercial Internet service providers emerged in 1989 in the United States and Australia. The ARPANET was decommissioned in 1990. === 1990s === The linking of commercial networks and enterprises by the early 1990s, as well as the advent of the World Wide Web, marked the beginning of the transition to the modern Internet. Steady advances in semiconductor technology and optical networking created new economic opportunities for commercial involvement in the expansion of the network in its core and for delivering services to the public. In mid-1989, MCI Mail and Compuserve established connections to the Internet, delivering email and public access products to the half million users of the Internet. Just months later, on 1 January 1990, PSInet launched an alternate Internet backbone for commercial use; one of the networks that added to the core of the commercial Internet of later years. In March 1990, the first high-speed T1 (1.5 Mbit/s) link between the NSFNET and Europe was installed between Cornell University and CERN, allowing much more robust communications than were capable with satellites. Later in 1990, Tim Berners-Lee began writing WorldWideWeb, the first web browser, after two years of lobbying CERN management. By Christmas 1990, Berners-Lee had built all the tools necessary for a working Web: the HyperText Transfer Protocol (HTTP) 0.9, the HyperText Markup Language (HTML), the first Web browser (which was also an HTML editor and could access Usenet newsgroups and FTP files), the first HTTP server software (later known as CERN httpd), the first web server, and the first Web pages that described the project itself. In 1991 the

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  Coreu

  COREU (French: Correspondance Européenne – Telex network of European correspondents, also EUKOR-Netzwerk in Austria) is a communication network of the European Union for the communication of the Council of the European Union, the European correspondents of the foreign ministries of the EU member states, permanent representatives of member states in Brussels, the European Commission, and the General Secretariat of the Council of the European Union. The European Parliament is not among the participants. COREU is the European equivalent of the American Secret Internet Protocol Router Network (SIPRNet, also known as Intelink-S). COREU's official aim is fast communication in case of crisis. The network enables a closer cooperation in matters regarding foreign affairs. In actuality the system's function exceeds that of mere communication, it also enables decision-making. COREU's first goal is to enable the exchange of information before and after decisions. Relaying upfront negotiations in preparation of meetings is the second goal. In addition, the system also allows the editing of documents and the decision-making, especially if there is little time. While the first two goals are preparatory measures for a shared foreign policy, the third is a methodical variant marked by practise that is defining for the image of the Common Foreign and Security Policy. == Members == (The following information dates from 2013): There is one representative in each of the capital cities in the EU.(since 1973) In Germany for example, this is the European correspondent (EU-KOR) from the Foreign Office. In Austria it is the European correspondent from the Referat II.1.a in the Federal Ministry for Europe, Integration and Foreign Affairs They are the correspondents (since 1982) for the European Commission They comprise the secretariat for the European Council They also make up the European External Action Service (EEAS) (responsible for foreign policy issues, since 1987) == Data volume and technical details == COREU functions as a spoke-hub distribution paradigm system with the hub in Brussels. The network is operated by the European Union Intelligence and Situation Centre (formerly Joint Situation Center, JSC). The technical infrastructure is located in a building of the European Council. COREU may be described as an advanced telex system with encrypted messages via dedicated terminals. Once a message has reached the destination, it is then redistributed via the local media. In contrast, messages of governments are transmitted via local media to the correspondents and from there delivered point-to-point to Brussels via COREU. In 2010, approximately 8500 communications had been distributed over this network. == History == A telex-based communication system under the name COREU was established in 1973. Originally, only the ministries of Foreign Affairs in the European capitals were connected to it. This telex system was replaced in 1997 by the mail system CORTESY (COREU Terminal Equipment System). The name was retained despite the technical innovation. COREU was reportedly compromised by hackers working for the People's Liberation Army Strategic Support Force, allowing for the theft of thousands of low-classified documents and diplomatic cables.

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  Time-lock puzzle

  A time-lock puzzle, or time-released cryptography, encrypts a message that cannot be decrypted until a specified amount of time has passed. The concept was first described by Timothy C. May, and a solution first introduced by Ron Rivest, Adi Shamir, and David A. Wagner in 1996. Time-lock puzzle are useful in cases where confidentiality of information is determined by time, such as a diarist who does not want their views released until 50 years after their death, an auction where bids are sealed until the bidding period is closed, electronic voting, and contract signing. They can additionally be used in creating further cryptographic primitives, such as verifiable delay functions and zero knowledge proofs. Time-released cryptography can be achieved through several different mechanisms. Use mathematical problems requiring sequential calculations to solve, and cannot be solved with parallelization. Thus, adding more computers to a problem will not help solve the problem faster. Use of a trusted agent, or multiple agents who each hold a part of the message and cryptographic keys, who release the message after a specified time period has passed. Distribute public encryption keys to users, and place private cryptographic keys with a trusted agent in an offline location, to be released at a later date.

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  List of color palettes

  The following is a list that contains color palettes for notable computer graphics, terminals and video game consoles. Only a simulated image using a palette and its name are given. Main articles are linked from the name of each palette, test charts, sample colours, simulated images, and further technical details (including references). During older eras of computing, manufacturers developed many different display systems often in a competitive, non-collaborative basis (with a few exceptions in the VESA consortium), creating many proprietary, non-standard different instances of display hardware. Often, as with early personal and home computers, a given machine employed its unique display subsystem, also with its unique color palette. Furthermore, software developers had made use of the color abilities of distinct display systems in many different ways. The result is that there is no single common standard nomenclature or classification taxonomy which can encompass every computer color palette. In order to organize the material, color palettes have been grouped following certain criteria. First, generic monochrome and full RGB repertories common to various computer display systems are listed. Then, usual color repertories used for display systems that employ indexed color techniques. And finally, specific manufacturers' color palettes implemented in many representative early personal computers and video game consoles of various brands. The list for personal computer palettes is split into two categories: 8-bit and 16-bit machines. This is not intended as a true strict categorization of such machines, because mixed architectures also exist (16-bit processors with an 8-bit data bus or 32-bit processors with a 16-bit data bus, among others). The distinction is based more on broad 8-bit and 16-bit computer ages or generations (around 1975–1985 and 1985–1995, respectively) and their associated state of the art in color display capabilities. The following is the common color test chart and sample image used to render each palette in this list: See further details in the summary paragraph of the corresponding article. == List of monochrome and RGB palettes == In this article, the term monochrome palette means a set of intensities for a monochrome display, and the term RGB palette is defined as the complete set of combinations a given RGB display can offer by mixing all the possible intensities of the red, green, and blue primaries available in its hardware. These are generic complete repertories of colors to produce black and white and RGB color pictures by the display hardware, not necessarily the total number of such colors that can be simultaneously displayed in a given text or graphic mode of any machine. RGB is the most common method to produce colors for displays; so these complete RGB color repertories have every possible combination of R-G-B triplets within any given maximum number of levels per component. For specific hardware and different methods to produce colors than RGB, see the List of computer hardware palettes and the List of video game consoles sections. For various software arrangements and sorts of colors, including other possible full RGB arrangements within 8-bit depth displays, see the List of software palettes section. === Monochrome palettes === These palettes only have shades of gray. === Dichrome palettes === Each permuted pair of red, green, and blue (16-bit color palette, with 65,536 colors). For example, "additive red green" has zero blue and "subtractive red green" has full blue. === Regular RGB palettes === These full RGB palettes employ the same number of bits to store the relative intensity for the red, green and blue components of every image's pixel color. Thus, they have the same number of levels per channel and the total number of possible colors is always the cube of a power of two. It should be understood that 'when developed' many of these formats were directly related to the size of some host computers 'natural word length' in bytes—the amount of memory in bits held by a single memory address such that the CPU can grab or put it in one operation. === Non-regular RGB palettes === These are also RGB palettes, in the sense defined above (except for 4-bit RGBI, which has an intensity bit that affects all channels at once), but either they do not have the same number of levels for each primary channel, or the numbers are not powers of two, so are not represented as separate bit fields. All of these have been used in popular personal computers. == List of software palettes == Systems that use a 4-bit or 8-bit pixel depth can display up to 16 or 256 colors simultaneously. Many personal computers in the later 1980s and early 1990s displayed at most 256 different colors, freely selected by software (either by the user or by a program) from their wider hardware's color palette. Usual selections of colors in limited subsets (generally 16 or 256) of the full palette includes some RGB level arrangements commonly used with the 8 bpp palettes as master palettes or universal palettes (i.e., palettes for multipurpose uses). These are some representative software palettes, but any selection can be made in such types of systems. === System specific palettes === These are selections of colors officially employed as system palettes in some popular operating systems for personal computers that feature 8-bit displays. === RGB arrangements === These are selections of colors based on evenly ordered RGB levels, mainly used as master palettes to display any kind of image within the limitations of the 8-bit pixel depth. === Other common uses of software palettes === == List of computer hardware palettes == In old personal computers and terminals that offered color displays, some color palettes were chosen algorithmically to provide the most diverse set of colors for a given palette size, and others were chosen to assure the availability of certain colors. In many early home computers, especially when the palette choices were determined at the hardware level by resistor combinations, the palette was determined by the manufacturer. Many early models output composite video colors. When seen on TV devices, the perception of the colors may not correspond with the value levels for the color values employed (most noticeable with NTSC TV color system). For current RGB display systems for PCs (Super VGA, etc.), see the 16-bit RGB and 24-bit RGB for High Color (thousands) and True Color (millions of colors) modes. For video game consoles, see the List of video game consoles section. For every model, their main different graphical color modes are listed based exclusively in the way they handle colors on screen, not all their different screen modes. The list is organized roughly historically by video hardware, not by branch. They are listed according to the original model of each system, which means that extended versions, clones, and compatibles also support the original palette. === Terminals and 8-bit machines === === 16-bit machines === === Video game console palettes === Color palettes of some of the most popular video game consoles. The criteria are the same as those of the List of computer hardware palettes section.

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  InfiniBand

  InfiniBand (IB) is a computer networking standard used in high-performance computing that features very high throughput and very low latency. It is used for data interconnect both among and within computers. InfiniBand is also used as either a direct or switched interconnect between servers and storage systems, as well as an interconnect between storage systems. It is designed to be scalable and uses a switched fabric network topology. Between 2014 and June 2016, it was the most commonly used interconnect in the TOP500 list of supercomputers. Mellanox (acquired by Nvidia) manufactures InfiniBand host bus adapters and network switches, which are used by large computer system and database vendors in their product lines. As a computer cluster interconnect, IB competes with Ethernet, Fibre Channel, and Intel Omni-Path. The technology is promoted by the InfiniBand Trade Association. == History == InfiniBand originated in 1999 from the merger of two competing designs: Future I/O and Next Generation I/O (NGIO). NGIO was led by Intel, with a specification released in 1998, and joined by Sun Microsystems and Dell. Future I/O was backed by Compaq, IBM, and Hewlett-Packard. This led to the formation of the InfiniBand Trade Association (IBTA), which included both sets of hardware vendors as well as software vendors such as Microsoft. At the time it was thought some of the more powerful computers were approaching the interconnect bottleneck of the PCI bus, in spite of upgrades like PCI-X. Version 1.0 of the InfiniBand Architecture Specification was released in 2000. Initially the IBTA vision for IB was simultaneously a replacement for PCI in I/O, Ethernet in the machine room, cluster interconnect and Fibre Channel. IBTA also envisaged decomposing server hardware on an IB fabric. Mellanox had been founded in 1999 to develop NGIO technology, but by 2001 shipped an InfiniBand product line called InfiniBridge at 10 Gbit/second speeds. Following the burst of the dot-com bubble there was hesitation in the industry to invest in such a far-reaching technology jump. By 2002, Intel announced that instead of shipping IB integrated circuits ("chips"), it would focus on developing PCI Express, and Microsoft discontinued IB development in favor of extending Ethernet. Sun Microsystems and Hitachi continued to support IB. In 2003, the System X supercomputer built at Virginia Tech used InfiniBand in what was estimated to be the third largest computer in the world at the time. The OpenIB Alliance (later renamed OpenFabrics Alliance) was founded in 2004 to develop an open set of software for the Linux kernel. By February, 2005, the support was accepted into the 2.6.11 Linux kernel. In November 2005 storage devices finally were released using InfiniBand from vendors such as Engenio. Cisco, desiring to keep technology superior to Ethernet off the market, adopted a "buy to kill" strategy. Cisco successfully killed InfiniBand switching companies such as Topspin via acquisition. Of the top 500 supercomputers in 2009, Gigabit Ethernet was the internal interconnect technology in 259 installations, compared with 181 using InfiniBand. In 2010, market leaders Mellanox and Voltaire merged, leaving just one other IB vendor, QLogic, primarily a Fibre Channel vendor. At the 2011 International Supercomputing Conference, links running at about 56 gigabits per second (known as FDR, see below), were announced and demonstrated by connecting booths in the trade show. In 2012, Intel acquired QLogic's InfiniBand technology, leaving only one independent supplier. By 2014, InfiniBand was the most popular internal connection technology for supercomputers, although within two years, 10 Gigabit Ethernet started displacing it. In 2016, it was reported that Oracle Corporation (an investor in Mellanox) might engineer its own InfiniBand hardware. In 2019 Nvidia acquired Mellanox, the last independent supplier of InfiniBand products. == Specification == Specifications are published by the InfiniBand trade association. === Performance === Original names for speeds were single-data rate (SDR), double-data rate (DDR) and quad-data rate (QDR) as given below. Subsequently, other three-letter initialisms were added for even higher data rates. Notes Each link is duplex. Links can be aggregated: most systems use a 4 link/lane connector (QSFP). HDR often makes use of 2x links (aka HDR100, 100 Gb link using 2 lanes of HDR, while still using a QSFP connector). NDR introduced OSFP connectors which host one or two links at 2x (NDR200) or 4x (NDR400). They are not logically configured as a single 8x link, even when connecting switches together with an OSFP cable. InfiniBand provides remote direct memory access (RDMA) capabilities for low CPU overhead. === Topology === InfiniBand uses a switched fabric topology, as opposed to early shared medium Ethernet. All transmissions begin or end at a channel adapter. Each processor contains a host channel adapter (HCA) and each peripheral has a target channel adapter (TCA). These adapters can also exchange information for security or quality of service (QoS). === Messages === InfiniBand transmits data in packets of up to 4 KB that are taken together to form a message. A message can be: a remote direct memory access read or write a channel send or receive a transaction-based operation (that can be reversed) a multicast transmission an atomic operation === Physical interconnection === In addition to a board form factor connection, it can use both active and passive copper (up to 10 meters) and optical fiber cable (up to 10 km). QSFP connectors are used. The InfiniBand Association also specified the CXP connector system for speeds up to 120 Gbit/s over copper, active optical cables, and optical transceivers using parallel multi-mode fiber cables with 24-fiber MPO connectors. === Software interfaces === Mellanox operating system support is available for Solaris, FreeBSD, Red Hat Enterprise Linux, SUSE Linux Enterprise Server (SLES), Windows, HP-UX, VMware ESX, and AIX. InfiniBand has no specific standard application programming interface (API). The standard only lists a set of verbs such as ibv_open_device or ibv_post_send, which are abstract representations of functions or methods that must exist. The syntax of these functions is left to the vendors. Sometimes for reference this is called the verbs API. The de facto standard software is developed by OpenFabrics Alliance and called the Open Fabrics Enterprise Distribution (OFED). It is released under two licenses GPL2 or BSD license for Linux and FreeBSD, and as Mellanox OFED for Windows (product names: WinOF / WinOF-2; attributed as host controller driver for matching specific ConnectX 3 to 5 devices) under a choice of BSD license for Windows. It has been adopted by most of the InfiniBand vendors, for Linux, FreeBSD, and Microsoft Windows. IBM refers to a software library called libibverbs, for its AIX operating system, as well as "AIX InfiniBand verbs". The Linux kernel support was integrated in 2005 into the kernel version 2.6.11. === Ethernet over InfiniBand === Ethernet over InfiniBand, abbreviated to EoIB, is an Ethernet implementation over the InfiniBand protocol and connector technology. EoIB enables multiple Ethernet bandwidths varying on the InfiniBand (IB) version. Ethernet's implementation of the Internet Protocol Suite, usually referred to as TCP/IP, is different in some details compared to the direct InfiniBand protocol in IP over IB (IPoIB).

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  KLJN Secure Key Exchange

  Random-resistor-random-temperature Kirchhoff-law-Johnson-noise key exchange, also known as RRRT-KLJN or simply KLJN, is an approach for distributing cryptographic keys between two parties that claims to offer unconditional security. This claim, which has been contested, is significant, as the only other key exchange approach claiming to offer unconditional security is Quantum key distribution. The KLJN secure key exchange scheme was proposed in 2005 by Laszlo Kish and Granqvist. It has the advantage over quantum key distribution in that it can be performed over a metallic wire with just four resistors, two noise generators, and four voltage measuring devices---equipment that is low-priced and can be readily manufactured. It has the disadvantage that several attacks against KLJN have been identified which must be defended against. "Given that the amount of effort and funding that goes into Quantum Cryptography is substantial (some even mock it as a distraction from the ultimate prize which is quantum computing), it seems to me that the fact that classic thermodynamic resources allow for similar inherent security should give one pause," wrote Henning Dekant, the founder of the Quantum Computing Meetup, in April 2013. The Cybersecurity Curricula 2017, a joint project of the Association for Computing Machinery, the IEEE Computer Society, the Association for Information Systems, and the International Federation for Information Processing Technical Committee on Information Security Education (IFIP WG 11.8) recommends teaching the KLJN Scheme as part of teaching "Advanced concepts" in its knowledge unit on cryptography. == See Also/Further Reading ==

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