AI App How To Use

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  Inductive programming

  Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints. Depending on the programming language used, there are several kinds of inductive programming. Inductive functional programming, which uses functional programming languages such as Lisp or Haskell, and most especially inductive logic programming, which uses logic programming languages such as Prolog and other logical representations such as description logics, have been more prominent, but other (programming) language paradigms have also been used, such as constraint programming or probabilistic programming. == Definition == Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases. Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language. In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete. In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples. The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint programming, probabilistic programming, abductive logic programming, modal logic, action languages, agent languages and many types of imperative languages. == History == The early works of Plotkin, and his "relative least general generalization (rlgg)", had an enormous impact in inductive logic programming. There were some encouraging results on learning recursive Prolog programs such as quicksort from examples together with suitable background knowledge, for example with GOLEM. However, after initial success, the community got disappointed by limited progress about the induction of recursive programs with ILP less and less focusing on recursive programs and leaning more and more towards a machine learning setting with applications in relational data mining and knowledge discovery. In parallel to work in ILP, Koza proposed genetic programming in the early 1990s as a generate-and-test based approach to learning programs. The idea of genetic programming was further developed into the inductive programming system ADATE and the systematic-search-based system MagicHaskeller. Here again, functional programs are learned from sets of positive examples together with an output evaluation (fitness) function which specifies the desired input/output behavior of the program to be learned. The early work in grammar induction (also known as grammatical inference) is related to inductive programming, as rewriting systems or logic programs can be used to represent production rules. In fact, early works in inductive inference considered grammar induction and Lisp program inference as basically the same problem. The results in terms of learnability were related to classical concepts, such as identification-in-the-limit, as introduced in the seminal work of Gold. More recently, the language learning problem was addressed by the inductive programming community. In the recent years, the classical approaches have been resumed and advanced with great success. Therefore, the synthesis problem has been reformulated on the background of constructor-based term rewriting systems taking into account modern techniques of functional programming, as well as moderate use of search-based strategies and usage of background knowledge as well as automatic invention of subprograms. Many new and successful applications have recently appeared beyond program synthesis, most especially in the area of data manipulation, programming by example and cognitive modelling (see below). Other ideas have also been explored with the common characteristic of using declarative languages for the representation of hypotheses. For instance, the use of higher-order features, schemes or structured distances have been advocated for a better handling of recursive data types and structures; abstraction has also been explored as a more powerful approach to cumulative learning and function invention. One powerful paradigm that has been recently used for the representation of hypotheses in inductive programming (generally in the form of generative models) is probabilistic programming (and related paradigms, such as stochastic logic programs and Bayesian logic programming). == Application areas == The first workshop on Approaches and Applications of Inductive Programming (AAIP) Archived 2016-03-03 at the Wayback Machine held in conjunction with ICML 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the domain of software engineering where structural learning, software assistants and software agents can help to relieve programmers from routine tasks, give programming support for end users, or support of novice programmers and programming tutor systems. Further areas of application are language learning, learning recursive control rules for AI-planning, learning recursive concepts in web-mining or for data-format transformations". Since then, these and many other areas have shown to be successful application niches for inductive programming, such as end-user programming, the related areas of programming by example and programming by demonstration, and intelligent tutoring systems. Other areas where inductive inference has been recently applied are knowledge acquisition, artificial general intelligence, reinforcement learning and theory evaluation, and cognitive science in general. There may also be prospective applications in intelligent agents, games, robotics, personalisation, ambient intelligence and human interfaces.

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  Quality of experience

  Quality of experience (QoE) is a measure of the delight or annoyance of a customer's experiences with a service (e.g., web browsing, phone call, TV broadcast). QoE focuses on the entire service experience; it is a holistic concept, similar to the field of user experience, but with its roots in telecommunication. QoE is an emerging multidisciplinary field based on social psychology, cognitive science, economics, and engineering science, focused on understanding overall human quality requirements. == Definition and concepts == In 2013, within the context of the COST Action QUALINET, QoE has been defined as:The degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and / or enjoyment of the application or service in the light of the user’s personality and current state.This definition has been adopted in 2016 by the International Telecommunication Union in Recommendation ITU-T P.10/G.100. Before, various definitions of QoE had existed in the domain, with the above-mentioned definition now finding wide acceptance in the community. QoE has historically emerged from Quality of Service (QoS), which attempts to objectively measure service parameters (such as packet loss rates or average throughput). QoS measurement is most of the time not related to a customer, but to the media or network itself. QoE however is a purely subjective measure from the user's perspective of the overall quality of the service provided, by capturing people's aesthetic and hedonic needs. QoE looks at a vendor's or purveyor's offering from the standpoint of the customer or end user, and asks, "What mix of goods, services, and support, do you think will provide you with the perception that the total product is providing you with the experience you desired and/or expected?" It then asks, "Is this what the vendor/purveyor has actually provided?" If not, "What changes need to be made to enhance your total experience?" In short, QoE provides an assessment of human expectations, feelings, perceptions, cognition and satisfaction with respect to a particular product, service or application. QoE is a blueprint of all human subjective and objective quality needs and experiences arising from the interaction of a person with technology and with business entities in a particular context. Although QoE is perceived as subjective, it is an important measure that counts for customers of a service. Being able to measure it in a controlled manner helps operators understand what may be wrong with their services and how to improve them. == QoE factors == QoE aims at taking into consideration every factor that contributes to a user's perceived quality of a system or service. This includes system, human and contextual factors. The following so-called "influence factors" have been identified and classified by Reiter et al.: Human Influence Factors Low-level processing (visual and auditory acuity, gender, age, mood, …) Higher-level processing (cognitive processes, socio-cultural and economic background, expectations, needs and goals, other personality traits…) System Influence Factors Content-related Media-related (encoding, resolution, sample rate, …) Network-related (bandwidth, delay, jitter, …) Device-related (screen resolution, display size, …) Context Influence Factors Physical context (location and space) Temporal context (time of day, frequency of use, …) Social context (inter-personal relations during experience) Economic context Task context (multitasking, interruptions, task type) Technical and information context (relationship between systems) Studies in the field of QoE have typically focused on system factors, primarily due to its origin in the QoS and network engineering domains. Through the use of dedicated test laboratories, the context is often sought to be kept constant. == QoE versus User Experience == QoE is strongly related to but different from the field of User Experience (UX), which also focuses on users' experiences with services. Historically, QoE has emerged from telecommunication research, while UX has its roots in Human–Computer Interaction. Both fields can be considered multi-disciplinary. In contrast to UX, the goal of improving QoE for users was more strongly motivated by economic needs. Wechsung and De Moor identify the following key differences between the fields: == QoE measurement == As a measure of the end-to-end performance at the service level from the user's perspective, QoE is an important metric for the design of systems and engineering processes. This is particularly relevant for video services because – due to their high traffic demands –, bad network performance may highly affect the user's experience. So, when designing systems, the expected output, i.e. the expected QoE, is often taken into account – also as a system output metric and optimization goal. To measure this level of QoE, human ratings can be used. The mean opinion score (MOS) is a widely used measure for assessing the quality of media signals. It is a limited form of QoE measurement, relating to a specific media type, in a controlled environment and without explicitly taking into account user expectations. The MOS as an indicator of experienced quality has been used for audio and speech communication, as well as for the assessment of quality of Internet video, television and other multimedia signals, and web browsing. Due to inherent limitations in measuring QoE in a single scalar value, the usefulness of the MOS is often debated. Subjective quality evaluation requires a lot of human resources, establishing it as a time-consuming process. Objective evaluation methods can provide quality results faster, but require dedicated computing resources. Since such instrumental video quality algorithms are often developed based on a limited set of subjective data, their QoE prediction accuracy may be low when compared to human ratings. QoE metrics are often measured at the end devices and can conceptually be seen as the remaining quality after the distortion introduced during the preparation of the content and the delivery through the network, until it reaches the decoder at the end device. There are several elements in the media preparation and delivery chain, and some of them may introduce distortion. This causes degradation of the content, and several elements in this chain can be considered as "QoE-relevant" for the offered services. The causes of degradation are applicable for any multimedia service, that is, not exclusive to video or speech. Typical degradations occur at the encoding system (compression degradation), transport network, access network (e.g., packet loss or packet delay), home network (e.g. WiFi performance) and end device (e.g. decoding performance). == QoE management == Several QoE-centric network management and bandwidth management solutions have been proposed, which aim to improve the QoE delivered to the end-users. When managing a network, QoE fairness may be taken into account in order to keep the users sufficiently satisfied (i.e., high QoE) in a fair manner. From a QoE perspective, network resources and multimedia services should be managed in order to guarantee specific QoE levels instead of classical QoS parameters, which are unable to reflect the actual delivered QoE. A pure QoE-centric management is challenged by the nature of the Internet itself, as the Internet protocols and architecture were not originally designed to support today's complex and high demanding multimedia services. As an example for an implementation of QoE management, network nodes can become QoE-aware by estimating the status of the multimedia service as perceived by the end-users. This information can then be used to improve the delivery of the multimedia service over the network and proactively improve the users' QoE. This can be achieved, for example, via traffic shaping. QoE management gives the service provider and network operator the capability to minimize storage and network resources by allocating only the resources that are sufficient to maintain a specific level of user satisfaction. As it may involve limiting resources for some users or services in order to increase the overall network performance and QoE, the practice of QoE management requires that net neutrality regulations are considered.

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  User-generated content

  User-generated content (UGC), alternatively known as user-created content (UCC), is content generated by users of the Internet such as images, videos, audio, text, testimonials, software, and user interactions. Online content aggregation platforms such as social media, discussion forums and wikis by their interactive and social nature, no longer produce multimedia content but provide tools to produce, collaborate, and share a variety of content, which can affect the attitudes and behaviors of the audience in various aspects. This transforms the role of consumers from passive spectators to active participants. User-generated content is used for a wide range of applications, including problem processing, news, entertainment, customer engagement, advertising, gossip, research and more. It is an example of the democratization of content production and the flattening of traditional media hierarchies. The BBC adopted a user-generated content platform for its websites in 2005, and Time magazine named "You" as the Person of the Year in 2006, referring to the rise in the production of UGC on Web 2.0 platforms. CNN also developed a similar user-generated content platform, known as iReport. There are other examples of news channels implementing similar protocols, especially in the immediate aftermath of a catastrophe or terrorist attack. Social media users can provide key eyewitness content and information that may otherwise have been inaccessible. Since 2020, there has been an increasing number of businesses who are utilizing User Generated Content (UGC) to promote their products and services. Several factors significantly influence how UGC is received, including the quality of the content, the credibility of the creator, and viewer engagement. These elements can impact users' perceptions and trust towards the brand, as well as influence the buying intentions of potential customers. UGC has proven to be an effective method for brands to connect with consumers, drawing their attention through the sharing of experiences and information on social media platforms. Due to new media and technology affordances, such as low cost and low barriers to entry, the Internet is an easy platform to create and dispense user-generated content, allowing the dissemination of information at a rapid pace in the wake of an event. == Definition == The advent of user-generated content marked a shift among media organizations from creating online content to providing facilities for amateurs to publish their own content. User-generated content has also been characterized as citizen media as opposed to the "packaged goods media" of the past century. Citizen Media is audience-generated feedback and news coverage. People give their reviews and share stories in the form of user-generated and user-uploaded audio and user-generated video. The former is a two-way process in contrast to the one-way distribution of the latter. Conversational or two-way media is a key characteristic of so-called Web 2.0, which encourages the publishing of one's own content and commenting on other people's content. The role of the passive audience, therefore, has shifted since the birth of new media, and an ever-growing number of participatory users are taking advantage of these interactive opportunities, especially on the Internet, to create independent content. Grassroots experimentation then generated an innovation in sounds, artists, techniques, and associations with audiences, which then are being used in mainstream media. The active, participatory, and creative audience is prevailing today with relatively accessible media, tools, and applications, and its culture is in turn affecting mass media corporations and global audiences. The Organisation for Economic Co-operation and Development (OECD) has defined three core variables for UGC: Accessible Content: User-generated content (UGC) is publicly produced through platforms located on the Internet and is available to any individual browsing such a publicly accessible website or a public social media account. There are other contexts where users must remain in a community or closed group to access and publish on such platforms (for example, wikis). This is a way of differentiating that although the content is accessible to the audience, there are certain restrictions for the users who generates the content. Creative effort: Creative effort was put into creating the work or adapting existing works to construct a new one; i.e. users must add their own value to the work. UGC often also has a collaborative element to it, as is the case with websites that users can edit collaboratively. For example, merely copying a portion of a television show and posting it to an online video website (an activity frequently seen on the UGC sites) would not be considered UGC. However, uploading photographs, expressing one's thoughts in a blog post or creating a new music video could be considered UGC. Yet the minimum amount of creative effort is hard to define and depends on the context. Creation outside of professional routines and practices: User-generated content is generally created outside of professional routines and practices. It often does not have an institutional or a commercial market context. In extreme cases, UGC may be produced by non-professionals without the expectation of profit or remuneration. Motivating factors include connecting with peers, achieving a certain level of fame, notoriety, or prestige, and the desire to express oneself. == Media pluralism == According to Cisco, in 2016 an average of 96,000 petabytes was transferred monthly over the Internet, more than twice as many as in 2012. In 2016, the number of active websites surpassed 1 billion, up from approximately 700 million in 2012. Reaching 1.66 billion daily active users in Q4 2019, Facebook has emerged as the most popular social media platform globally. Other social media platforms are also dominant at the regional level such as: Twitter in Japan, Naver in the Republic of Korea, Instagram (owned by Facebook) and LinkedIn (owned by Microsoft) in Africa, VKontakte (VK) and Odnoklassniki (eng. Classmates) in Russia and other countries in Central and Eastern Europe, WeChat and QQ in China. However, a concentration phenomenon is occurring globally giving dominance to a few online platforms that become popular for some unique features they provide, most commonly for the added privacy they offer users through disappearing messages or end-to-end encryption (e.g. Jami, Signal, Snapchat, Telegram, Viber, and WhatsApp), but they have tended to occupy niches and to facilitate the exchanges of information that remain rather invisible to larger audiences. Production of freely accessible information has been increasing since 2012. In January 2017, Wikipedia had more than 43 million articles, almost twice as many as in January 2012. This corresponded to a progressive diversification of content and an increase in contributions in languages other than English. In 2017, less than 12 percent of Wikipedia content was in English, down from 18 percent in 2012. Graham, Straumann, and Hogan say that the increase in the availability and diversity of content has not radically changed the structures and processes for the production of knowledge. For example, while content on Africa has dramatically increased, a significant portion of this content has continued to be produced by contributors operating from North America and Europe, rather than from Africa itself. == History == The massive, multi-volume Oxford English Dictionary was exclusively composed of user-generated content. In 1857, Richard Chenevix Trench of the London Philological Society sought public contributions throughout the English-speaking world for the creation of the first edition of the OED. As Simon Winchester recounts: So what we're going to do, if I have your agreement that we're going to produce such a dictionary, is that we're going to send out invitations, were going to send these invitations to every library, every school, every university, every book shop that we can identify throughout the English-speaking world... everywhere where English is spoken or read with any degree of enthusiasm, people will be invited to contribute words. And the point is, the way they do it, the way they will be asked and instructed to do it, is to read voraciously and whenever they see a word, whether it's a preposition or a sesquipedalian monster, they are to... if it interests them and if where they read it, they see it in a sentence that illustrates the way that that word is used, offers the meaning of the day to that word, then they are to write it on a slip of paper... the top left-hand side you write the word, the chosen word, the catchword, which in this case is 'twilight'. Then the quotation, the quotation illustrates the meaning of the word. And underneath it, the citation, where it came from, whether it was printed or whether it was in manuscri

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  Personal media

  Personal media are media of communication which are used by an individual rather than by a corporation or institution. They are generally contrasted with mass media which are produced by teams of people and broadcast to a general population. In other words, personal media allow individuals, as opposed to corporate entities, to contribute knowledge and opinion to the public. The term dates from the 1980s. New technologies such as social media and self-publishing are creating a variety of modes for modern media. Marika Lüders suggests a two-dimensional model for classifying such media with one dimension being the degree of interaction between the senders and receivers; and the other dimension being the level of institutionalisation and professionalism. Katherine Nashleanas links the concept of personal media to the notion of 'control' by an individual as opposed to a centralised authority. She argues that although personal media including the fax have been available to the general public since the 1960s, more recent technologies such as the smartphone confer greater control over content production and distribution to their users.

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  Deep Learning Anti-Aliasing

  Deep Learning Anti-Aliasing (DLAA) is a form of spatial anti-aliasing developed by Nvidia. DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. DLAA is similar to Deep Learning Super Sampling (DLSS) in its anti-aliasing method, with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, whereas the main priority of DLAA is improving image quality at the cost of performance (irrelevant of resolution upscaling or downscaling). DLAA is similar to temporal anti-aliasing (TAA) in that they are both spatial anti-aliasing solutions relying on past frame data. Compared to TAA, DLAA is substantially better when it comes to shimmering, flickering, and handling small meshes like wires. == Technical overview == DLAA collects game rendering data including raw low-resolution input, motion vectors, depth buffers, and exposure information. This information feeds into a convolutional neural network that processes the image to reduce aliasing while preserving fine detail. The neural network architecture employs an auto-encoder design trained on high-quality reference images. The training dataset includes diverse scenarios focusing on challenging cases like sub-pixel details, high-contrast edges, and transparent surfaces. The network then processes frames in real-time. Unlike traditional anti-aliasing solutions that rely on manually written heuristics, such as TAA, DLAA uses its neural network to preserve fine details while eliminating unwanted visual artifacts. == History == DLAA was initially called and marketed by Nvidia as DLSS 2x. The first game that added support for DLAA was The Elder Scrolls Online, which implemented the feature in 2021. By June 2022, DLAA was only available in six games. This number rose to 17 by February 2023. In June 2023, TechPowerUp reported that "DLAA is seeing sluggish adoption among game developers", and that Nvidia was working on adding DLAA to the quality presets of DLSS to boost adoption. By December 2023, DLAA was supported in 41 games. In early 2025, an update for the Nvidia App added a driver-based DLSS override feature that enables users to activate DLAA even in games that do not support it natively. == Differences between TAA and DLAA == TAA is used in many modern video games and game engines; however, all previous implementations have used some form of manually written heuristics to prevent temporal artifacts such as ghosting and flickering. One example of this is neighborhood clamping which forcefully prevents samples collected in previous frames from deviating too much compared to nearby pixels in newer frames. This helps to identify and fix many temporal artifacts, but deliberately removing fine details in this way is analogous to applying a blur filter, and thus the final image can appear blurry when using this method. DLAA uses an auto-encoder convolutional neural network trained to identify and fix temporal artifacts, instead of manually programmed heuristics as mentioned above. Because of this, DLAA can generally resolve detail better than other TAA and TAAU implementations, while also removing most temporal artifacts. == Differences between DLSS and DLAA == While DLSS handles upscaling with a focus on performance, DLAA handles anti-aliasing with a focus on visual quality. DLAA runs at the given screen resolution with no upscaling or downscaling functionality provided by DLAA. DLSS and DLAA share the same AI-driven anti-aliasing method. As such, DLAA functions like DLSS without the upscaling part. Both are made by Nvidia and require Tensor Cores. However, DLSS and DLAA cannot be enabled at the same time, only one can be selected depending on whether performance or image quality is prioritized. == Reception == TechPowerUp found that "[c]ompared to TAA and DLSS, DLAA is clearly producing the best image quality, especially at lower resolutions", arguing that, while "DLSS was already doing a better job than TAA at reconstructing small objects", "DLAA does an even better job". In a Cyberpunk 2077 performance test, IGN stated that "DLAA provided somewhat similar results [FPS wise] to the normal raster mode in most cases but got significant performance boost with the help of frame generation", a feature not available when using native resolution. Rock Paper Shotgun noted that, while DLAA is "not a completely perfect form of anti-aliasing, as the occasional jaggies are present", it "looks a lot sharper overall [than TAA], and especially in motion." According to PC World, "DLAA offers very good anti-aliasing without losing visual information — alternatives like TAA tend to struggle during motion-filled scenes, where DLAA doesn’t. Furthermore, DLAA’s loss of performance is lower than with conventional anti-aliasing methods."

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  Telecommunications device for the deaf

  A telecommunications device for the deaf (TDD) is a teleprinter, an electronic device for text communication over a telephone line, that is designed for use by persons with hearing or speech difficulties. Other names for the device include teletypewriter (TTY), textphone (common in Europe), and minicom (United Kingdom). The typical TDD is a device about the size of a typewriter or laptop computer with a QWERTY keyboard and small screen that uses an LED, LCD, or VFD screen to display typed text electronically. In addition, TDDs commonly have a small spool of paper on which text is also printed – old versions of the device had only a printer and no screen. The text is transmitted live, via a telephone line, to a compatible device, i.e. one that uses a similar communication protocol. Special telephone services have been developed to carry the TDD functionality even further. In certain countries, there are systems in place so that a deaf person can communicate with a hearing person on an ordinary voice phone using a human relay operator. There are also "carry-over" services, enabling people who can hear but cannot speak ("hearing carry-over", a.k.a. "HCO"), or people who cannot hear but are able to speak ("voice carry-over", a.k.a. "VCO") to use the telephone. The term TDD is sometimes discouraged because people who are deaf are increasingly using mainstream devices and technologies to carry out most of their communication. The devices described here were developed for use on the partially-analog Public Switched Telephone Network (PSTN). They do not work well on the new internet protocol (IP) networks. Thus as society increasingly moves toward IP based telecommunication, the telecommunication devices used by people who are deaf will not be TDDs. In the US and Canada, the devices are referred to as TTYs. Teletype Corporation, of Skokie, Illinois, made page printers for text, notably for news wire services and telegrams, but these used standards different from those for deaf communication, and although in quite widespread use, were technically incompatible. Furthermore, these were sometimes referred to by the "TTY" initialism, short for "Teletype". When computers had keyboard input mechanisms and page printer output, before CRT terminals came into use, Teletypes were the most widely used devices. They were called "console typewriters". (Telex used similar equipment, but was a separate international communication network.) == History == === APCOM acoustic coupler or MODEM device === The TDD concept was developed by James C. Marsters (1924–2009), a dentist and private airplane pilot who became deaf as an infant because of scarlet fever, and Robert Weitbrecht, a deaf physicist. In 1964, Marsters, Weitbrecht and Andrew Saks, an electrical engineer and grandson of the founder of the Saks Fifth Avenue department store chain, founded APCOM (Applied Communications Corp.), located in the San Francisco Bay area, to develop the acoustic coupler, or modem; their first product was named the PhoneType. APCOM collected old teleprinter machines (TTYs) from the Department of Defense and junkyards. Acoustic couplers were cabled to TTYs enabling the AT&T standard Model 500 telephone to couple, or fit, into the rubber cups on the coupler, thus allowing the device to transmit and receive a unique sequence of tones generated by the different corresponding TTY keys. The entire configuration of teleprinter machine, acoustic coupler, and telephone set became known as the TTY. Weitbrecht invented the acoustic coupler modem in 1964. The actual mechanism for TTY communications was accomplished electro-mechanically through frequency-shift keying (FSK) allowing only half-duplex communication, where only one person at a time can transmit. === Paul Taylor TTY device === During the late 1960s, Paul Taylor combined Western Union Teletype machines with modems to create teletypewriters, known as TTYs. He distributed these early, non-portable devices to the homes of many in the deaf community in St. Louis, Missouri. He worked with others to establish a local telephone wake-up service. In the early 1970s, these small successes in St. Louis evolved into the nation's first local telephone relay system for the deaf. === Micon Industries MCM device === In 1973, the Manual Communications Module (MCM), which was the world's first electronic portable TTY allowing two-way telecommunications, premiered at the California Association of the Deaf convention in Sacramento, California. The battery-powered MCM was invented and designed by a deaf news anchor and interpreter, Kit Patrick Corson, in conjunction with Michael Cannon and physicist Art Ogawa. It was manufactured by Michael Cannon's company, Micon Industries, and initially marketed by Kit Corson's company, Silent Communications. In order to be compatible with the existing TTY network, the MCM was designed around the five-bit Baudot code established by the older TTY machines instead of the ASCII code used by computers. The MCM was an instant success with the deaf community despite the drawback of a $599 cost. Within six months there were more MCMs in use by the deaf and hard of hearing than TTY machines. After a year Micon took over the marketing of the MCM and subsequently concluded a deal with Pacific Bell (who coined the term "TDD") to purchase MCMs and rent them to deaf telephone subscribers for $30 per month. After Micon formed an alliance with APCOM, Michael Cannon (Micon), Paul Conover (Micon), and Andrea Saks (APCOM) successfully petitioned the California Public Utilities Commission (CPUC), resulting in a tariff that paid for TTY devices to be distributed free of cost to deaf persons. Micon produced over 1,000 MCMs per month, resulting in approximately 50,000 MCMs being disseminated into the deaf community. Before he left Micon in 1980, Michael Cannon developed several computer compatible variations of the MCM and a portable, battery operated printing TTY, but they were never as popular as the original MCM. Newer model TTYs could communicate with selectable codes that allow communications at a higher bit rate on those models similarly equipped. However, the lack of true computer interface functionality spelled the demise of the original TTY and its clones. During the mid-1970s, other so-called portable telephone devices were being cloned by other companies, and this was the time period when the term "TDD" began being used largely by those outside the deaf community. === Text messaging and the Def-Tone System (DTS) === This relay system became known commonly as the Def-Tone System (DTS) because the tones representing letters of the alphabet were eventually carried in tones outside the range of human hearing. Today, this is commonly called multi-tap because you press a number 1, 2 or 3 times to get a corresponding letter. In 1994 Joseph Alan Poirier, a college student-worker, recommended using the system to send texts to forklifts to improve delivery of parts to the assembly line at GM Powertrain in Toledo, Ohio, and sending a text to pagers. He recommended taking pagers to alphanumeric displays incorporating the same system in discussions with the pager supplier for Outback Steakhouse and having relays put in the forklifts to ping alert messages to the pagers used in that system. He called it text messaging, coining the phrase. It is theorized that when Toyota forklift was allegedly hired by GM for this work, one of the subcontractors, Kyocera, utilized the work for the Toyota forklift company to create text messaging for cell phones. === Marsters Award === In 2009, AT&T received the James C. Marsters Promotion Award from TDI (formerly Telecommunications for the Deaf, Inc.) for its efforts to increase accessibility to communication for people with disabilities. The award holds some irony; it was AT&T that, in the 1960s, resisted efforts to implement TTY technology, claiming it would damage its communication equipment. In 1968, the Federal Communications Commission struck down AT&T's policy and forced it to offer TTY access to its network. == Protocols == There are many different standards for TDDs and textphones. === Original 5-bit Baudot code === The original standard used by TTYs is a variant of the Baudot code. The maximum speed of this protocol is 10 characters per second. This is a half-duplex protocol, which means that only one person at a time may transmit characters. If both try to transmit at the same time, the characters will be garbled on the other end. This protocol is commonly used in the United States. This is a variant of the Baudot code, implemented as 5-bits per character transmitted asynchronously using frequency-shift key-modulation at either 45.5 or 50 baud, 1 start bit, 5 data bits, and 1.5 stop bits. Details of the protocol implementation are available in TIA-825-A and also in T-REC V.18 Annex A "5-bit operational mode". === Turbo Code === The UltraTec company implements another protocol known as Enh

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  Digital edition

  A digital edition is an online magazine or online newspaper delivered in electronic form which is formatted identically to the print version. Digital editions are often called digital facsimiles to underline the likeness to the print version. Digital editions have the benefit of reduced cost to the publisher and reader by avoiding the time and the expense to print and deliver paper edition. This format is considered more environmentally friendly due to the reduction of paper and energy use. These editions also often feature interactive elements such as hyperlinks both within the publication itself and to other internet resources, search option and bookmarking, and can also incorporate multimedia such as video or animation to enhance articles themselves or for advertisement purposes. Some delivery methods also include animation and sound effects that replicate turning of the page to further enhance the experience of their print counterparts. Magazine publishers have traditionally relied on two revenue sources: selling ads and selling magazines. Additionally some publishers are using other electronic publication methods such as RSS to reach out to readers and inform them when new digital editions are available. Current technologies are generally either reader-based, requiring a download of an application and subsequent download of each edition, or browser-based, often using Macromedia Flash, requiring no application download (such as Adobe Acrobat). Some application-based readers allow users to access editions while not connected to internet. Dedicated hardware such as the Amazon Kindle and the iPad is also available for reading digital editions of select books, popular national magazines such as Time, The Atlantic, and Forbes and popular national newspapers such as the New York Times, Wall Street Journal, and Washington Post. Archives of print newspapers, in some cases dating hundreds of years back, are being digitized and made available online. Google is indexing existing digital archives produced by the newspapers themselves or by third parties. Newspaper and magazine archival began with microform film formats solving the problem of efficiently storing and preserving. This format, however, lacked accessibility. Many libraries, especially state libraries in the United States are archiving their collections digitally and converting existing microfilm to digital format. The Library of Congress provides project planning assistance and the National Endowment for the Humanities procures funding through grants from its National Digital Newspaper Program. Digital magazines, ezines, e-editions and emags are sometimes referred to as digital editions, however some of these formats are published only in digital format unlike digital editions which replicate a printed edition as well. == Digital magazines == Digital-replica magazines number in thousands—consumer and business publications, house magazines for associations, institutions and corporations – and conversion from print to digital was still increasing as of 2009. A 2008 report funded by digital-replica technology providers and auditing agencies counted 1,786 digital-replica editions having more than 7 million circulation among business-to-business publications, of which 230 editions were audited The same report counted 1,470 digital-replica editions of consumer magazines having 5.5 million digital circulation, of which 240 editions were audited. These authors estimated that by year end of 2009 there would be 8,000 digital magazines, having a combined distribution of more than 30 million people. Surveys have shown that, while not all subscribers prefer a digital edition, some do because of the environmental benefit and also because digital magazines are searchable and may easily be passed along or linked to. One such survey funded by a digital publisher reported on inputs from more than 30,000 subscribers to business, consumer and other digital magazines. == Digital magazine business models == === Reduced printing and distribution costs === The publishers' choice to save by moving some or all subscribers from print to digital is widely accepted. Oracle magazine, which has 176,000 of its 516,000 subscribers receiving digital according to its June 2009 BPA circulation statement, is said to be the most widely circulated digital edition of a business-to-business publication. Publishers who do this need to choose whether to make some issues all-digital, move some subscribers to digital edition, add some digital-only subscribers, or send all subscribers the digital edition. === Paid subscription revenue === In 2009, a major consumer magazine, PC Magazine, went all-digital, charging an annual subscription fee for its digital-replica edition. Many consumer magazines and newspapers are already available in eReader formats that are sold through booksellers. === Sponsorship and advertising revenue === Digital editions often carry special "front cover" advertising, or advertising on the email message alerting the subscriber of the digital edition. Publishers also produce special digital-only inserts and rich-media ads or advertorials. === Designed-for-digital issues === Another approach is to fully replace printed issues with digital ones, or to use digital editions for extra issues that would otherwise have to be printed.

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  DBOS

  DBOS (Formerly Database-Oriented Operating System, now just DBOS) is an open source durable workflow execution software library written for the Python, TypeScript, Java, and Go programming languages. DBOS arose from a joint open source project from MIT and Stanford, after a discussion between Michael Stonebraker and Matei Zaharia on how to scale and improve scheduling and performance of millions of Apache Spark tasks. Today it is a commercial company that offers an open source system to add durable computing to any software, built on concepts derived from the joint research project. == History == === 2020: Academic R&D Project === DBOS originated in 2020 as a joint open source project between MIT, Stanford, and Carnegie Mellon. The project explored the idea of operating system services built atop a distributed database - a database-oriented operating system meant to simplify and improve the scalability, security and resilience of large-scale distributed applications. The basic concept was to run a multi-node multi-core, transactional, highly-available distributed database, such as VoltDB, as the only application for a microkernel, and then to implement scheduling, messaging, file systems and other operating system services on top of the database. The architectural philosophy is described by this quote from the abstract of their initial preprint: All operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. A prototype was built with competitive performance to existing systems. ==

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  Automatic scorer

  An automatic scorer is the computerized scoring system to keep track of scoring in ten-pin bowling. It was introduced en masse in bowling alleys in the 1970s and combined with mechanical pinsetters to detect overturned pins. By eliminating the need for manual score-keeping, these systems have introduced new bowlers into the game who otherwise would not participate because they had to count the score themselves, as many do not understand the mathematical formula involved in bowler scoring. At first, people were skeptical about whether a computer could keep an accurate score. In the twenty-first century, automatic scorers are used in most bowling centers around the world. The three manufacturers of these specialty computers have been Brunswick Bowling, AMF Bowling (later QubicaAMF), and RCA. == History == Automatic equipment is considered a cornerstone of the modern bowling center. The traditional bowling center of the early 20th century was advanced in automation when the pinsetter person ("pin boy"), who set back up by hand the bowled down pins, was replaced by a machine that automatically replaced the pins in their proper play positions. This machine came out in the 1950s. A detection system was developed from the pinsetter mechanism in the 1960s that could tell which pins had been knocked down, and that information could be transferred to a digital computer. Automatic electronic scoring was first conceived by Robert Reynolds, who was described by a newspaper story at the time as "a West Coast electronics calculator expert." He worked with the technical staff of Brunswick Bowling to develop it. The goal was realized in the late 1960s when a specialized computer was designed for the purpose of automatic scorekeeping for bowling. The field test for the automatic scorer took place at Village Lanes bowling center, Chicago in 1967. The scoring machine received approval for official use by the American Bowling Congress in August of that year. They were first used in national official league gaming on October 10, 1967. In November, Brunswick announced that they were accepting orders for the new digital computer, which cost around $3,000 per bowling lane. Bowling centers that installed these new automatic scoring devices in the 1970s charged a ten cents extra per line of scoring for the convenience. == Description == Each Automatic Scorer computer unit kept score for four lanes. It had two bowler identification panels serving two lanes each. The bowler pushed it into his named position when his turn came up so the computer knew who was bowling and score accordingly. After the bowler rolled the bowling ball down the lane and knocked down pins, the pinsetter detected which pins were down and relayed this information back to the computer for scoring. The result was then printed on a scoresheet and projected overhead onto a large screen for all to see. The Automatic Scorer digital computer was mathematically accurate, however the detection system at the pinsetter mechanism sometimes reported the wrong number of pins knocked down. The computer could be corrected manually for any errors in the system; similarly, human errors, such as neglecting to move the bowler identification mechanism, could be corrected for by manual action. The scorer could take into account bowlers' handicaps and could adjust for late-arriving bowlers. The automatic scorer is directly connected to the foul detection unit. As a result, foul line violations are automatically scored. Brunswick had put ten years of research and development into the Automatic Scorer, and by 1972 there were over 500 of these computers installed in bowling centers around the world. AMF Bowling, competitor to Brunswick, entered into the automatic scorer computer field during the 1970s and their systems were installed into their brand of bowling centers. By 1974, RCA was also making these computers for automatic scoring. == Reception and further developments == The purposes of the computerized scoring were to avoid errors by human scorers and to prevent cheating. It had the side benefit of speeding up the progress of the game and introducing new bowlers to the game. Score-keeping for bowling is based on a formula that many new to bowling were not familiar with and thought difficult to learn. These casual bowlers unfamiliar with the formula thought the scores given by the computers were confusing. Some bowlers were not comfortable with automatic scorers when they were introduced in the 1970s, so kept score using the traditional method on paper score sheets. The introduction of this device increased the popularity of the sport. Automatic scorers came to be considered a normal part of modern bowling installations worldwide, with owners and managers saying that bowlers expect such equipment to be present in bowling establishments and that business increased following their introduction. Brunswick introduced a color television style automatic scorer in 1983. Bowling center owners could use these style automatic scorers for advertising, management, videos, and live television. By the 2010s, these types of electronic visual displays could show bowler avatars and social media connections to publicize the bowlers' scores. Some are capable of being extended entertainment systems of games for children and adults. Some scoring systems support variations on traditional bowling, such as different kinds of bingo games where certain pins have to be knocked down at certain times or practice regimes where certain spares have to be accomplished. By this point, QubicaAMF Worldwide, an outgrowth of AMF, was one of the leading providers of bowling scoring equipment.

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  Web series

  A web series, also known as a short-form series or web show, is a collection of short scripted or unscripted online videos released on the Internet (i.e., World Wide Web), generally in episodic form. A single installment of a web series can be called a webisode or an episode. The scale of a web series is small, and a typical episode can be anywhere from 3 to 15 minutes long (though some may run up to 20 minutes). Web series first emerged in the mid-1990s and became more prominent in the early 2000s. Web series are distributed online on video-sharing websites and apps, such as YouTube, Vimeo, and TikTok, and can be watched on devices such as smartphones, tablets, desktops, laptops, and Smart TVs (or television sets connected to the Internet with a media streaming device). They can also be released on social media platforms. Because of the nature of the Internet, a web series may be interactive and immersive. Web series are classified as new media. Web series are different from streaming television series, as the latter are designed to be watched on streaming platforms such as Netflix, Amazon Prime Video, or Hotstar, with the streaming services offering original productions made for and by them, as well as acquiring the rights to distribute licensed content. The length of a streaming television series episode is 30 to 60 minutes (runtimes can also be longer). Although the design of a web series can be similar to that of a television series, its development and production do not entail the same financial investment required for a television series. The popularity of some web series, however, has led to them being optioned for television. Web series differ from short-form content in that the latter are vertical videos specifically designed for smartphone viewing and intended for fast-paced consumption, with runtimes typically ranging from less than one minute to three minutes. There are film festivals for web series, like Webfest Berlin, NYC Web Fest, LA Web Fest, and Vancouver Web Fest. Awards organizations have also been established to celebrate excellence in web series, such as the Streamys, Webbys, IAWTV Awards, and Indie Series Awards. Most major award ceremonies have also created web series and digital media award categories, including the Emmy Awards and the Canadian Screen Awards. == History == === 1990s === In April 1995, "Global Village Idiots", an episode of the reality-based program Rox on public access cable television in Bloomington, Indiana, was uploaded to the Internet, making Rox the first show distributed via the web. The same year, Scott Zakarin created The Spot, an episodic online story that integrated photos, videos, and blogs into the storyline. Likened to Melrose Place-on-the-Web, The Spot featured a rotating cast of characters playing trendy twenty-somethings who rented rooms in a fabled Santa Monica, California beach house called "The Spot". The Spot earned Infoseek's "Cool Site of the Year," an award which later became the Webby. In January 1999, Showtime licensed the animated sci-fi web series WhirlGirl, making it the first independently produced web series licensed by a national television network. In February 1999, the show premiered simultaneously on Showtime and online. The character occasionally appeared on Showtime, for example, hosting a "Lethal Ladies" programming block, but spent most of her time online, appearing in 100 webisodes. === 2000s === As broadband bandwidth increased in speed and availability, delivering high-quality video over the Internet became a reality. In the early 2000s, the Japanese anime industry began broadcasting original net animation (ONA), a type of original video animation (OVA) series, on the Internet. Early examples of the ONA series include Infinite Ryvius: Illusion (2000), Ajimu (2001), and Mahou Yuugi (2001). In 2000, The Brothers Chaps launched the Adobe Flash-created web series Homestar Runner. After being put on hiatus in 2010, it returned in 2014. In 2002, Matt Jolly (better known as "Krinkels") released the first episode of Madness Combat to Newgrounds. The show is still ongoing, with the latest episode "Madness Combat 12: Contravention" released on Twitch in September 2024. In 2003, Microsoft launched MSN Video, offering NBC-related content. Its web series, Weird TV 2000, a spin-off of the syndicated television series Weird TV, featured dozens of shorts, comedy sketches, and mini-documentaries produced exclusively for MSN Video. The video-sharing site YouTube was launched in early 2005, allowing users to share television programs. YouTube co-founder Jawed Karim said the inspiration for YouTube first came from Janet Jackson's role in the 2004 Super Bowl incident, when her breast was exposed during her performance, and later from the 2004 Indian Ocean tsunami. Karim could not easily find video clips of either event online, which led to the idea of a video-sharing site. From 2003 to 2006, many independent web series gained significant popularity, most notably the science fiction series Red vs. Blue by Rooster Teeth. The series was distributed independently via online portals YouTube and Revver, as well as the Rooster Teeth website, acquiring over 100 million social media views during its run. (Rooster Teeth would eventually create the computer-animated web series RWBY in 2013.) In 2004, the adult-animated series Salad Fingers was created, which amassed a cult following. The comedy show The Burg, hailed as the internet's first sitcom and starring Kelli Giddish and Lindsey Broad, rapidly gained an audience and press attention before its creators signed a creation deal with Michael Eisner. The drama Sam Has 7 Friends, which ran in the summer and fall of 2006, was nominated for a Daytime Emmy Award and was temporarily removed from the Internet when it was also acquired by Eisner. In 2004–2005, Spanish producer Pedro Alonso Pablos recorded a series of video interviews featuring actors and directors such as Guillermo del Toro, Santiago Segura, Álex de la Iglesia, and Keanu Reeves, which were distributed through his own website. lonelygirl15, California Heaven, "The Burg", and SamHas7Friends also gained popularity during this time, acquiring audiences in the millions. (Science fiction thriller lonelygirl15 was so successful that it secured a sponsorship deal with Neutrogena in 2007.) In 2004, Stewart St. John, executive producer and head writer of 1990s webisodies The Spot, revived the brand for online audiences as The Spot (2.0), with a new cast, and as a separate soap opera on Sprint PCS Vision-enabled cell phones, creating the first American mobile phone series. St. John and partner Todd Fisher produced over 2,500 daily videos of the mobile soap, driving story lines across platforms to its web counterpart. In 2007, the creators of lonelygirl15 followed up on the show's success with KateModern, a comedy-drama series that debuted on social network Bebo, and took place in the same fictional universe as their previous show. Big Fantastic created and produced the soap opera Prom Queen, financed and distributed by Michael Eisner's production firm Vuguru, and debuted the series on MySpace. Vuguru partnered with Mark Cuban's channel HDNet to release All-for-nots, a mockumentary series by The Burg creators Kathleen Grace and Thom Woodley, which debuted at the SXSW Festival in 2008. These web series highlighted interactivity with the audience in addition to the narrative on relatively low budgets. In contrast, the eight-episode show Sanctuary, starring actor/producer Amanda Tapping, cost $4.3 million to produce. Both Sanctuary and Prom Queen were nominated for a Daytime Emmy Award. Award-winning producer/director Marshall Herskovitz created the drama Quarterlife, which debuted on MySpace and was later distributed on NBC. In 2008, major television studios began releasing web series, such as the ABC comedy show Squeegies, the NBC sci-fi show Gemini Division, and the Bravo reality series The Malan Show. Warner Bros. relaunched The WB as an online network beginning with original mystery web series, Sorority Forever, created and produced by Big Fantastic and executive produced by McG. Meanwhile, MTV announced a new original web series created by Craig Brewer, $5 Cover, that brought together the indie music world and new media expansion. Joss Whedon created, produced, and self-financed musical comedy-drama Dr. Horrible's Sing-Along Blog starring Neil Patrick Harris and Felicia Day. Big Fantastic wrote and produced Foreign Body, a mystery web series that served as a prequel to Robin Cook's novel of the same name. Beckett and Goodfried founded a new Internet studio, EQAL, and produced a spin-off of lonelygirl15 titled LG15: The Resistance. The mainstream press began to provide coverage. In the United Kingdom, KateModern ended its run on Bebo. Bebo also hosted a six-month-long reality travel show, The Gap Year, produced by Endemol UK, and produced an interactive sci-fi drama Kirill for

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  Modulation error ratio

  The modulation error ratio (MER) is a measure used to quantify the performance of a digital radio (or digital TV) transmitter or receiver in a communications system using digital modulation (such as QAM). A signal sent by an ideal transmitter or received by a receiver would have all constellation points precisely at the ideal locations, however various imperfections in the implementation (such as noise, low image rejection ratio, phase noise, carrier suppression, distortion, etc.) or signal path cause the actual constellation points to deviate from the ideal locations. Transmitter MER can be measured by specialized equipment, which demodulates the received signal in a similar way to how a real radio demodulator does it. Demodulated and detected signal can be used as a reasonably reliable estimate for the ideal transmitted signal in MER calculation. == Definition == An error vector is a vector in the I-Q plane between the ideal constellation point and the point received by the receiver. The Euclidean distance between the two points is its magnitude. The modulation error ratio is equal to the ratio of the root mean square (RMS) power (in Watts) of the reference vector to the power (in Watts) of the error. It is defined in dB as: M E R ( d B ) = 10 log 10 ⁡ ( P s i g n a l P e r r o r ) {\displaystyle \mathrm {MER(dB)} =10\log _{10}\left({P_{\mathrm {signal} } \over P_{\mathrm {error} }}\right)} where Perror is the RMS power of the error vector, and Psignal is the RMS power of ideal transmitted signal. MER is defined as a percentage in a compatible (but reciprocal) way: M E R ( % ) = P e r r o r P s i g n a l × 100 % {\displaystyle \mathrm {MER(\%)} ={\sqrt {P_{\mathrm {error} } \over P_{\mathrm {signal} }}}\times 100\%} with the same definitions. MER is closely related to error vector magnitude (EVM), but MER is calculated from the average power of the signal. MER is also closely related to signal-to-noise ratio. MER includes all imperfections including deterministic amplitude imbalance, quadrature error and distortion, while noise is random by nature.

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  Contact center telephony

  In marketing, contact center telephony is the communication and collaboration system used by businesses to either manage high volumes of inbound queries or outbound telephone calls keeping their workforce or agents productive and in control to serve or acquire customers. This business communication system is an extension of computer telephony integration (CTI). == Overview == The interactions between callers and customer service representatives are supported by the collective system of computers, telephones and the Internet. The shift from CTI to contact center telephony is marked by the sheer change in the customer’s behavior when it comes to communication. Means customers are no longer confined only to voice-based communication i.e. phone to connect with their customer service departments. In addition, they are making use of email, SMS, chat, social media, and other virtual contact channels. This is also the reason for the shift in nomenclature from "call centers" to "contact centers", "contact" being a wider term than "call". Respecting the trend, contact center owners need to adopt unified communication or multi-channel approach to let customers get in touch with them via their preferred communication mediums, either voice or non-voice (data). Cloud-based phone system is a further advancement in the direction as it allows operators to access all the features and benefits of call center telephony over the Web against an affordable & flexible pay-as-you-go subscription model. Thus, in-house infrastructure deployment to manage public switched telephone networks, storage, communication applications, and collaboration servers is no more an obligation. Neither is the need to invest resources for their upgrade, repair, maintenance and security as cloud vendor would be responsible for the same. == India == India, a popular call center business process outsourcing destination, often uses a cloud-based phone system in order to cut operational expenses and downtime, and increase connectivity. == Promotion == Businesses can rely on contact center telephony services to respond to their customers’ queries over phone, email, chat, fax, etc. Integrating it with their customer relationship management tools, entire contact details of customers and their interaction sessions with different customer service representatives can be found at one place. The combination can manage not just sales and marketing but also deliver excellent post-sales customer service or technical support to allow customers derive the most from their products or services. Hence, it’s becoming instrumental in increasing customer satisfaction and loyalty and most of the call center services in India are taking refuge from it. The entire contact center telephony service can be availed by professionals over a browser. Hence, businesses can leverage the concept of BYOD (bring your own device) and mobility and serve their customers well using mobile applications. According to market analysts, BYOD increases satisfaction among workforce, and hence their individual and collective productivity as well. BYOD programme significantly reduces the TCO (total cost of ownership) as professionals prefer to work with their own devices rather than using company-provisioned devices. Next, they tend to be more caring towards such devices and can even shell out money to update and upgrade those when required. Integration of IM, along with audio and video conferencing services helps call center or contact center representatives to get real time assistance from their peers or seniors to resolve any complex issues. They can internally exchange information and knowledge articles as and when required. Real-time call monitoring/barging system can be used by quality assessment team to provide important guidelines to agents to maintain the standard of the service as per industry norms. Integrated recording feature is helpful for internal training and quality purposes to improve productivity and customer satisfaction in equal measures. It also helps in getting business insights and improving products or services to gain deeper penetration into the market.

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  Autonomic networking

  Autonomic networking follows the concept of Autonomic Computing, an initiative started by IBM in 2001. Its ultimate aim is to create self-managing networks to overcome the rapidly growing complexity of the Internet and other networks and to enable their further growth, far beyond the size of today. == Increasing size and complexity == The ever-growing management complexity of the Internet caused by its rapid growth is seen by some experts as a major problem that limits its usability in the future. What's more, increasingly popular smartphones, PDAs, networked audio and video equipment, and game consoles need to be interconnected. Pervasive Computing not only adds features, but also burdens existing networking infrastructure with more and more tasks that sooner or later will not be manageable by human intervention alone. Another important aspect is the price of manually controlling huge numbers of vitally important devices of current network infrastructures. == Autonomic nervous system == The autonomic nervous system (ANS) is the part of complex biological nervous systems that is not consciously controlled. It regulates bodily functions and the activity of specific organs. As proposed by IBM, future communication systems might be designed in a similar way to the ANS. == Components of autonomic networking == As autonomics conceptually derives from biological entities such as the human autonomic nervous system, each of the areas can be metaphorically related to functional and structural aspects of a living being. In the human body, the autonomic system facilitates and regulates a variety of functions including respiration, blood pressure and circulation, and emotive response. The autonomic nervous system is the interconnecting fabric that supports feedback loops between internal states and various sources by which internal and external conditions are monitored. === Autognostics === Autognostics includes a range of self-discovery, awareness, and analysis capabilities that provide the autonomic system with a view on high-level state. In metaphor, this represents the perceptual sub-systems that gather, analyze, and report on internal and external states and conditions – for example, this might be viewed as the eyes, visual cortex and perceptual organs of the system. Autognostics, or literally "self-knowledge", provides the autonomic system with a basis for response and validation. A rich autognostic capability may include many different "perceptual senses". For example, the human body gathers information via the usual five senses, the so-called sixth sense of proprioception (sense of body position and orientation), and through emotive states that represent the gross wellness of the body. As conditions and states change, they are detected by the sensory monitors and provide the basis for adaptation of related systems. Implicit in such a system are imbedded models of both internal and external environments such that relative value can be assigned to any perceived state - perceived physical threat (e.g. a snake) can result in rapid shallow breathing related to fight-flight response, a phylogenetically effective model of interaction with recognizable threats. In the case of autonomic networking, the state of the network may be defined by inputs from: individual network elements such as switches and network interfaces including specification and configuration historical records and current state traffic flows end-hosts application performance data logical diagrams and design specifications Most of these sources represent relatively raw and unprocessed views that have limited relevance. Post-processing and various forms of analysis must be applied to generate meaningful measurements and assessments against which current state can be derived. The autognostic system interoperates with: configuration management - to control network elements and interfaces policy management - to define performance objectives and constraints autodefense - to identify attacks and accommodate the impact of defensive responses === Configuration management === Configuration management is responsible for the interaction with network elements and interfaces. It includes an accounting capability with historical perspective that provides for the tracking of configurations over time, with respect to various circumstances. In the biological metaphor, these are the hands and, to some degree, the memory of the autonomic system. On a network, remediation and provisioning are applied via configuration setting of specific devices. Implementation affecting access and selective performance with respect to role and relationship are also applied. Almost all the "actions" that are currently taken by human engineers fall under this area. With only a few exceptions, interfaces are set by hand, or by extension of the hand, through automated scripts. Implicit in the configuration process is the maintenance of a dynamic population of devices under management, a historical record of changes and the directives which invoked change. Typical to many accounting functions, configuration management should be capable of operating on devices and then rolling back changes to recover previous configurations. Where change may lead to unrecoverable states, the sub-system should be able to qualify the consequences of changes prior to issuing them. As directives for change must originate from other sub-systems, the shared language for such directives must be abstracted from the details of the devices involved. The configuration management sub-system must be able to translate unambiguously between directives and hard actions or to be able to signal the need for further detail on a directive. An inferential capacity may be appropriate to support sufficient flexibility (i.e. configuration never takes place because there is no unique one-to-one mapping between directive and configuration settings). Where standards are not sufficient, a learning capacity may also be required to acquire new knowledge of devices and their configuration. Configuration management interoperates with all of the other sub-systems including: autognostics - receives direction for and validation of changes policy management - implements policy models through mapping to underlying resources security - applies access and authorization constraints for particular policy targets autodefense - receives direction for changes === Policy management === Policy management includes policy specification, deployment, reasoning over policies, updating and maintaining policies, and enforcement. Policy-based management is required for: constraining different kinds of behavior including security, privacy, resource access, and collaboration configuration management describing business processes and defining performance defining role and relationship, and establishing trust and reputation It provides the models of environment and behavior that represent effective interaction according to specific goals. In the human nervous system metaphor, these models are implicit in the evolutionary "design" of biological entities and specific to the goals of survival and procreation. Definition of what constitutes a policy is necessary to consider what is involved in managing it. A relatively flexible and abstract framework of values, relationships, roles, interactions, resources, and other components of the network environment is required. This sub-system extends far beyond the physical network to the applications in use and the processes and end-users that employ the network to achieve specific goals. It must express the relative values of various resources, outcomes, and processes and include a basis for assessing states and conditions. Unless embodied in some system outside the autonomic network or implicit to the specific policy implementation, the framework must also accommodate the definition of process, objectives and goals. Business process definitions and descriptions are then an integral part of the policy implementation. Further, as policy management represents the ultimate basis for the operation of the autonomic system, it must be able to report on its operation with respect to the details of its implementation. The policy management sub-system interoperates (at least) indirectly with all other sub-systems but primarily interacts with: autognostics - providing the definition of performance and accepting reports on conditions configuration management - providing constraints on device configuration security - providing definitions of roles, access and permissions === Autodefense === Autodefense represents a dynamic and adaptive mechanism that responds to malicious and intentional attacks on the network infrastructure, or use of the network infrastructure to attack IT resources. As defensive measures tend to impede the operation of IT, it is optimally capable of balancing performance objectives with typically over-riding threat management actions. In the

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  Blue check

  A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official

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  Web science

  Web science is an emerging interdisciplinary field concerned with the study of large-scale socio-technical systems, particularly the World Wide Web. It considers the relationship between people and technology, the ways that society and technology co-constitute one another and the impact of this co-constitution on broader society. Web Science combines research from disciplines as diverse as sociology, computer science, economics, and mathematics. The Web Science Institute, founded at the University of Southampton by director Wendy Hall and colleagues, describes Web Science as focusing "the analytical power of researchers from disciplines as diverse as mathematics, sociology, economics, psychology, law and computer science to understand and explain the Web. It is necessarily interdisciplinary – as much about social and organizational behaviour as about the underpinning technology." A central pillar of Web science development is Artificial Intelligence or "AI". The current artificial intelligence that in development at the moment is Human-Centered, with goals to further professional development courses as well as influencing public policy. Artificial intelligence developers are focused on the most impactful uses of this technology, while also hoping to expedite the growth and development of the human race. An early definition was given by American computer scientist Ben Shneiderman: "Web Science" is processing the information available on the web in similar terms to those applied to natural environment. == Areas of activity == === Emergent properties === Philip Tetlow, an IBM-based scientist influential in the emergence of web science as an independent discipline, argued for the concept of web life, which considers the Web not as a connected network of computers, as in common interpretations of the Internet, but rather as a sociotechnical machine capable of fusing together individuals and organisations into larger coordinated groups. It argues that unlike the technologies that have come before it, the Web is different in that its phenomenal growth and complexity are starting to outstrip our capability to control it directly, making it impossible for us to grasp its completeness in one go. Tetlow made use of Fritjof Capra's concept of the 'web of life' as a metaphor. == Research groups == There are numerous academic research groups engaged in Web Science research, many of which are members of WSTNet, the Web Science Trust Network of research labs. Health Web Science emerged as a sub-discipline of Web Science that studies the role of the Web's impact on human's health outcomes and how to further utilize the Web to improve health outcomes. These groups focus on the developmental possibilities, provided through Web Science, in areas such as health care and social welfare. Discussion of web science has been widely adopted as a method in which the internet can have a real world impact in the field of medicine, currently coined Medicine 2.0. The World Wide Web acts as a medium for the spread and circulation of knowledge, though these various research groups consider themselves responsible for maintaining verifiable and testable knowledge. Using their knowledge of the healthcare system as well as web science, researchers are focused on formatting and structuring their knowledge in a way that is easily accessible throughout the internet. The World Wide Web is quickly evolving meaning that the information we provide and its formatting must also. Recognizing the overlap between both aspects, the spread of knowledge and development of the internet, allows us to properly display our knowledge in a manner that evolves as quickly as the internet and everyday medical research. The accessibility of the internet and quick development of knowledge must be companied with efficient formatting to allocate successful dissemination of information, as described by these various researcher groups. == Related major conferences == Association for Computing Machinery (ACM), Hypertext Conference (HT) sponsored by SIGWEB ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) International AAAI Conference on Weblogs and Social Media (ICWSM) The Web Conference (WWW) Association for Computing Machinery (ACM) Web Science Conference (WebSci)

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