AI Code Meme

AI Code Meme — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Lenny (chatbot)

  Lenny is a chatbot designed to scam bait telemarketers, scammers, and other unwanted incoming calls using messages. == Background == Telemarketers may be perceived by some as annoying and wasting people's time, and some deliberately attempt to scam or defraud people. In April 2018, stats published by YouMail estimated the United States received over three billion robocalls that month. Attempts to block the callers have been hindered by Caller ID spoofing. == Features == The bot was written in 2011, and development taken over by an Alberta-based programmer known as "Mango" two years later. It is driven by sixteen pre-recorded audio clips, spoken in a soft and slow Australian accent in the manner of an elderly man. The bot's original creator stated on Reddit that in building the character he asked himself the question "What would be a telemarketer's worst nightmare?" He answered with this being a lonely old man who is up for a chat, proud of his family and can't focus on the telemarketer's goal. There is no speech recognition or artificial intelligence, and the bot's software is simple and straightforward. The first four clips are played sequentially in order to grab the telemarketer's interest and begin their sales pitch to Lenny, then the remaining twelve are played sequentially on loop until the telemarketer hangs up. The program waits for a gap of 1.5 seconds of silence before playing the next audio clip, to simulate natural breaks in the conversation. The messages are purposefully vague and open-ended so they can be applied to as many conversations as possible. They include references to Lenny's children, the state of the economy, and being interrupted by some ducks outside. According to research into the bot, around 75% of callers realise they are talking to a computer program within two minutes; however, some calls have lasted around an hour. == Distribution == Though other chatbots had been developed earlier, Lenny was the first one to be released for free on a public server and could be accessed by anyone. Recordings of conversations with the bot are widely shared online on websites such as Reddit and YouTube. Though "Mango" only intended Lenny to be used against dishonest telemarketers, such as scammers, he does not mind it being used against callers who are merely annoying. The bot has also been used against political campaigners, such as a supporter of Pierre Poilievre in the 2015 Canadian federal election.

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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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  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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  Open Rights Group

  The Open Rights Group (ORG) is a UK-based organisation that works to preserve digital rights and freedoms by campaigning on digital rights issues and by fostering a community of grassroots activists. It campaigns on numerous issues including mass surveillance, internet filtering and censorship, and intellectual property rights. == History == The organisation was started by Danny O'Brien, Cory Doctorow, Ian Brown, Rufus Pollock, James Cronin, Stefan Magdalinski, Louise Ferguson and Suw Charman after a panel discussion at Open Tech 2005. O'Brien created a pledge on PledgeBank, placed on 23 July 2005, with a deadline of 25 December 2005: "I will create a standing order of 5 pounds per month to support an organisation that will campaign for digital rights in the UK but only if 1,000 other people will too." The pledge reached 1000 people on 29 November 2005. The Open Rights Group was launched at a "sell-out" meeting in Soho, London. == Work == The group has made submissions to the All Party Internet Group (APIG) inquiry into digital rights management and the Gowers Review of Intellectual Property. The group was honoured in the 2008 Privacy International Big Brother Awards alongside No2ID, Liberty, Genewatch UK and others, as a recognition of their efforts to keep state and corporate mass surveillance at bay. In 2010 the group worked with 38 Degrees to oppose the introduction of the Digital Economy Act, which was passed in April 2010. The group opposes measures in the draft Online Safety Bill introduced in 2021, that it sees as infringing free speech rights and online anonymity. The group campaigns against the Department for Digital, Culture, Media and Sport's plan to switch to an opt-out model for cookies. The group spokesperson stated that "[t]he UK government propose to make online spying the default option" in response to the proposed switch. == Areas of interest == The organisation, though focused on the impact of digital technology on the liberty of UK citizens, operates with an apparently wide range of interests within that category. Its interests include: === Access to knowledge === Copyright Creative Commons Free and open source software The public domain Crown copyright Digital Restrictions Management Software patents === Free speech and censorship === Internet filtering Right to parody s. 127 Communications Act 2003 === Government and democracy === Electronic voting Freedom of information legislation === Privacy, surveillance and censorship === Automatic Vehicle Tracking Communications data retention Identity management Net Neutrality NHS patients' medical database Police DNA Records RFID == Structure == ORG has a paid staff, whose members include: Jim Killock (executive director) Former staff include Suw Charman-Anderson and Becky Hogge, both executive directors, e-voting coordinator Jason Kitcat, campaigner Peter Bradwell, grassroots campaigner Katie Sutton and administrator Katerina Maniadaki. Neil Gaiman was previously the group's patron. As of October 2022, the group had over 43,000 supporters. == ORGCON == ORGCON was the first ever conference dedicated to digital rights in the UK, marketed as "a crash course in digital rights". It was held for the first time in 2010 at City University in London and included keynote talks from Cory Doctorow, politicians and similar pressure groups including Liberty, NO2ID and Big Brother Watch. ORGCON has since been held in 2012, 2013, 2014, 2017, and 2019 where the keynote was given by Edward Snowden.

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  Confusion matrix

  In machine learning, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. In unsupervised learning it is usually called a matching matrix. The term is used specifically in the problem of statistical classification. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa – both variants are found in the literature. The diagonal of the matrix therefore represents all instances that are correctly predicted. The name stems from the fact that it makes it easy to identify whether the system is confusing two classes (i.e., commonly mislabeling one class as another). The confusion matrix has its origins in human perceptual studies of auditory stimuli. It was adapted for machine learning studies and used by Frank Rosenblatt, among other early researchers, to compare human and machine classifications of visual (and later auditory) stimuli. It is a special kind of contingency table, with two dimensions ("actual" and "predicted"), and identical sets of "classes" in both dimensions (each combination of dimension and class is a variable in the contingency table). == Example == Given a sample of 12 individuals, 8 that have been diagnosed with cancer and 4 that are cancer-free, where individuals with cancer belong to class 1 (positive) and non-cancer individuals belong to class 0 (negative), we can display that data as follows: Assume that we have a classifier that distinguishes between individuals with and without cancer in some way, we can take the 12 individuals and run them through the classifier. The classifier then makes 9 accurate predictions and misses 3: 2 individuals with cancer wrongly predicted as being cancer-free (sample 1 and 2), and 1 person without cancer that is wrongly predicted to have cancer (sample 9). Notice, that if we compare the actual classification set to the predicted classification set, there are 4 different outcomes that could result in any particular column: The actual classification is positive and the predicted classification is positive (1,1). This is called a true positive result because the positive sample was correctly identified by the classifier. The actual classification is positive and the predicted classification is negative (1,0). This is called a false negative result because the positive sample is incorrectly identified by the classifier as being negative. The actual classification is negative and the predicted classification is positive (0,1). This is called a false positive result because the negative sample is incorrectly identified by the classifier as being positive. The actual classification is negative and the predicted classification is negative (0,0). This is called a true negative result because the negative sample gets correctly identified by the classifier. We can then perform the comparison between actual and predicted classifications and add this information to the table, making correct results appear in green so they are more easily identifiable. The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications. The four outcomes can be formulated in a 2×2 confusion matrix, as follows: The color convention of the three data tables above were picked to match this confusion matrix, in order to easily differentiate the data. Now, we can simply total up each type of result, substitute into the template, and create a confusion matrix that will concisely summarize the results of testing the classifier: In this confusion matrix, of the 8 samples with cancer, the system judged that 2 were cancer-free, and of the 4 samples without cancer, it predicted that 1 did have cancer. All correct predictions are located in the diagonal of the table (highlighted in green), so it is easy to visually inspect the table for prediction errors, as values outside the diagonal will represent them. By summing up the 2 rows of the confusion matrix, one can also deduce the total number of positive (P) and negative (N) samples in the original dataset, i.e. P = T P + F N {\displaystyle P=TP+FN} and N = F P + T N {\displaystyle N=FP+TN} . == Table of confusion == In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy). Accuracy will yield misleading results if the data set is unbalanced; that is, when the numbers of observations in different classes vary greatly. For example, if there were 95 cancer samples and only 5 non-cancer samples in the data, a particular classifier might classify all the observations as having cancer. The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate (sensitivity) for the cancer class but a 0% recognition rate for the non-cancer class. F1 score is even more unreliable in such cases, and here would yield over 97.4%, whereas informedness removes such bias and yields 0 as the probability of an informed decision for any form of guessing (here always guessing cancer). According to Davide Chicco and Giuseppe Jurman, the most informative metric to evaluate a confusion matrix is the Matthews correlation coefficient (MCC). Other metrics can be included in a confusion matrix, each of them having their significance and use. Some researchers have argued that the confusion matrix, and the metrics derived from it, do not truly reflect a model's knowledge. In particular, the confusion matrix cannot show whether correct predictions were reached through sound reasoning or merely by chance (a problem known in philosophy as epistemic luck). It also does not capture situations where the facts used to make a prediction later change or turn out to be wrong (defeasibility). This means that while the confusion matrix is a useful tool for measuring classification performance, it may give an incomplete picture of a model’s true reliability. == Confusion matrices with more than two categories == Confusion matrix is not limited to binary classification and can be used in multi-class classifiers as well. The confusion matrices discussed above have only two conditions: positive and negative. For example, the table below summarizes communication of a whistled language between two speakers, with zero values omitted for clarity. == Confusion matrices in multi-label and soft-label classification == Confusion matrices are not limited to single-label classification (where only one class is present) or hard-label settings (where classes are either fully present, 1, or absent, 0). They can also be extended to Multi-label classification (where multiple classes can be predicted at once) and soft-label classification (where classes can be partially present). One such extension is the Transport-based Confusion Matrix (TCM), which builds on the theory of optimal transport and the principle of maximum entropy. TCM applies to single-label, multi-label, and soft-label settings. It retains the familiar structure of the standard confusion matrix: a square matrix sized by the number of classes, with diagonal entries indicating correct predictions and off-diagonal entries indicating confusion. In the single-label case, TCM is identical to the standard confusion matrix. TCM follows the same reasoning as the standard confusion matrix: if class A is overestimated (its predicted value is greater than its label value) and class B is underestimated (its predicted value is less than its label value), A is considered confused with B, and the entry (B, A) is increased. If a class is both predicted and present, it is correctly identified, and the diagonal entry (A, A) increases. Optimal transport and maximum entropy are used to determine the extent to which these entries are updated. TCM enables clearer comparison between predictions and labels in complex classification tasks, while maintaining a consistent matrix format across settings.

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  Web worker

  A web worker, as defined by the World Wide Web Consortium (W3C) and the Web Hypertext Application Technology Working Group (WHATWG), is a JavaScript script executed from an HTML page that runs in the background, independently of scripts that may also have been executed from the same HTML page. Web workers are often able to utilize multi-core CPUs more effectively. The W3C and WHATWG envision web workers as long-running scripts that are not interrupted by scripts that respond to clicks or other user interactions. Keeping such workers from being interrupted by user activities should allow Web pages to remain responsive at the same time as they are running long tasks in the background. The web worker specification is part of the HTML Living Standard. == Overview == As envisioned by WHATWG, web workers are relatively heavy-weight and are not intended to be used in large numbers. They are expected to be long-lived, with a high start-up performance cost, and a high per-instance memory cost. Web workers run outside the context of an HTML document's scripts. Consequently, while they do not have access to the DOM, they can facilitate concurrent execution of JavaScript programs. == Features == Web workers interact with the main document via message passing. The following code creates a Worker that will execute the JavaScript in the given file. To send a message to the worker, the postMessage method of the worker object is used as shown below. The onmessage property uses an event handler to retrieve information from a worker. Once a worker is terminated, it goes out of scope and the variable referencing it becomes undefined; at this point a new worker has to be created if needed. == Example == The simplest use of web workers is for performing a computationally expensive task without interrupting the user interface. In this example, the main document spawns a web worker to compute prime numbers, and progressively displays the most recently found prime number. The main page is as follows: The Worker() constructor call creates a web worker and returns a worker object representing that web worker, which is used to communicate with the web worker. That object's onmessage event handler allows the code to receive messages from the web worker. The Web Worker itself is as follows: To send a message back to the page, the postMessage() method is used to post a message when a prime is found. == Support == If the browser supports web workers, a Worker property will be available on the global window object. The Worker property will be undefined if the browser does not support it. The following example code checks for web worker support on a browser Web workers are currently supported by Chrome, Opera, Edge, Internet Explorer (version 10), Mozilla Firefox, and Safari. Mobile Safari for iOS has supported web workers since iOS 5. The Android browser first supported web workers in Android 2.1, but support was removed in Android versions 2.2–4.3 before being restored in Android 4.4.

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  Creator economy

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

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  Line splice

  In electrical engineering and telecommunications, a line splice is a joint directly connecting lengths of electrical cables (electrical splice) or optical fibers (optical splice). The splices are often protected by sleeves. == Splicing of copper wires == The splicing of copper wires happens in the following steps: The cores are laid one above the other at the junction. The core insulation is removed. The wires are wrapped two to three times around each other (twisting). The bare veins on a length of about 3 cm "strangle" or "twist". In some cases, the strangulation is soldered. To isolate the splice, an insulating sleeve made of paper or plastic is pushed over it. The splicing of copper wires is mainly used on paper insulated wires. LSA techniques (LSA: soldering, screwing and stripping free) are used to connect copper wires, making the copper wires faster and easier to connect. LSA techniques include: Wire connection sleeves (AVH = Adernverbindungshülsen) and other crimp connectors. The two wires to be connected are inserted into the AVH without being stripped, which is then compressed with special pliers. The about 2 cm long AVH consist of contact, pressure and insulation. For wire connection strips (AVL = Adernverbindungsleisten) several pairs of wires (10 = AVL10 or 20 = AVL20) are inserted, the strip is then closed with a lid and pressed together with a hydraulic press, which ensures the connection. == Splicing of glass fibers == Fiber-optic cables are spliced using a special arc-splicer, with installation cables connected at their ends to respective "pigtails" - short individual fibers with fiber-optic connectors at one end. The splicer precisely adjusts the light-guiding cores of the two ends of the glass fibers to be spliced. The adjustment is done fully automatically in modern devices, whereas in older models this is carried out manually by means of micrometer screws and microscope. An experienced splicer can precisely position the fiber ends within a few seconds. Subsequently, the fibers are fused together (welded) with an electric arc. Since no additional material is added, such as gas welding or soldering, this is called a "fusion splice". Depending on the quality of the splicing process, attenuation values at the splice points are achieved by 0.3 dB, with good splices also below 0.02 dB. For newer generation devices, alignment is done automatically by motors. Here one differentiates core and jacket centering. At core centering (usually single-mode fibers), the fiber cores are aligned. A possible core offset with respect to the jacket is corrected. In the jacket centering (usually in multimode fibers), the fibers are adjusted to each other by means of electronic image processing in front of the splice. When working with good equipment, the damping value is according to experience at max. 0.1 dB. Measurements are made by means of special measuring devices including optical time-domain reflectometry (OTDR). A good splice should have an attenuation of less than 0.3 dB over the entire distance. Finished fiber optic splices are housed in splice boxes. One differentiates: Fusion splice Adhesive splicing Crimp splice or NENP (no-epoxy no-polish), mechanical splice

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  Dataset shift

  Dataset shift is a phenomenon in machine learning and statistics in which the joint distribution of input variables and target labels is different in the training phase and the deployment or test phase (i.e., P t r a i n ( X , Y ) ≠ P t e s t ( X , Y ) {\displaystyle P_{train}(X,Y)\neq P_{test}(X,Y)} ). This happens when the statistical properties of data used to train a model are no longer representative of the data encountered in real-world use, often resulting in degraded predictive performance and diminished generalization ability. Dataset shift is a generic term for a number of particular types of distributional change. Covariate shift is when the distribution of the input features changes, but the conditional relationship between inputs and outputs remains constant . Prior probability shift (or label shift) happens when the distribution of target labels changes, but the conditional distribution of inputs given labels stays the same. Concept shift (also known as concept drift) is the change of the conditional relationship between inputs and outputs that renders previously learned patterns invalid over time. A key challenge for deploying machine learning systems is dataset shift, in particular in dynamic environments where the data distributions change over time. Detecting and mitigating such shifts is an active area of research, e.g., drift detection, domain adaptation, continual learning.

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  Audience capture

  Audience capture is the phenomenon where an influencer is affected by their audience, catering to it with what they believe it wants to hear or is willing to pay for. This creates a positive feedback loop, which can lead the influencer to express more extreme views and behaviors. A famous example of audience capture can be found in the story of the online influencer Nicholas Perry, known as Nikocado Avocado. Perry started off on YouTube with videos of himself playing the violin and supporting veganism. He then shifted to videos of himself eating known as mukbang. Audience capture led him to more and more extreme eating leading him in turn to obesity and poor health. The effect can cause ideological media creators to become more politically radical, based on the feedback of their audience.

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  Technology company

  A technology company, or tech company, is a company that focuses primarily on the manufacturing, support, research and development of—most commonly computing, telecommunication and consumer electronics–based—technology-intensive products and services, which include businesses relating to digital electronics, software, optics, new energy, and Internet-related services such as cloud storage and e-commerce services. Big Tech refers to the 6 largest companies, both in the United States and globally, symbolized by the metonym 'Silicon Valley', where 4 of them are based. == Details == According to Fortune, as of 2020, the ten largest technology companies by revenue are: Apple Inc., Samsung, Foxconn, Alphabet Inc., Microsoft, Huawei, Dell Technologies, Hitachi, IBM, and Sony. Amazon has higher revenue than Apple, but is classified by Fortune in the retail sector. The most profitable listed in 2020 are Apple Inc., Microsoft, Alphabet Inc., Intel, Meta Platforms, Samsung, and Tencent. Apple Inc., Alphabet Inc. (owner of Google), Meta Platforms (owner of Facebook), Microsoft, and Amazon.com, Inc. are often referred to as the Big Five multinational technology companies based in the United States. These five technology companies dominate major functions, e-commerce channels, and information of the entire Internet ecosystem. As of 2017, the Big Five had a combined valuation of over $3.3 trillion and make up more than 40 percent of the value of the Nasdaq-100 index. Many large tech companies have a reputation for innovation, spending large sums of money annually on research and development. According to PwC's 2017 Global Innovation 1000 ranking, tech companies made up nine of the 20 most innovative companies in the world, with the top R&D spender (as measured by expenditure) being Amazon, followed by Alphabet Inc., and then Intel. As a result of numerous influential tech companies and tech startups opening offices in proximity to one another, a number of technology districts have developed in various areas across the globe. These include: Silicon Valley in the San Francisco Bay Area, Silicon Wadi in Israel, Silicon Docks in Dublin, Silicon Hills in Austin, Tech City in London; Digital Media City in Seoul, Zhongguancun in Beijing, Cyberjaya in Malaysia and Cyberabad in Hyderabad, India. As of 2026, Europe has six of the world's 100 most valuable tech companies, compared with 56 in the United States and 16 in China.

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  List of UPnP AV media servers and clients

  This is a list of UPnP AV media servers and client application or hard appliances. == UPnP AV media servers == === Software === === Cross-platform === Allonis myServer, a multi-faceted media player/organizer with a DLNA/UPnP server, controller, and renderer, including conversion. Runs on Microsoft Windows. Supports most all HTML5 devices as remote controls. Asset UPnP (DLNA compatible) from Illustrate. An audio specific UPnP/DLNA server for Windows, QNAP, macOS and Linux. Features audio WAVE/LPCM transcoding from a range of audio codecs, ReplayGain and playlists. FreeMi UPnP Media Server, very simple server, historically used to stream to the STB Freebox, based on .net/mono. Home Media Server, a free media server/player/controller for Windows, Linux, macOS, individual device settings, transcoding, external and internal subtitles, restricted device access to folders, uploading files, Internet-Radio, Internet-Television, Digital Video Broadcasting (DVB), DMR-control and "Play To", Music (Visualization), Photo (Slideshow), support for 3D-subtitles, support for BitTorrent files, Web-navigation with HTML5 player, Digital Media Renderer (DMR) emulation for AirPlay and Google Cast devices. Jellyfin, a free and open-source suite of multimedia applications designed to organize, manage, and share digital media files to networked devices. JRiver Media Center, a multi-faceted media player/organizer with a DLNA/UPnP server, controller, and renderer, including conversion. Supports Microsoft Windows, macOS and Linux. Kodi (previously XBMC), a cross platform open source software media-player/media center for Android, Apple TV, Linux, macOS and Windows. LimboMedia, a free cross platform home- and UPnP/DLNA mediaserver with android app and WebM transcoding for browser playback (build with java and FFmpeg). MinimServer, a Java-based highly configurable uPnP/DNLA music server with additional consideration given to Classical Music, supports transcoding with MinimStreamer, supports Microsoft Windows, macOS, Linux, and various NAS devices. Neutron Music Player, acts as a cross platform UPnP/DLNA Media Renderer server available for Android, iOS, BlackBerry 10 & PlayBook platforms. Supports gapless playback and has possibility to output rendered audio further to the high-resolution internal DAC or external USB DAC or another UPnP/DLNA Media Renderer with all supported DSP effects applied. Plex, a cross-platform and closed source software media player and entertainment hub for digital media, available for macOS, Microsoft Windows, Linux, as well as mobile clients for iOS (including Apple TV (2nd generation) onwards), Android, Windows Phone, and many devices such as Xbox. Supports on-the-fly transcoding of video and music. PonoMusic World. Based on the JRiver Media Center software, includes similar features along with a store for purchasing HD audio tracks. PS3 Media Server, a free cross platform Java based UPnP DLNA server especially good for AVC and other current HD media codecs with on-the-fly transcoding. Serviio, is available with a free and a pro license. It can stream media files (music, video or images) to renderer devices (e.g. a TV set, Blu-ray player, games console or mobile phone) on a local area network. TVMOBiLi, a cross platform, high performance UPnP/DLNA Media Server for Windows, macOS and Linux. TwonkyMedia server, a cross-platform multimedia server and entertainment hub for digital media, available for Android, Apple TV, iOS, Linux, macOS, Microsoft Windows, Windows Phone, and Xbox 360. Universal Media Server, a free (open source) DLNA-compliant UPnP Media Server for Windows, macOS and Linux (originally based on the PS3 Media Server). It is able to stream videos, audio and images to any DLNA-capable device. It contains more features than most paid UPnP/DLNA Media Servers. It streams to many devices including TVs (Samsung, Sony, Panasonic, LG, Philips and more.), PS3, Xbox(One/360), smartphones, Blu-ray players and more. vGet Cast, a simple, cross platform (Chrome App) DLNA server and controller for single, local video files. Vuze, an open-source Java-based BitTorrent client which contains MediaServer plugin. Wild Media Server, a media server/player/controller for Windows, Linux, macOS, individual device settings, transcoding, external and internal subtitles, restricted device access to folders, uploading files, Internet-Radio, Internet-Television, Digital Video Broadcasting (DVB), DMR-control and "Play To", Music (Visualization), Photo (Slideshow), support for 3D-subtitles, support for BitTorrent files, Web-navigation with HTML5 player, Digital Media Renderer (DMR) emulation for AirPlay and Google Cast devices. === Android === BubbleUPnP Android UPnP/DLNA server, player, controller and renderer CastLab Android UPnP/DLNA server. Pixel Media Server, Android UPnP/DLNA Media Server. Supports all popular Video and Audio files. It also support external subtitle file (SRT) Plato is an Android UPnP client app that can play videos and audio. Toaster Cast Android UPnP/DLNA server, controller and renderer vGet, Android App that can play videos embedded in websites on DLNA renderers. Media Cast UPnP, Android UPnP client app that can play videos/Audio. Media Server Pro is a DLNA server that allows individual file selections for sharing. Slick UPnP A minimal and intuitive open-source Android UPnP client app that can play video/audio. (It is not DMS) YAACC Open source UPnP controller, renderer and server app === Linux === === Microsoft Windows === Sundtek Streamingserver a native Windows TV Server providing DVB, ATSC and ISDB-T via UPnP/DLNA, it also supports streaming media files (it only supports TV devices from Sundtek). Stream What You Hear, a Windows application that streams the sound of your computer (i.e.: “what you hear”) to UPnP/DLNA device such as TVs, amps, network receivers, game consoles, etc... TVersity Media Server, a Windows application that streams multimedia content from a personal computer to UPnP, DLNA and mobile devices (Chromecast is also supported). It was the first media server to offer real-time transcoding (back in 2005). TVersity Screen Server, a Windows application that mirrors the screen of a personal computer to UPnP, DLNA and mobile devices. DVBViewer, a Windows application, mainly for TV/Radio recording/playback, but with the ability to stream live TV/radio as well as multimedia files via UPnP/DLNA. DivX, a Windows application, mainly for video encoding into DivX format, but has the ability to stream multimedia files via DLNA. foobar2000, a freeware audio player for Windows. Highly customizable, audio only. Download of dlna-extension from the developers' webpage necessary. Home Media Center, a free and open source media server compatible with DLNA. Includes web interface for streaming content to web browser (Android, iOS, ...), subtitles integration and Windows desktop streaming. This server is easy to use. KooRaRoo Media, a commercial DLNA media server and organizer for Windows. Includes on-the-fly transcoding, per-file and per-folder parental controls, powerful organizing features with dynamic playlists, Internet radio streaming, "Play To" functionality and remote device control, burned-in and external subtitles, extensive format support including RAW photo formats. Streams all files to all devices. Media Go, media player and tagger MediaMonkey, a free media player/tagger/editor with an UPnP/DLNA client and server for Microsoft Windows MusicBee, an audio player, supports UPnP via a plugin. Mezzmo, a commercial software package. Mezzmo streams music, movies, photos and subtitles to the UPnP and DLNA-enabled devices. It automatically finds and organizes music, movies and photos, imports multimedia files from iPad, iPhone, iPod, Audio CDs, iTunes, Windows Media Player and WinAmp. DLNA server supports all popular media file formats with real time transcoding to meet the device specifications. PlayOn, a commercial UPnP/DLNA media server for Windows, includes a transcoder for streaming web video. TVble, a cloud connected (Rotten tomatoes/TMDB etc.), Torrent streaming, DLNA enabled media server. Allows single file or playlist downloads. Windows Media Connect from Microsoft, a free UPnP AV MediaServer and control point (server and client) for Microsoft Windows WMC version 2.0 can be installed for usage with Windows Media Player 10 for Windows XP WMC version 3.0 can be installed for usage with Windows Media Player 11 for Windows XP WMC version 4.0 comes pre-installed on Windows Vista with its Windows Media Player 11 WMC can also refer to Windows Media Center. From the Windows Media Center entry in Wikipedia: In May 2015, Microsoft announced that Windows Media Center would be discontinued on Windows 10, and that it would be uninstalled when upgrading; but stated that those upgrading from a version of Windows that included the Media Center application would receive the paid Windows DVD Player app to maintain DVD playback functio

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  Scene statistics

  Scene statistics is a discipline within the field of perception. It is concerned with the statistical regularities related to scenes. It is based on the premise that a perceptual system is designed to interpret scenes. Biological perceptual systems have evolved in response to physical properties of natural environments. Therefore natural scenes receive a great deal of attention. Natural scene statistics are useful for defining the behavior of an ideal observer in a natural task, typically by incorporating signal detection theory, information theory or estimation theory. == Within-domain versus across-domain == Geisler (2008) distinguishes between four kinds of domains: (1) Physical environments (2) Images/Scenes (3) Neural responses and (4) Behavior. Within the domain of images/scenes one can study the characteristics of information related to redundancy and efficient coding. Across-domain statistics determine how an autonomous system should make inferences about its environment, process information and control its behavior. To study these statistics it is necessary to sample or register information in multiple domains simultaneously. == Applications == === Prediction of picture and video quality === One of the most successful applications of Natural Scenes Statistics Models has been perceptual picture and video quality prediction. For example, the Visual Information Fidelity (VIF) algorithm, which is used to measure the degree of distortion of pictures and videos, is used extensively by the image and video processing communities to assess perceptual quality. This is often after processing, such as compression, which can degrade the appearance of a visual signal. The premise is that the scene statistics are changed by distortion and that the visual system is sensitive to the changes in the scene statistics. VIF is heavily used in the streaming television industry. Other popular picture quality models that use natural scene statistics include BRISQUE and NIQE, both of which are no-reference since they do not require any reference picture to measure quality against.

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  Nanonetwork

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

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  Digital media in education

  Digital media in education refers to the use of digital technologies to support and enhance teaching and learning processes. This includes the application of multiple digital software applications, devices, and online platforms as tools for learning. Learners interact with these technologies to access, analyze, evaluate, and create media content and communication in various forms. The integration of digital media in education has dramatically increased over time, significantly transforming traditional educational practices. When viewed through a global and inclusive lens, digital education should be guided by principles of equity, inclusion, and public infrastructure to ensure meaningful participation of all learners. == History == === 20th century === Technological advances in the 20th century, particularly the invention of the Internet, laid the foundation for incorporating technology into education. In the early 1900s, the overhead projector and instructional radio broadcasts were among the first technologies used for educational purposes. The introduction of computers in classrooms occurred in 1950, when a flight simulation program was developed to train pilots at the Massachusetts Institute of Technology. However, access to computers remained extremely limited for several decades. In 1964, John Kemeny and Thomas Kurtz developed the BASIC programming language, which simplified computer interaction and introduced time-sharing, enabling multiple users to work on the same system simultaneously. This innovation made computing increasingly accessible for educational settings. By the 1980s, schools began to show more interest in computers as companies released mass-market devices to the public. Networking further enabled the interconnection of computers into unified communication systems, which proved more efficient and cost-effective than previous stand-alone machines. This development prompted wider adoption of computing in educational institutions. The invention of the World Wide Web in 1992 further simplified internet navigation and sparked further interest in educational settings. Initially, computers were integrated into school curricula for tasks such as word processing, spreadsheet creation, and data organization. By the late 1990s, the Internet became a research tool, functioning as a vast library. By 1999, 99% of public school teachers in the United States reported having access to at least one computer in their schools, and 84% had a computer available in their classrooms. The emergence of World Wide Web also contributed to the development of learning management systems (LMS), which allowed educators to create online teaching environments for content storage, student activities, discussions, and assignments. Advances in digital compression and high-speed Internet made video creation and distribution more affordable, fostering the use of the systems designed for recording lectures. These tools were often incorporated into learning management platforms, supporting the expansion of fully online courses. === 21st century === By 2002, the Massachusetts Institute of Technology began offering recorded lectures to the public, marking a significant milestone in the movement toward accessible online education. The launch of YouTube in 2005 further transformed educational content distribution. Educators increasingly uploaded lectures and instructional videos on platforms with initiatives like Khan Academy, which was active in 2006, contributing to You Tube's role as a prominent educational resource. In 2007, Apple launched iTunesU, another platform for sharing educational resources and videos. Meanwhile, learning management systems gained popularity, with Blackboard and Canvas becoming two of the most widely used platforms with Canvas's release in 2008. That same year also marked the introduction of the first Massive Open Online Course (MOOC), which provided open access to webinars and expert-led instructions for global learners. As technology evolved, traditional projectors were gradually replaced by interactive whiteboards, which enabled educators to integrate digital tools more effectively in their classrooms. By 2009, 97% of classrooms in the United States had at least one computer, and 93% had Internet access. The COVID-19 pandemic, which forced schools across the world to close, significantly impacted education with schools shifting to distance education. Students attended classes remotely using devices such as laptops, phones, and tablets, supported by digital platforms that facilitated at-home learning environments. However, adapting assessment methods to the new learning environment posed certain challenges. A study conducted by Eddie M. Mulenga and José M. Marbán on Zambian students during the pandemic revealed difficulties in adapting to digital learning, particularly in subjects like mathematics. Similar issues were reported among students in Romania, where the transition to virtual learning presented significant obstacles in engagement and adaptability. === Post-pandemic developments === In the period following the onset of COVID-19, education systems worldwide rapidly adopted digital solutions to maintain continuity of learning and teaching. By the end of March 2020, all 46 OECD and partners countries closed some or all of their schools nationwide. By June 2020, the length of school closures in these countries ranged from 7 to over 18 weeks. These disruptions in formal education prompted governments and educators to quickly adopt digital learning. This global shift to online education highlighted considerable inequalities in digital access, although many systems struggled with inequitable access, especially in regions lacking devices, stable internet connections, or conducive home learning environments. Stimultaneously, commercial educational technology (ed-tech) companies introduced rapid digital solutions to the disruption caused by the pandemic. This led to what has been described as a "seller's market," where the urgency of implementation may cause the prioritization of availability and scale over pedagogical and equity considerations. In the post-pandemic era, digital media in education continues to evolve. It increasingly intersects with artificial intelligence (AI) technologies such as adaptive learning platforms, AI-enabled content generation, and personalized learning environments. These tools enhance global engagement and access but also raise concerns about infrastructure, inclusivity, ethical implementation as well as critical pedagogies. Scholars recommend that educators and policymakers adopt inclusive practices, prioritize equitable infrastructure, and develop critical digital literacy. Facer and Selwyn also emphasize the need for public digital infrastructure and sustainable and justice-oriented policies that empower all learners. Overall, these perspectives reflect a growing consensus that digital media in education should be implemented critically to promote inclusive, multimodal, and future-oriented learning environments.

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