AI Art Backlash

AI Art Backlash — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Act! LLC

  ACT! (previously known as Activity Control Technology, Automated Contact Tracking, ACT! by Sage, and Sage ACT!) is a customer relationship management and marketing automation software platform designed for small and medium-sized businesses. It has over 2.8 million registered users as of December 2014. == History == The company Conductor Software was founded in 1986, in Dallas, Texas, by Pat Sullivan and Mike Muhney. The original name for the software was Activity Control Technology; it was renamed to Automated Contact Tracking, later abbreviated to ACT. The name of the company was subsequently changed to Contact Software International and it was sold in 1993 to Symantec Corporation, who in 1999 then sold it to SalesLogix. The Sage Group purchased Interact Commerce (formerly SalesLogix) in 2001 through Best Software, then its North American software division. Swiftpage acquired it in 2013. Beginning with the 2006 version, the name was styled ACT! by Sage, and in 2010 revised to Sage ACT!. Following its 2013 acquisition by Swiftpage, it was renamed to ACT! Swiftpage. In May 2018, ACT! was sold to SFW Advisors. In December 2018, Kuvana, a marketing automation software solution, was acquired by SFW and merged with ACT! This add-on is now a complementary service to the core CRM solution. In December 2019, ACT! hired Steve Oriola as chairman and CEO. In 2020, Swiftpage changed its company name to ACT!. In March 2023, ACT! hired Bruce Reading as President and CEO. == Software == ACT! features include contact, company and opportunity management, a calendar, marketing automation and e-marketing tools, reports, interactive dashboards with graphical visualizations, and the ability to track prospective customers. ACT! integrates with Microsoft Word, Excel, Outlook, Google Contacts, Gmail, and other applications via Zapier. For custom integrations, ACT! has an in-built API. ACT! can be accessed from Windows desktops (Win7 and later) with local or network shared database; synchronized to laptops or remote officers; Citrix or Remote Desktop; Web browsers (Premium only) with self or SaaS hosting; smartphones and tablets via HTML5 Web (Premium only); smartphones and tablets via sync with Handheld Contact.

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  Digital image correlation for electronics

  Digital image correlation analyses have applications in material property characterization, displacement measurement, and strain mapping. As such, DIC is becoming an increasingly popular tool when evaluating the thermo-mechanical behavior of electronic components and systems. == CTE measurements and glass transition temperature identification == The most common application of DIC in the electronics industry is the measurement of coefficient of thermal expansion (CTE). Because it is a non-contact, full-field surface technique, DIC is ideal for measuring the effective CTE of printed circuit boards (PCB) and individual surfaces of electronic components. It is especially useful for characterizing the properties of complex integrated circuits, as the combined thermal expansion effects of the substrate, molding compound, and die make effective CTE difficult to estimate at the substrate surface with other experimental methods. DIC techniques can be used to calculate average in-plane strain as a function of temperature over an area of interest during a thermal profile. Linear curve-fitting and slope calculation can then be used to estimate an effective CTE for the observed area. Because the driving factor in solder fatigue is most often the CTE mismatch between a component and the PCB it is soldered to, accurate CTE measurements are vital for calculating printed circuit board assembly (PCBA) reliability metrics. DIC is also useful for characterizing the thermal properties of polymers. Polymers are often used in electronic assemblies as potting compounds, conformal coatings, adhesives, molding compounds, dielectrics, and underfills. Because the stiffness of such materials can vary widely, accurately determining their thermal characteristics with contact techniques that transfer load to the specimen, such as dynamic mechanical analysis (DMA) and thermomechanical analysis (TMA), is difficult to do with consistency. Accurate CTE measurements are important for these materials because, depending on the specific use case, expansion and contraction of these materials can drastically affect solder joint reliability. For example, if a stiff conformal coating or other polymeric encapsulation is allowed to flow under a QFN, its expansion and contraction during thermal cycling can add tensile stress to the solder joints and expedite fatigue failure. DIC techniques will also allow the detection of glass transition temperature (Tg). At a glass transition temperature, the strain vs. temperature plot will exhibit a change in slope. Determining the Tg is very important for polymeric materials that could have glass transition temperatures within the operating temperature range of the electronics assemblies and components on which they are used. For example, some potting materials can see the Elastic Modulus of the material change by a factor of 100 or more over the glass transition region. Such changes can have drastic effects on an electronic assembly's reliability if they are not planned for in the design process. == Out-of-plane component warpage == When 3D DIC techniques are employed, out-of-plane motion can be tracked in addition to in-plane motion. Out-of-plane warpage is especially of interest at the component level of electronics packaging for solder joint reliability quantification. Excessive warpage during reflow can contribute to defective solder joints by lifting the edges of the component away from the board and creating head-in-pillow defects in ball grid arrays (BGA). Warpage can also shorten the fatigue life of adequate joints by adding tensile stresses to edge joints during thermal cycling. == Thermo-mechanical strain mapping == When a PCBA is over-constrained, thermo-mechanical stress brought about during thermal expansion can cause board strains that could negatively affect individual component and overall assembly reliability. The full-field monitoring capabilities of an image correlation technique allow for the measurement of strain magnitude and location on the surface of a specimen during a displacement-causing event, such as PCBA during a thermal profile. These "strain maps" allow for the comparison of strain levels over full areas of interest. Many traditional discrete methods, like extensometers and strain gauges, only allow for localized measurements of strain, inhibiting their ability to efficiently measure strain across larger areas of interest. DIC techniques have also been used to generate strain maps from purely mechanical events, such as drop impact tests, on electronic assemblies.

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  Communications system

  A communications system is a collection of individual telecommunications networks systems, relay stations, tributary stations, and terminal equipment usually capable of interconnection and interoperation to form an integrated whole. Communication systems allow the transfer of information from one place to another or from one device to another through a specified channel or medium. The components of a communications system serve a common purpose, are technically compatible, use common procedures, respond to controls, and operate in union. In the structure of a communication system, the transmitter first converts the data received from the source into a light signal and transmits it through the medium to the destination of the receiver. The receiver connected at the receiving end converts it to digital data, maintaining certain protocols e.g. FTP, ISP assigned protocols etc. Telecommunications is a method of communication (e.g., for sports broadcasting, mass media, journalism, etc.). Communication is the act of conveying intended meanings from one entity or group to another through the use of mutually understood signs and semiotic rules. == Types == === By media === An optical communication system is any form of communications system that uses light as the transmission medium. Equipment consists of a transmitter, which encodes a message into an optical signal, a communication channel, which carries the signal to its destination, and a receiver, which reproduces the message from the received optical signal. Fiber-optic communication systems transmit information from one place to another by sending light through an optical fiber. The light forms a carrier signal that is modulated to carry information. A radio communication system is composed of several communications subsystems that give exterior communications capabilities. A radio communication system comprises a transmitting conductor in which electrical oscillations or currents are produced and which is arranged to cause such currents or oscillations to be propagated through the free space medium from one point to another remote therefrom and a receiving conductor at such distant point adapted to be excited by the oscillations or currents propagated from the transmitter. Power-line communication systems operate by impressing a modulated carrier signal on power wires. Different types of power-line communications use different frequency bands, depending on the signal transmission characteristics of the power wiring used. Since the power wiring system was originally intended for transmission of AC power, the power wire circuits have only a limited ability to carry higher frequencies. The propagation problem is a limiting factor for each type of power line communications. === By technology === A duplex communication system is a system composed of two connected parties or devices which can communicate with one another in both directions. The term duplex is used when describing communication between two parties or devices. Duplex systems are employed in nearly all communications networks, either to allow for a communication "two-way street" between two connected parties or to provide a "reverse path" for the monitoring and remote adjustment of equipment in the field. An antenna is basically a small length of a conductor that is used to radiate or receive electromagnetic waves. It acts as a conversion device. At the transmitting end it converts high frequency current into electromagnetic waves. At the receiving end it transforms electromagnetic waves into electrical signals that is fed into the input of the receiver. several types of antenna are used in communication. Examples of communications subsystems include the Defense Communications System (DCS). === Examples: by technology === Telephone Mobile phone Tablet computer Television Telegraph Edison Telegraph TV cable Computer === By application area === The term transmission system is used in the telecommunications industry to emphasize the intermediate media, protocols, and equipment in the circuit, rather than particular end-user applications. A tactical communications system is a communications system that (a) is used within, or in direct support of tactical forces (b) is designed to meet the requirements of changing tactical situations and varying environmental conditions, (c) provides securable communications, such as voice, data, and video, among mobile users to facilitate command and control within, and in support of, tactical forces, and (d) usually requires extremely short installation times, usually on the order of hours, in order to meet the requirements of frequent relocation. An Emergency communication system is any system (typically computer based) that is organized for the primary purpose of supporting the two way communication of emergency messages between both individuals and groups of individuals. These systems are commonly designed to integrate the cross-communication of messages between are variety of communication technologies. An Automatic call distributor (ACD) is a communication system that automatically queues, assigns and connects callers to handlers. This is used often in customer service (such as for product or service complaints), ordering by telephone (such as in a ticket office), or coordination services (such as in air traffic control). A Voice Communication Control System (VCCS) is essentially an ACD with characteristics that make it more adapted to use in critical situations (no waiting for dial tone, or lengthy recorded announcements, radio and telephone lines equally easily connected to, individual lines immediately accessible etc..) == Key components == =

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  Microformat

  Microformats (μF) are predefined HTML markup (like HTML classes) created to serve as descriptive and consistent metadata about elements, designating them as representing a certain type of data (such as contact information, geographic coordinates, events, products, recipes, etc.). They allow software to process the information reliably by having set classes refer to a specific type of data rather than being arbitrary. Microformats emerged around 2005 and were predominantly designed for use by search engines, web syndication and aggregators such as RSS. Google confirmed in 2020 that it still parses microformats for use in content indexing. Microformats are referenced in several W3C social web specifications, including IndieAuth and Webmention. Although the content of web pages has been capable of some "automated processing" since the inception of the web, such processing is difficult because the markup elements used to display information on the web do not describe what the information means. Microformats can bridge this gap by attaching semantics, and thereby obviating other, more complicated, methods of automated processing, such as natural language processing or screen scraping. The use, adoption and processing of microformats enables data items to be indexed, searched for, saved or cross-referenced, so that information can be reused or combined. As of 2013, microformats allow the encoding and extraction of event details, contact information, social relationships and similar information. Microformats2, abbreviated as mf2, is the updated version of microformats. Mf2 provides an easier way of interpreting HTML structured syntax and vocabularies than the earlier ways that made use of RDFa and microdata. == Background == Microformats emerged around 2005 as part of a grassroots movement to make recognizable data items (such as events, contact details or geographical locations) capable of automated processing by software, as well as directly readable by end-users. Link-based microformats emerged first. These include vote links that express opinions of the linked page, which search engines can tally into instant polls. CommerceNet, a nonprofit organization that promotes e-commerce on the Internet, has helped sponsor and promote the technology and support the microformats community in various ways. CommerceNet also helped co-found the Microformats.org community site. Neither CommerceNet nor Microformats.org operates as a standards body. The microformats community functions through an open wiki, a mailing list, and an Internet relay chat (IRC) channel. Most of the existing microformats originated at the Microformats.org wiki and the associated mailing list by a process of gathering examples of web-publishing behaviour, then codifying it. Some other microformats (such as rel=nofollow and unAPI) have been proposed, or developed, elsewhere. == Technical overview == XHTML and HTML standards allow for the embedding and encoding of semantics within the attributes of markup elements. Microformats take advantage of these standards by indicating the presence of metadata using the following attributes: class Classname rel relationship, description of the target address in an anchor-element (...) rev reverse relationship, description of the referenced document (in one case, otherwise deprecated in microformats) For example, in the text "The birds roosted at 52.48, -1.89" is a pair of numbers which may be understood, from their context, to be a set of geographic coordinates. With wrapping in spans (or other HTML elements) with specific class names (in this case geo, latitude and longitude, all part of the geo microformat specification): Software agents can recognize exactly what each value represents and can then perform a variety of tasks such as indexing, locating it on a map and exporting it to a GPS device. === Examples === In this example, the contact information is presented as follows: With hCard microformat markup, that becomes: Here, the formatted name (fn), organisation (org), telephone number (tel) and web address (url) have been identified using specific class names and the whole thing is wrapped in class="vcard", which indicates that the other classes form an hCard (short for "HTML vCard") and are not merely coincidentally named. Other, optional, hCard classes also exist. Software, such as browser plug-ins, can now extract the information, and transfer it to other applications, such as an address book. == Specific microformats == Several microformats have been developed to enable semantic markup of particular types of information. However, only hCard and hCalendar have been ratified, the others remaining as drafts: hAtom (superseded by h-entry and h-feed) – for marking up Atom feeds from within standard HTML hCalendar – for events hCard – for contact information; includes: adr – for postal addresses geo – for geographical coordinates (latitude, longitude) hMedia – for audio/video content hAudio – for audio content hNews – for news content hProduct – for products hRecipe – for recipes and foodstuffs. hReview – for reviews rel-directory – for distributed directory creation and inclusion rel-enclosure – for multimedia attachments to web pages rel-license – specification of copyright license rel-nofollow, an attempt to discourage third-party content spam (e.g. spam in blogs) rel-tag – for decentralized tagging (Folksonomy) XHTML Friends Network (XFN) – for social relationships XOXO – for lists and outlines == Uses == Using microformats within HTML code provides additional formatting and semantic data that applications can use. For example, applications such as web crawlers can collect data about online resources, or desktop applications such as e-mail clients or scheduling software can compile details. The use of microformats can also facilitate "mash ups" such as exporting all of the geographical locations on a web page into (for example) Google Maps to visualize them spatially. Several browser extensions, such as Operator for Firefox and Oomph for Internet Explorer, provide the ability to detect microformats within an HTML document. When hCard or hCalendar are involved, such browser extensions allow microformats to be exported into formats compatible with contact management and calendar utilities, such as Microsoft Outlook. When dealing with geographical coordinates, they allow the location to be sent to applications such as Google Maps. Yahoo! Query Language can be used to extract microformats from web pages. On 12 May 2009 Google announced that they would be parsing the hCard, hReview and hProduct microformats, and using them to populate search result pages. They subsequently extended this in 2010 to use hCalendar for events and hRecipe for cookery recipes. Similarly, microformats are also processed by Bing and Yahoo!. As of late 2010, these are the world's top three search engines. Microsoft said in 2006 that they needed to incorporate microformats into upcoming projects, as did other software companies. Alex Faaborg summarizes the arguments for putting the responsibility for microformat user interfaces in the web browser rather than making more complicated HTML: Only the web browser knows what applications are accessible to the user and what the user's preferences are It lowers the barrier to entry for web site developers if they only need to do the markup and not handle "appearance" or "action" issues Retains backwards compatibility with web browsers that do not support microformats The web browser presents a single point of entry from the web to the user's computer, which simplifies security issues == Evaluation == Various commentators have offered review and discussion on the design principles and practical aspects of microformats. Microformats have been compared to other approaches that seek to serve the same or similar purpose. As of 2007, there had been some criticism of one, or all, microformats. The spread and use of microformats was being advocated as of 2007. Opera Software CTO and CSS creator Håkon Wium Lie said in 2005 "We will also see a bunch of microformats being developed, and that’s how the semantic web will be built, I believe." However, in August 2008 Toby Inkster, author of the "Swignition" (formerly "Cognition") microformat parsing service, pointed out that no new microformat specifications had been published since 2005. === Design principles === Computer scientist and entrepreneur, Rohit Khare stated that reduce, reuse, and recycle is "shorthand for several design principles" that motivated the development and practices behind microformats. These aspects can be summarized as follows: Reduce: favor the simplest solutions and focus attention on specific problems; Reuse: work from experience and favor examples of current practice; Recycle: encourage modularity and the ability to embed, valid XHTML can be reused in blog posts, RSS feeds, and anywhere else you can access the web. === Accessibi

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  Gemini Enterprise Agent Platform

  Gemini Enterprise Agent Platform (formerly known as Vertex AI) is a managed machine learning (ML) and artificial intelligence (AI) platform developed by Google Cloud. It provides a unified environment for building, training, deploying, and scaling ML models and generative AI applications. The platform integrates tools for the full ML lifecycle, including data preparation, model training, evaluation, deployment, and monitoring, under a single API and user interface. Vertex AI was announced at Google I/O and released as a generally available product on May 18, 2021. At launch, Google described Vertex AI as unifying its AutoML offerings with its prior Cloud AI Platform capabilities, and as adding operational features intended to help teams move models from experimentation into production use. On April 22, 2026, Google announced Gemini Enterprise Agent Platform as the replacement evolution of Vertex AI. == History == Google Cloud announced the general availability of Vertex AI on May 18, 2021, at the Google I/O developer conference. The platform was designed to consolidate Google Cloud's previously separate ML offerings, including AutoML and the legacy AI Platform, into a single system. At launch, Google claimed that Vertex AI required roughly 80% fewer lines of code to train a model compared to competing platforms. In June 2023, Google made generative AI support in Vertex AI generally available, giving developers access to foundation models including PaLM 2, Imagen, and Codey through the platform's Model Garden and the newly launched Generative AI Studio. At the time of this launch, Model Garden included over 60 models from Google and its partners. In August 2023, at the Google Cloud Next conference, Google announced further updates to Vertex AI, including the addition of third-party models such as Claude 2 from Anthropic and Llama 2 from Meta to the Model Garden, as well as new tools called Vertex AI Extensions for connecting models to APIs for real-time data retrieval. At the same event, Vertex AI Search and Conversation were made generally available, providing enterprise search and chatbot capabilities powered by foundation models. In April 2024, at Google Cloud Next, the company introduced Vertex AI Agent Builder, a no-code tool for creating AI-powered conversational agents built on top of Gemini large language models. This brought together the existing Vertex AI Search and Conversation products with new developer tools for building generative AI experiences. == Features == === Model training === Vertex AI supports both AutoML, which enables code-free model training on tabular, image, text, or video data, and custom training, which gives users full control over the ML framework, training code, and hyperparameter tuning. The platform provides serverless training as well as dedicated training clusters with GPU and TPU accelerators. Vertex AI Vizier handles automatic hyperparameter tuning, and Vertex AI Experiments allows comparison and tracking of training runs. === Model Garden === The Vertex AI Model Garden is a curated catalog of over 200 enterprise-ready models, including Google's own foundation models (such as Gemini, Imagen, and Veo), third-party models (such as Anthropic's Claude and Mistral AI models), and popular open-source models (such as Llama and Gemma). Models are accessible as fully managed model-as-a-service APIs. === Pipelines (workflow orchestration) === Vertex AI Pipelines provides managed orchestration of ML workflows and supports pipelines built with the Kubeflow Pipelines SDK, among other options described in Google Cloud documentation. === Vertex AI Studio === Vertex AI Studio provides tools for prompt design, testing, and model management, allowing developers to prototype and build generative AI applications using natural language, code, images, or video. === Agent Builder and Agent Engine === Vertex AI Agent Builder is a suite of products for building, deploying, and governing AI agents in production environments. It supports development with the open-source Agent Development Kit (ADK) and other frameworks. Vertex AI Agent Engine provides the underlying infrastructure for deploying and scaling agents, with support for enterprise security features including HIPAA compliance, customer-managed encryption keys (CMEK), and VPC Service Controls. === Generative AI tooling and model access === Google markets Vertex AI as providing access to Google foundation models (including the Gemini family) and developer tools such as Vertex AI Studio, along with a model catalog that includes Google and selected open source models (marketed as "Model Garden"). Google has also offered products within Vertex AI aimed at building generative search and conversational applications, including offerings named "Vertex AI Search" and "Vertex AI Conversation" as reported in 2023 coverage of platform updates. === MLOps tools === The platform includes a range of MLOps capabilities: Vertex AI Pipelines for orchestrating and automating ML workflows as reusable pipelines. Vertex AI Feature Store for serving, sharing, and reusing ML features across projects. Vertex AI Model Registry for storing, versioning, and managing trained models. Vertex AI Model Monitoring for detecting training-serving skew and inference drift in deployed models. Vertex Explainable AI for interpreting model predictions. Vertex AI Workbench for managed JupyterLab notebook environments integrated with Google Cloud Storage and BigQuery. == Industry recognition == Google was named a Leader for the fifth consecutive year in the 2024 Gartner Magic Quadrant for Cloud AI Developer Services, a recognition that encompasses Vertex AI and its related offerings. Google was also recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms and was named a Leader in the Forrester Wave for AI/ML Platforms, Q3 2024. In October 2025, Google was also named a Leader in the 2025 IDC (International Data Corporation) MarketScape for Worldwide GenAI Life-Cycle Foundation Model Software. == Pricing == Vertex AI uses a pay-as-you-go pricing model, with costs determined by the specific services consumed, including model training, prediction serving, and data storage. For generative AI tasks, pricing is based on a per-token model, with rates varying depending on the specific model used and whether tokens are input or output. Google offers a free tier for new users, which includes limited custom training hours and online prediction usage, along with an introductory US$300 in Google Cloud credits valid for 90 days. == Adoption == In the year following its 2021 launch, Google reported that usage of Vertex AI and BigQuery had driven 2.5 times more machine learning predictions compared to the prior year, and that active customers of Vertex AI Workbench had grown 25-fold over a six-month period. Early enterprise adopters included Ford, Wayfair, and Seagate, among others. Wayfair reported that it was able to run large model training jobs 5 to 10 times faster using the platform.

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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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  Electric Literature

  Electric Literature is an American literary magazine. == History == Founded by Andy Hunter and Scott Lindenbaum in 2009 as a print quarterly journal, Electric Literature transitioned to a daily website in 2012 under the helm of Halimah Marcus and Benjamin Samuel. Electric Literature publishes essays, reading lists, interviews, fiction, poetry, graphic narratives, humor, and book news, all available to read online for free without a paywall. It launched the first fiction magazine on the iPhone and iPad. Work published has been recognized by Best American Short Stories, Essays, Poetry, and Comics, the Pushcart Prize, Best Canadian Short Stories, The Best of the Small Presses, and the O. Henry Prize. in 2014, Electric Literature became a registered non-profit. In 2016, Halimah Marcus was appointed the first executive director of Electric Literature. She has been with the magazine since 2010. In 2021, Denne Michele Norris became editor-in-chief of Electric Literature, the first Black and openly trans editor-in-chief of a major U.S. literary publication. In 2022, Electric Literature was the Digital Prize Winner of the Whiting Literary Magazine Prizes. In 2023, Electric Literature partnered with Banned Books USA to offer free banned and challenged books to residents of Florida. In 2025, Electric Literature published their first book, edited by Norris and published by HarperOne: Both/And: Essays by Trans and Gender-Nonconforming Writers of Color. It builds on a prior essay series that Electric Literature sponsored for trans writers of color. Both/And became a finalist for the 2026 Lambda Literary Award for Transgender Nonfiction. == Recommended reading == In May 2012, Electric Literature launched Recommended Reading, a weekly fiction magazine. Each issue is curated by a well known editor or writer. == The Commuter == The Commuter, a weekly magazine for poetry, flash, graphic, or experimental narrative, debuted in January 2018, helmed by writer Kelly Luce.

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  FaceApp

  FaceApp is a photo and video editing application for iOS and Android developed by FaceApp Technology Limited, a company based in Cyprus. The app generates highly realistic transformations of human faces in photographs by using neural networks. The app can transform a face to make it smile, look younger, look older, or change gender. == History == FaceApp was launched on iOS in January 2017 and on Android in February 2017. It was developed by Yaroslav Goncharov, a former executive at Yandex, and created by the Russian company Wireless Lab. == Features == There are multiple options to manipulate the photo uploaded such as editor options of adding an impression, make-up, smiles, hair colors, hairstyles, glasses, age or beards. Filters, lens blur and backgrounds along with overlays, tattoos, and vignettes are also a part of the app. The gender change transformations of FaceApp have attracted particular interest from the LGBT and transgender communities, due to their ability to realistically simulate the appearance of a person as the opposite gender. == Criticism == In 2017, FaceApp faced criticism for a "hot" filter that appeared to lighten users' skin tones, prompting accusations of racial bias. The feature was briefly renamed "spark" before being removed. Founder Yaroslav Goncharov attributed the issue to training data bias and apologized. In August of that year, more criticism arose when it featured "ethnicity filters" depicting "White", "Black", "Asian", and "Indian". The filters were immediately removed from the app. In 2019, FaceApp faced criticism over its handling of user data, including concerns that it stored users' photos on its servers and could use them for commercial purposes. Founder Yaroslav Goncharov stated that images were processed on cloud servers like Google Cloud Platform and Amazon Web Services, not transferred to Russia, and were temporarily stored only to support editing functions before being deleted. U.S. Senator Chuck Schumer raised concerns about data privacy and called for an FBI investigation.

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  Actionstep

  Actionstep is a cloud-based legal practice management software for law firms and compliance-focused businesses. Actionstep is built to be a comprehensive practice management software with features for workflow automation as well as automatic document generation == History == Actionstep was created by Ted Jordan, CEO of Actionstep, in 2004. It was first used commercially in 2005 by a New Zealand construction franchise as well as a law firm. Actionstep soon expanded into central government and a wider range of small business users (mainly in New Zealand and Australia). After a few years the expanse of their legal client base prompted the company to add key legal specific features to the product with the aim of further expanding their legal market. Through Actionstep's tenure as a practice management software they have gradually expanded from their headquarters in New Zealand and offices located in the United Kingdom and the United States of America. In October 2020, private equity firm Serent Capital Partners purchased 84.25% stake in Actionstep. In April 2022, the company announced unlimited annual leave to its staff == Product == The premise of Actionstep is that it saves companies from having to purchase software tailored to their work flow and instead allows companies to modify the program without additional coding.{{Citation needed}} The founder and CEO Ted Jordan used cloud technology to allow the software to be continuously updated without the need to purchase or redesign new software. This theoretically allows businesses to remain current all the time and cut external I.T. costs.{{Citation needed}} Actionstep also integrates with software from other companies, such as Xero accounting, Microsoft Office & Office 365, Gmail, Google Drive, Dropbox, NetDocuments, QuickBooks, LawPay, BundleDocs, Box, HotDocs, Infotrack, GlobalX, PEXA, JOSEF and Zapier. Actionstep contains workflow automation features aimed at increasing office efficiency. These automated processes include automatic task assignment, information collection, document generation & automation, cataloguing, and matter generation. == Awards == Actionstep was named First International Best of SaaS Showplace Award Winner in 2009. Actionstep has also been a finalist in the ComputerWorld Excellence Awards (2007), and the Vero Excellence in Business Support (2010).

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  Bulletin (service)

  Bulletin was an online newsletter platform launched by Facebook on July 6, 2021, that allows notable writers to make announcements directly to their subscribers. Its competitors included Substack, of which Bulletin was called a "near-clone." Writers participating in the platform's launch included Malcolm Gladwell, Mitch Albom, Tan France, Jessica Yellin, Jane Wells, Erin Andrews and Dorie Greenspan. Facebook CEO Mark Zuckerberg stated that Bulletin represented the first time that the company had "built a project that is directly for journalists and individual writers." In October 2022 Meta announced the shutdown of Bulletin. The platform went into read only mode in January 2023 and became unavailable in April 2023. == History == Facebook announced Bulletin as its online newsletter platform on June 29, 2021. and launched by the company on July 6, 2021. Facebook CEO Mark Zuckerberg touted the service by saying that Bulletin represented the first time that the company had "built a project that is directly for journalists and individual writers." Writers participating in the platform's launch included Malcolm Gladwell, Mitch Albom, Tan France, Jessica Yellin, Jane Wells, Erin Andrews and Dorie Greenspan. == Reception == Unlike competitor such as Substack, Facebook indicated upon service's launch that it would not take a cut of subscription fees of writers using that platform. According to Washington Post technology writer Will Oremus, the move was criticized by those who viewed it as a form of predatory pricing intended by Facebook to force those competitors out of business. Sandeep Vaheesan, legal director of the think tank Open Markets, called for the government to reexamine predatory pricing as a violation of antitrust law, saying, "We want companies to compete by making better products, investing in new equipment and tech — not purely relying on their financial advantages to capture market share."

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  Randomized benchmarking

  Randomized benchmarking is an experimental method for measuring the average error rates of quantum computing hardware platforms. The protocol estimates the average error rates by implementing long sequences of randomly sampled quantum gate operations. Randomized benchmarking is the industry-standard protocol used by quantum hardware developers such as IBM and Google to test the performance of the quantum operations. The original theory of randomized benchmarking, proposed by Joseph Emerson and collaborators, considered the implementation of sequences of Haar-random operations, but this had several practical limitations. The now-standard protocol for randomized benchmarking (RB) relies on uniformly random Clifford operations, as proposed in 2006 by Dankert et al. as an application of the theory of unitary t-designs. In current usage randomized benchmarking sometimes refers to the broader family of generalizations of the 2005 protocol involving different random gate sets that can identify various features of the strength and type of errors affecting the elementary quantum gate operations. Randomized benchmarking protocols are an important means of verifying and validating quantum operations and are also routinely used for the optimization of quantum control procedures. == Overview == Randomized benchmarking offers several key advantages over alternative approaches to error characterization. For example, the number of experimental procedures required for full characterization of errors (called tomography) grows exponentially with the number of quantum bits (called qubits). This makes tomographic methods impractical for even small systems of just 3 or 4 qubits. In contrast, randomized benchmarking protocols are the only known approaches to error characterization that scale efficiently as number of qubits in the system increases. Thus RB can be applied in practice to characterize errors in arbitrarily large quantum processors. Additionally, in experimental quantum computing, procedures for state preparation and measurement (SPAM) are also error-prone, and thus quantum process tomography is unable to distinguish errors associated with gate operations from errors associated with SPAM. In contrast, RB protocols are robust to state-preparation and measurement errors Randomized benchmarking protocols estimate key features of the errors that affect a set of quantum operations by examining how the observed fidelity of the final quantum state decreases as the length of the random sequence increases. If the set of operations satisfies certain mathematical properties, such as comprising a sequence of twirls with unitary two-designs, then the measured decay can be shown to be an invariant exponential with a rate fixed uniquely by features of the error model. == History == Randomized benchmarking was proposed in Scalable noise estimation with random unitary operators, where it was shown that long sequences of quantum gates sampled uniformly at random from the Haar measure on the group SU(d) would lead to an exponential decay at a rate that was uniquely fixed by the error model. Emerson, Alicki and Zyczkowski also showed, under the assumption of gate-independent errors, that the measured decay rate is directly related to an important figure of merit, the average gate fidelity and independent of the choice of initial state and any errors in the initial state, as well as the specific random sequences of quantum gates. This protocol applied for arbitrary dimension d and an arbitrary number n of qubits, where d=2n. The SU(d) RB protocol had two important limitations that were overcome in a modified protocol proposed by Dankert et al., who proposed sampling the gate operations uniformly at random from any unitary two-design, such as the Clifford group. They proved that this would produce the same exponential decay rate as the random SU(d) version of the protocol proposed in Emerson et al.. This follows from the observation that a random sequence of gates is equivalent to an independent sequence of twirls under that group, as conjectured in and later proven in. This Clifford-group approach to Randomized Benchmarking is the now standard method for assessing error rates in quantum computers. A variation of this protocol was proposed by NIST in 2008 for the first experimental implementation of an RB-type for single qubit gates. However, the sampling of random gates in the NIST protocol was later proven not to reproduce any unitary two-design. The NIST RB protocol was later shown to also produce an exponential fidelity decay, albeit with a rate that depends on non-invariant features of the error model In recent years a rigorous theoretical framework has been developed for Clifford-group RB protocols to show that they work reliably under very broad experimental conditions. In 2011 and 2012, Magesan et al. proved that the exponential decay rate is fully robust to arbitrary state preparation and measurement errors (SPAM). They also proved a connection between the average gate fidelity and diamond norm metric of error that is relevant to the fault-tolerant threshold. They also provided evidence that the observed decay was exponential and related to the average gate fidelity even if the error model varied across the gate operations, so-called gate-dependent errors, which is the experimentally realistic situation. In 2018, Wallman and Dugas et al., showed that, despite concerns raised in, even under very strong gate-dependence errors the standard RB protocols produces an exponential decay at a rate that precisely measures the average gate-fidelity of the experimentally relevant errors. The results of Wallman. in particular proved that the RB error rate is so robust to gate-dependent errors models that it provides an extremely sensitive tool for detecting non-Markovian errors. This follows because under a standard RB experiment only non-Markovian errors (including time-dependent Markovian errors) can produce a statistically significant deviation from an exponential decay The standard RB protocol was first implemented for single qubit gate operations in 2012 at Yale on a superconducting qubit. A variation of this standard protocol that is only defined for single qubit operations was implemented by NIST in 2008 on a trapped ion. The first implementation of the standard RB protocol for two-qubit gates was performed in 2012 at NIST for a system of two trapped ions

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  Grid network

  A grid network is a computer network consisting of a number of computer systems connected in a grid topology. In a regular grid topology, each node in the network is connected with two neighbors along one or more dimensions. If the network is one-dimensional, and the chain of nodes is connected to form a circular loop, the resulting topology is known as a ring. Network systems such as FDDI use two counter-rotating token-passing rings to achieve high reliability and performance. In general, when an n-dimensional grid network is connected circularly in more than one dimension, the resulting network topology is a torus, and the network is called "toroidal". When the number of nodes along each dimension of a toroidal network is 2, the resulting network is called a hypercube. A parallel computing cluster or multi-core processor is often connected in regular interconnection network such as a de Bruijn graph, a hypercube graph, a hypertree network, a fat tree network, a torus, or cube-connected cycles. A grid network is not the same as a grid computer or a computational grid, although the nodes in a grid network are usually computers, and grid computing requires some kind of computer network or "universal coding" to interconnect the computers.

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  Neuro-symbolic AI

  Neuro-symbolic AI is a subfield of artificial intelligence that integrates neural methods (e.g., neural networks and deep learning) with symbolic methods (e.g., formal logic, knowledge representation, and automated reasoning). The goal is to combine the strengths of both approaches, resulting in AI systems that can be trained from raw data and demonstrate robustness against outliers or errors in the base data, while preserving explainability, explicit use of expert knowledge, and explicit cognitive reasoning. As argued by Leslie Valiant and others, the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning. Gary Marcus argued, "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning." Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much of useful knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation." Angelo Dalli, Henry Kautz, Francesca Rossi, and Bart Selman also argued for such a synthesis. Their arguments attempt to address the two kinds of thinking, as discussed in Daniel Kahneman's book Thinking, Fast and Slow. It describes cognition as encompassing two components: System 1 is fast, reflexive, intuitive, and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is used for pattern recognition. System 2 handles planning, deduction, and deliberative thinking. In this view, deep learning best handles the first kind of cognition, while symbolic reasoning best handles the second kind. Both are necessary for the development of a robust and reliable AI system capable of learning, reasoning, and interacting with humans to accept advice and answer questions. Since the 1990s, dual-process models with explicit references to the two contrasting systems have been the focus of research in both the fields of AI and cognitive science by numerous researchers. In 2025, the adoption of neurosymbolic AI, an approach that integrates neural networks with symbolic reasoning, increased in response to the need to address hallucination issues in large language models. For example, Amazon implemented Neurosymbolic AI in its Vulcan warehouse robots and Rufus shopping assistant to enhance accuracy and decision-making. == Approaches == Approaches for integration are diverse. Henry Kautz's taxonomy of neuro-symbolic architectures follows, along with some examples: Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models. Examples include BERT, RoBERTa, and GPT-3. Symbolic[Neural] is exemplified by AlphaGo, where symbolic techniques are used to invoke neural techniques. In this case, the symbolic approach is Monte Carlo tree search and the neural techniques learn how to evaluate game positions. Neural | Symbolic uses a neural architecture to interpret perceptual data as symbols and relationships that are reasoned about symbolically. Neural-Concept Learner is an example. Neural: Symbolic → Neural relies on symbolic reasoning to generate or label training data that is subsequently learned by a deep learning model, e.g., to train a neural model for symbolic computation by using a Macsyma-like symbolic mathematics system to create or label examples. NeuralSymbolic uses a neural net that is generated from symbolic rules. An example is the Neural Theorem Prover, which constructs a neural network from an AND-OR proof tree generated from knowledge base rules and terms. Logic Tensor Networks also fall into this category. Neural[Symbolic] according to Kautz, this approach embeds true symbolic reasoning inside a neural network. These are tightly-coupled neural-symbolic systems, in which the logical inference rules are internal to the neural network. This way, the neural network internally computes the inference from the premises and learns to reason based on logical inference systems. Early work on connectionist modal and temporal logics by Garcez, Lamb, and Gabbay is aligned with this approach. These categories are not exhaustive, as they do not consider multi-agent systems. In 2005, Bader and Hitzler presented a more fine-grained categorization that took into account, e.g., whether the use of symbols included logic and, if so, whether the logic was propositional or first-order logic. The 2005 categorization and Kautz's taxonomy above are compared and contrasted in a 2021 article. Sepp Hochreiter argued that Graph Neural Networks "...are the predominant models of neural-symbolic computing" since "[t]hey describe the properties of molecules, simulate social networks, or predict future states in physical and engineering applications with particle-particle interactions." == Artificial general intelligence == Gary Marcus argues that "...hybrid architectures that combine learning and symbol manipulation are necessary for robust intelligence, but not sufficient", and that there are ...four cognitive prerequisites for building robust artificial intelligence: hybrid architectures that combine large-scale learning with the representational and computational powers of symbol manipulation, large-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledge, reasoning mechanisms capable of leveraging those knowledge bases in tractable ways, and rich cognitive models that work together with those mechanisms and knowledge bases. This echoes earlier calls for hybrid models as early as the 1990s. == History == Garcez and Lamb described research in this area as ongoing, at least since the 1990s. During that period, the terms symbolic and sub-symbolic AI were popular. A series of workshops on neuro-symbolic AI has been held annually since 2005 Neuro-Symbolic Artificial Intelligence. In the early 1990s, an initial set of workshops on this topic were organized. == Research == Key research questions remain, such as: What is the best way to integrate neural and symbolic architectures? How should symbolic structures be represented within neural networks and extracted from them? How should common-sense knowledge be learned and reasoned about? How can abstract knowledge that is hard to encode logically be handled? == Implementations == Implementations of neuro-symbolic approaches include: AllegroGraph: an integrated Knowledge Graph based platform for neuro-symbolic application development. Scallop: a language based on Datalog that supports differentiable logical and relational reasoning. Scallop can be integrated in Python and with a PyTorch learning module. Logic Tensor Networks: encode logical formulas as neural networks and simultaneously learn term encodings, term weights, and formula weights. DeepProbLog: combines neural networks with the probabilistic reasoning of ProbLog. Abductive Learning: integrates machine learning and logical reasoning in a balanced-loop via abductive reasoning, enabling them to work together in a mutually beneficial way. SymbolicAI: a compositional differentiable programming library.

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  Hardware-based encryption

  Hardware-based encryption is the use of computer hardware to assist software, or sometimes replace software, in the process of data encryption. Typically, this is implemented as part of the processor's instruction set. For example, the AES encryption algorithm (a modern cipher) can be implemented using the AES instruction set on the ubiquitous x86 architecture. Such instructions also exist on the ARM architecture. However, more unusual systems exist where the cryptography module is separate from the central processor, instead being implemented as a coprocessor, in particular a secure cryptoprocessor or cryptographic accelerator, of which an example is the IBM 4758, or its successor, the IBM 4764. Hardware implementations can be faster and less prone to exploitation than traditional software implementations, and furthermore can be protected against tampering. == History == Prior to the use of computer hardware, cryptography could be performed through various mechanical or electro-mechanical means. An early example is the Scytale used by the Spartans. The Enigma machine was an electro-mechanical system cipher machine notably used by the Germans in World War II. After World War II, purely electronic systems were developed. In 1987 the ABYSS (A Basic Yorktown Security System) project was initiated. The aim of this project was to protect against software piracy. However, the application of computers to cryptography in general dates back to the 1940s and Bletchley Park, where the Colossus computer was used to break the encryption used by German High Command during World War II. The use of computers to encrypt, however, came later. In particular, until the development of the integrated circuit, of which the first was produced in 1960, computers were impractical for encryption, since, in comparison to the portable form factor of the Enigma machine, computers of the era took the space of an entire building. It was only with the development of the microcomputer that computer encryption became feasible, outside of niche applications. The development of the World Wide Web lead to the need for consumers to have access to encryption, as online shopping became prevalent. The key concerns for consumers were security and speed. This led to the eventual inclusion of the key algorithms into processors as a way of both increasing speed and security. == Implementations == === In the instruction set === ==== x86 ==== The X86 architecture, as a CISC (Complex Instruction Set Computer) Architecture, typically implements complex algorithms in hardware. Cryptographic algorithms are no exception. The x86 architecture implements significant components of the AES (Advanced Encryption Standard) algorithm, which can be used by the NSA for Top Secret information. The architecture also includes support for the SHA Hashing Algorithms through the Intel SHA extensions. Whereas AES is a cipher, which is useful for encrypting documents, hashing is used for verification, such as of passwords (see PBKDF2). ==== ARM ==== ARM processors can optionally support Security Extensions. Although ARM is a RISC (Reduced Instruction Set Computer) architecture, there are several optional extensions specified by ARM Holdings. === As a coprocessor === IBM 4758 – The predecessor to the IBM 4764. This includes its own specialised processor, memory and a Random Number Generator. IBM 4764 and IBM 4765, identical except for the connection used. The former uses PCI-X, while the latter uses PCI-e. Both are peripheral devices that plug into the motherboard. === Proliferation === Advanced Micro Devices (AMD) processors are also x86 devices, and have supported the AES instructions since the 2011 Bulldozer processor iteration. Due to the existence of encryption instructions on modern processors provided by both Intel and AMD, the instructions are present on most modern computers. They also exist on many tablets and smartphones due to their implementation in ARM processors. == Advantages == Implementing cryptography in hardware means that part of the processor is dedicated to the task. This can lead to a large increase in speed. In particular, modern processor architectures that support pipelining can often perform other instructions concurrently with the execution of the encryption instruction. Furthermore, hardware can have methods of protecting data from software. Consequently, even if the operating system is compromised, the data may still be secure (see Software Guard Extensions). == Disadvantages == If, however, the hardware implementation is compromised, major issues arise. Malicious software can retrieve the data from the (supposedly) secure hardware – a large class of method used is the timing attack. This is far more problematic to solve than a software bug, even within the operating system. Microsoft regularly deals with security issues through Windows Update. Similarly, regular security updates are released for Mac OS X and Linux, as well as mobile operating systems like iOS, Android, and Windows Phone. However, hardware is a different issue. Sometimes, the issue will be fixable through updates to the processor's microcode (a low level type of software). However, other issues may only be resolvable through replacing the hardware, or a workaround in the operating system which mitigates the performance benefit of the hardware implementation, such as in the Spectre exploit.

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  Anonymous social media

  Anonymous social media is a subcategory of social media wherein the main social function is to share and interact around content and information anonymously on mobile and web-based platforms. Another key aspect of anonymous social media is that content or information posted is not connected with particular online identities or profiles. == Background == Appearing very early on the web as mostly anonymous-confession websites, this genre of social media has evolved into various types and formats of anonymous self-expression. One of the earliest anonymous social media forums was 2channel, which was first introduced online on May 30, 1999, as a Japanese text board forum. With the way digital content is consumed and created continuously changing, the trending shift from web to mobile applications is also affecting anonymous social media. This can be seen as anonymous blogging, or various other format based content platforms such as nameless question and answer online platforms like Ask.fm introduced mobile versions of their services. The number of new networks joining the anonymous social sharing scene continues to grow rapidly. == Degrees of anonymity == Across different forms of anonymous social media there are varying degrees of anonymity. Some applications, such as Librex, require users to sign up for an account, even though their profile is not linked to their posts. While these applications remain anonymous, some of these sites can sync up with the user's contact list or location to develop a context within the social community and help personalize the user's experience, such as Yik Yak or Secret. Other sites, such as 4chan and 2channel, allow for a purer form of anonymity as users are not required to create an account, and posts default to the username of "Anonymous". While users can still be traced through their IP address, there are anonymizing services like I2P or various proxy server services that encrypt a user's identity online by running it through different routers. Secret users must provide a phone number or email when signing up for the service, and their information is encrypted into their posts. Stylometry poses a risk to the anonymity or pseudonymity of social media users, who may be identifiable by writing style; in turn, they may use adversarial stylometry to resist such identification. == Controversy == Apps such as Formspring, Ask, Sarahah, Whisper, and Secret have elicited discussion around the rising popularity of anonymity apps, including debate and anticipation about this social sharing class. As more and more platforms join the league of anonymous social media, there is growing concern about the ethics and morals of anonymous social networking as cases of cyber-bullying, and personal defamation occurs. Formspring, also known as spring.me, and Ask.fm have both been associated with teen suicides as a result of cyberbullying on the sites. Formspring has been associated with at least three teen suicides and Ask.fm with at least five. For instance, the app Secret got shut down due to its escalated use of cyberbullying. The app Yik Yak has also helped to contribute to more cyberbullying situations and, in turn, was blocked on some school networks. Their privacy policy meant that users could not be identified without a subpoena, search warrant, or court order. Another app called After School also sparked controversy for its app design that lets students post any anonymous content. Due to these multiple controversies, the app has been removed from both Apple and Google app stores. As the number of people using these platforms multiplies, unintended uses of the apps have increased, urging popular networks to enact in-app warnings and prohibit the use for middle and high school students. 70% of teens admit to making an effort to conceal their online behavior from their parents. Even Snapchat has some relation to the health of children after using social media. This is an app that is meant to be quick and simple but in many ways it can be overwhelming. A person can post something, and it will be gone in seconds. Oftentimes, the post that was made was inappropriate and harmful to another person. It's a never-ending cycle. Some of these apps have also been criticized for causing chaos in American schools, such as lockdowns and evacuations. In order to limit the havoc caused, anonymous apps are currently removing all abusive and harmful posts. Apps such as Yik Yak, Secret, and Whisper are removing these posts by outsourcing the job of content supervision to oversea surveillance companies. These companies hire a team of individuals to inspect and remove any harmful or abusive posts. Furthermore, algorithms are also used to detect and remove any abusive posts the individuals may have missed. Another method used by the anonymous app named Cloaq to reduce the number of harmful and abusive posts is to limit the number of users that can register during a certain period. Under this system, all contents are still available to the public, but only registered users can post. Other websites such as YouTube have gone on to create new policies regarding anonymity. YouTube now does not allow anonymous comments on videos. Users must have a Google account to like, dislike, comment or reply to comments on videos. Once a sign-in user "likes" a video, it will be added to that user's 'Liked video playlist'. YouTube changed their "Liked video playlist" policy in December 2019, allowing a signed-in user to keep their "Liked video playlist" private. Historically, these controversies and the rise of cyberbullying have been blamed on the anonymous aspect of many social media platforms, but about half of US adult online harassment cases do not involve anonymity, and researchers have found that if targeted harassment exists offline it will also be found online, because online harassment is a reflection of existing prejudices. == As platforms for anonymous discussion == Anonymous social media can be used for political discussion in countries where political opinions opposed to the government are normally suppressed, and allow persons of different genders to communicate freely in cultures where such communication is not generally accepted. In the United States, the 2016 presidential election led to an increase in the use of anonymous social media websites to express political stances. Moreover, anonymous social media can also provide authentic connection to complete anonymous communication. There have been cases where these anonymous platforms have saved individuals from life-threatening situation or spread news about a social cause. Additionally, anonymous social websites also allow internet users to communicate while also safeguarding personal information from criminal actors and corporations that sell users' data. A study in 2017 on the content posted to 4chan's /pol/ board found that the majority of the content was unique, including 70% of the 1 million images included in the studied data set. == Revenue generated by anonymous social media == === Anonymous apps === Generating revenue from anonymous apps has been a discussion for investors. Since little information is collected about the users, it is difficult for anonymous apps to advertise to users. However some apps, such as Whisper, have found a method to overcome this obstacle. They have developed a "keyword-based" approach, where advertisements are shown to users depending on certain words they type. The app Yik Yak has been able to capitalize on the features they provide. Anonymous apps such a Chrends take the approach of using anonymity to provide freedom of speech. Telephony app Burner has regularly been a top grossing utilities app in the iOS and Android app stores using its phone number generation technology. Despite the success of some anonymous apps, there are also apps, such as Secret, which have yet to find a way to generate revenue. The idea of an anonymous app has also caused mixed opinions within investors. Some investors have invested a large sum of money because they see the potential revenue generated within these apps. Other investors have stayed away from investing these apps because they feel these apps bring more harm than good. === Anonymous sites === There are several sources to generate revenue for anonymous social media sites. One source of revenue is by implementing programs such as a premium membership or a gift-exchanging program. Another source of revenue is by merchandising goods and specific usernames to users. In addition, sites such as FMyLife, have implemented a policy where the anonymous site will receive 50% of profit from apps that makes money off it. In terms of advertisements, some anonymous sites have had troubles implementing or attracting them. There are several reasons for this problem. Anonymous sites, such as 4chan, have received few advertisement offers due to some of the contents it generates. Other anonymous sites, such as Reddit, have been ca

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