AI Generator Character

AI Generator Character — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Splitwise

  Splitwise is an online expense-splitting application software accessible via web browser and mobile app. The app facilitates repayments of shared bills by calculating what each person in a group owes. The primary competitor to the app is Venmo, which only operates in the U.S. Splitwise allows users to create groups with friends to determine what each person owes. All expenses and allocations are added to the app, and Splitwise simplifies the transaction history to determine exactly what payments need to be made to whom to settle outstanding balances. Splitwise stores user information via cloud storage. It was developed and is owned by Splitwise Inc., based in Providence, Rhode Island, United States. == History == The app was launched in February 2011 as SplitTheRent, intended to be used for rent splitting, by Ryan Laughlin, Jon Bittner and Marshall Weir. In September 2013, Splitwise was integrated with Venmo to allow users to settle payments via Venmo. In April 2024, Splitwise partnered with Tink, a Visa payment services company, to incorporate a bank transfer feature directly in the Splitwise app. === Financing === In December 2014, the company raised $1.4 million. In October 2016, the company raised $5 million. In April 2021, Splitwise raised $20 million in funding from series A round run by Insight Partners. == Reception == A 2022 opinion piece in The Guardian by London journalist Imogen West-Knights shared the negative effects of exactly splitting bills among friends and family members. West-Knights argued that Splitwise and similar apps can "turn people into those true enemies of all that is fun and joyful in the world: accountants." However, she said the app does work better when used by couples rather than friend groups. Other reviews noted that the app makes people petty. In contrast, an article published by Condé Nast Traveler describes how Splitwise eliminated stress caused by complicated offline bill splitting, saying it "fixed such a pervasive obstacle in group travel." Coverage by The Wall Street Journal lands somewhere in between the two contrasting views, saying Splitwise and similar apps are helpful, but users need to be prepared for difficult money-related conversations that may arise. An etiquette advisor at Debrett's, said, "The less talk you can have about money on any of these occasions, the better." An editor suggested conversations as simple as asking, "We’re splitting this evenly, right?" before a meal.

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  Algorithmic curation

  Algorithm curation is the selection of online media by technologies such as recommender systems and personalized search. Curation entails the selective sharing of online content and recommendations based on inferred interests. Curation algorithms implement different filter approaches, such as collaborative filtering and content-based filtering. Examples include search engine and social media products such as the Twitter feed, Facebook's News Feed, and Google Personalized Search. == History == === Early algorithmic curation === Online platforms use newsfeed algorithms to determine what content to present to each user. The volume of content published on social media platforms created a need for automated filtering, as manual review of all available content by users is not feasible. These systems function as a form of gatekeeper, shaping which new material users are exposed to and influencing knowledge, attention, and political exposure. ==== Information overload ==== Early ranking algorithms addressed information overload by surfacing the most recent or most popular posts. Later systems shifted toward ranking content based on predicted engagement, aiming to increase the time users spend on a platform. Research has found that these engagement-oriented systems can increase the spread of misinformation and contribute to political polarization as a side effect of optimising for user interaction. ==== How algorithm changes users' feeds over time ==== Algorithmic curation has been found to increase source diversity in some respects while simultaneously reducing the number of external links presented to users, which limits exposure to off-platform content. Research using agent-based modelling has examined how user behaviour, information quality, and algorithmic design interact with one another over time. === Emergence of AI === Platforms increasingly shifted from rule-based ranking systems toward machine-learning and AI-driven approaches, which allow feeds to be personalised at a larger scale and with greater responsiveness to user behaviour. For example, X (formerly Twitter) moved away from a chronological feed toward an AI-powered ranking system that personalises content for each user. These systems are capable of making ranking decisions across volumes of content and user interactions that would not be practical to handle manually. == Approach == === Filter types === ==== Collaborative filtering ==== Collaborative filtering (CF) methods create recommendations based on a person's usage patterns. CF predicts a person's preference for an item by matching their interests with those of users who have similar interests. This process allows for the sharing of ratings between users with similar profiles. CF is based on patterns of human behaviour rather than machine analysis of content itself. Users of CF systems rate items they have interacted with, and these ratings form a profile of interests. The CF system then matches that user with others who have similar profiles, and uses their ratings to generate recommendations. Collaborative filtering can be applied across various content types including text, images, music, and financial products, and can account for complex attributes such as taste and quality that are difficult to represent explicitly. ==== Content-based filtering ==== Content-based filtering (CBF) builds a user profile to represent the types of items a user has engaged with, based on keywords and attributes used to describe those items. Recommendations are generated by presenting items similar to those the user has previously engaged with or is currently viewing. The CBF method creates a profile for each item based on discrete attributes and features, and then constructs a content-based user profile using a weighted vector of those features derived from items the user has rated, purchased, or interacted with. The weights represent the relative importance of each feature, and can be computed using techniques such as Bayesian classifiers, cluster analysis, decision trees, and artificial neural networks, with the goal of estimating the probability that a user will engage with a suggested item. One application of content-based filtering is Pandora Radio, where users provide an artist, genre, or composer to generate a station, and the system surfaces music with similar attributes. == Technology == === Recommender system === Recommender systems rank and suggest content to users based on a combination of implicit and explicit user input. Implicit signals include time spent viewing or engaging with a specific item. Explicit signals include actions such as liking posts, saving store pages, reading news articles, or sharing content. === Personalized search === Personalized search aims to retrieve results most relevant to the user by incorporating contextual factors beyond the explicit query, such as past queries, browsing history, and inferred interests. Social media platforms such as X (formerly Twitter) and Bluesky generate recommendations based on similar users and the content those users interact with. Personalized search may also allow users to explicitly filter results by blocking content containing certain phrases or hashtags. For first-time users without prior history, personalized search may draw on content-based filtering to establish an initial context. Similar processes are used by search engines and retail platforms to tailor results and product recommendations to individual users. == AI contribution == Artificial intelligence contributes to algorithmic curation through machine-learning models capable of processing large volumes of data. Techniques such as deep learning and reinforcement learning allow curation algorithms to model user preferences with greater granularity alongside established filtering approaches. This enables platforms to adjust content rankings rapidly in response to user behaviour. In social media and streaming contexts, AI-driven systems arrange feeds according to predicted relevance, with the outputs shaped by patterns present in the training data. == Social media and potential impact == === Echo chambers === Social media algorithms, such as those used by X (formerly Twitter), recommend content that the system predicts a user will engage with positively. Content from accounts with differing perspectives is less likely to be surfaced, which may reduce source and topic diversity and contribute to the formation of echo chambers. For example, Facebook's news feed is designed to surface content aligned with users' prior engagement, which may reinforce existing views. This dynamic may contribute to filter bubbles, in which users are seldom exposed to content outside their existing interests. Users may further narrow their feeds by actively blocking certain content or accounts. === Over-representation === A pattern observed across social media platforms is the concentration of algorithmic visibility among a small subset of users. Content from the most active users, those with the largest followings, or those generating the most engagement tends to be surfaced more frequently, meaning a small number of accounts can account for a disproportionate share of what appears in other users' feeds.

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  Artificial Intelligence for Digital Response

  Artificial Intelligence for Digital Response (AIDR) is a free and open source platform to filter and classify social media messages related to emergencies, disasters, and humanitarian crises. It has been developed by the Qatar Computing Research Institute and awarded the Grand Prize for the 2015 Open Source Software World Challenge. Muhammad Imran stated that he and his team "have developed novel computational techniques and technologies, which can help gain insightful and actionable information from online sources to enable rapid decision-making" - according to him the system "combines human intelligence with machine learning techniques, to solve many real-world challenges during mass emergencies and health issues". == How to use == It can be used by logging in with ones Twitter credentials and by collecting tweets by specifying keywords or hashtags, like #ChileEarthquake, and possibly a geographical region as well. == Use == It has been deployed in conjunction with UNICEF in Zambia to classify short messages related to AIDS/HIV received through the U-Report platform. AIDR was used for the first time during the 2010 Pakistan floods. The first real test of AIDR took place during the 2014 Iquique earthquake in Chile. == Related talks and events == Muhammad Imran delivered a keynote talk on the science behind the AIDR system at the International Conference on Information Systems for Crisis Response And Management (ISCRAM). Abdelkader Lattab and Ji Lucas also presented the system at the 2016 QCRI-IBM Data Science Connect event.

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  Death and the Internet

  A recent extension to the cultural relationship with death is the increasing number of people who die having created a large amount of digital content, such as social media profiles, that will remain after death. This may result in concern and confusion, because of automated features of dormant accounts (e.g. birthday reminders), uncertainty of the deceased's preferences that profiles be deleted or left as a memorial, and whether information that may violate the deceased's privacy (such as email or browser history) should be made accessible to family. Issues with how this information is sensitively dealt with are further complicated as it may belong to the service provider (not the deceased) and many do not have clear policies on what happens to the accounts of deceased users. While some sites, including Facebook and X (formerly Twitter), have policies related to death, others remain dormant until if applicable, deleted due to inactivity or transferred to family or friends. The FADA (Fiduciary Access to Digital Assets Act) was set in place to make it possible to transfer digital possessions legally. More broadly, the heavy increase in social media use is affecting cultural practices surrounding death. "Virtual funerals" and other forms of previously physical memorabilia are being introduced into the digital world, complete with public details of a person's life and death. == E-mail == Gmail and Hotmail allow the email accounts of the deceased to be accessed provided certain requirements are met. Yahoo! Mail will not provide access, citing the No Right of Survivorship and Non-Transferability clause in the Yahoo! terms of service. In 2005, Yahoo! was ordered by the Probate Court of Oakland County, Michigan, to release emails of deceased US Marine Justin Ellsworth to his father, John Ellsworth. == By website == === Facebook === ==== Policies ==== In its early days, Facebook used to delete profiles of dead people, but does not anymore. In October 2009, the company introduced "memorial pages" in response to multiple user requests related to the 2007 Virginia Tech shooting. After receiving a proof of death via a special form, the profile would be converted into a tribute page with minimal personal details, where friends and family members could share their grief. In February 2015, Facebook allowed users to appoint a friend or family member as a "legacy contact" with the rights to manage their page after death. It also gave Facebook users an option to have their account permanently deleted when they die. As of January 2019, all 3 options were active. ==== Controversies ==== In 2013, BuzzFeed criticized Facebook for the lack of control over memorialization that resulted in a "Facebook death" prank aimed at locking users out of their own accounts. In 2017, Reuters reported that a German court rejected a mother's demand to access her deceased daughter's memorialized account stating that the right to private telecommunications outweighed the right to inheritance. In July 2018, Dubai's DIFC Courts ruling clarified that Facebook, Twitter and other social media accounts should be bequeathed in legally binding will. Social media networks have also been criticized for not responding to relatives' requests to alter information on memorialized accounts. Another criticism is that Facebook users often are unaware that their content is ultimately owned not by them, but by Facebook. === Dropbox === ==== Policies ==== Dropbox determines inactive accounts by looking at sign-ins, file shares, and file activity over the previous 12 months. Once an account is determined inactive, Dropbox deletes the files on the account. To request access to the account of a deceased person, heirs are required to send appropriate documents by physical mail. === Google === ==== Policies ==== In April 2013, Google announced the creation of the 'Inactive Account Manager', which allows users of Google services to set up a process in which ownership and control of inactive accounts is transferred to a delegated user. Google also allows users to submit a range of requests regarding accounts belonging to deceased users. Google works with immediate family members and representatives to close online accounts in some cases once a user is known to be deceased, and in certain circumstances may also provide content from a deceased user's account. === X (formerly Twitter) === ==== Policies ==== Until 2010, Twitter (launched in July 2006) did not have a policy on handling deceased user accounts, and simply deleted timelines of deceased users. In August 2010, Twitter allowed memorialization of accounts upon request from family members, and also provided them with an option of either deleting the account or obtaining a permanent backup of the deceased user's public tweets. In 2014, Twitter updated its policy to include an option to delete deceased user photographs. This policy was implemented after multiple Twitter trolls sent Zelda Williams, daughter of Robin Williams, photoshopped images of her father. As of January 2019, the only option that Twitter offered for the accounts of dead people was account deactivation. Previously published content is not removed. To deactivate an account Twitter requires an immediate family member to present a copy of their ID and a death certificate of the deceased. Twitter specified that it does not provide account access to anyone, but does allow people having account login information to continue posting. A prominent example is Roger Ebert's account maintained by his wife Chaz. ==== Controversies ==== In 2012, The Next Web columnist Martin Bryant noticed that since Twitter, unlike Facebook, did not have a "one account per real person" emphasis, memorializing accounts presented a difficulty to the service. He also criticized the service for the lack of control over hacking of such accounts and disapproved the practice of passing dead people's usernames to new owners after a certain period of inactivity. In 2013, Variety ran a feature about Cory Monteith's Twitter account that had 1.5 million followers at the moment on his death and gained almost 1 million new followers afterwards. Monteith's fans also launched #DontDeleteCorysTwitter campaign. As of February 2019, the celebrity's account had 1.63 million followers. Various media reported awkward incidents related to automatic posting and account hacking. === iTunes === ==== Policies ==== iCloud and iTunes accounts are "non transferable" since the content is not owned — users only have a licence to access it. === Wikipedia === Users who have made at least several hundred edits or are otherwise known for substantial contributions to Wikipedia can be noted at a central memorial page. Wikipedia user pages are ordinarily fully edit-protected after the user has died, to prevent vandalism. === YouTube === YouTube grants access to accounts of deceased persons under certain conditions. It is one of the data options that one can select to give access to a trusted contact with Google's Inactive Account Manager. === Instagram === ==== Policies ==== As of the COVID-19 pandemic, Instagram has notified its users of a delay in time of reviewing reports of deceased users due to the limited staff the pandemic has caused. Users that submit a report on a deceased user on Instagram can either memorialize the account or remove it from Instagram's platform. Through memorializing the account, Instagram secures and protects a platform of a deceased user, but per their policy, they do not supply any of the login credentials to the account. For both memorializing or removing a deceased users account, a verified user needs to submit a tangible document that shows proof of death of the user. However, to fully remove an account, the user must be a close or direct family member to the deceased person, and show proof of credibility as well. === Microsoft === ==== Policies ==== Per Microsoft's policies, they do not supply any of the login credentials to a deceased user's Microsoft account. A user does not have to contact or notify Microsoft of the deceased user, as the related user is able to close the account themselves. At default, Microsoft removes accounts after 2 years of inactivity. If the user does not have access to the deceased user's account, Microsoft recommends that the user deletes all bank accounts linked to that of the deceased to ensure no subscriptions are still going through. If the user wants to request to gain access to the deceased user's account, a court order or a subpoena has to be provided to Microsoft, but does not guarantee access to the deceased user's account. For users that live in Germany, more documentation is needed to gain access of a deceased user's account, including the deceased user's death certificate, a form of ID, and a documentation of consent from the deceased. The requesting user needs to provide a form of ID as well. == Digital inheritance == Digital inheritance is the process of handing over

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  AI anthropomorphism

  AI anthropomorphism is the attribution of human-like feelings, mental states, and behavioral characteristics to artificial intelligence systems. Factors related to the user of the AI – such as culture, age, education, gender, and personality traits – are also important determinants of the strength of anthropomorphic effects. Since the earliest days of AI development, humans have interpreted machine outputs through anthropomorphic frameworks, but the recent emergence of generative AI has amplified these tendencies. In research and engineering, there is a distinction between anthropomorphism and anthropomorphic design. The former is an innate human tendency toward non-human entities. The latter is the scientific community effort to “design anthropomorphism”. Such a design can involve the manipulation of cues, including AI appearance, behaviour and language. Contemporary AI systems today can generate extremely human-like outputs and are often designed specifically to do so, meaning that their anthropomorphic effects can be especially powerful. In some cases, anthropomorphism is accompanied with explicit beliefs that AI systems are capable of empathy, goodwill, understanding, or consciousness. == Background == === In early AIs === Views of artificial agents possessing a human-like intelligence have existed since the early development of computers in the mid-1900s. The use of the human mind as a metaphor for understanding the workings of machine systems was prevalent among researchers in the early days of computer science, with multiple influential works widely distributing the idea of intelligent machines. Among the most widely cited papers of this period was Alan Turing's "Computing Machinery and Intelligence" in which he introduced the Turing Test, stating that a machine was intelligent if it could produce conversation that was indistinguishable from that of a human. These academic works in the 1940s and 1950s gave early credibility to the idea that machine workings could be thought of similarly to human minds. The public quickly came to view artificial systems similarly, with often exaggerated conceptions of the capabilities of early machines. Among the most well-known demonstrations of this was through the chatbot ELIZA designed by Joseph Weizenbaum in 1966. ELIZA responded to user inputs with a rudimentary text-processing approach that could not be considered anything resembling true understanding of the inputs, yet users, even when operating with full conscious knowledge of ELIZA's limitations, often began to ascribe motivation and understanding to the program's output. Weizenbaum later wrote, "I had not realized ... that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people." Comparisons between the intellectual capabilities of artificial intelligence and human intelligence were continually intensified by the attempts of computer scientists to develop machines that could perform human tasks at a level equal to or better than humans. A symbolic turning point was achieved in 1997, when IBM's chess supercomputer Deep Blue defeated then-world champion Garry Kasparov in a highly publicized six-game match. The defeat of a human by a machine for the first time in chess – a game viewed as a canonical example of human intellect – and the media attention surrounding the match led to a significant shift, where views of parallels between human and artificial intelligence moved from abstract speculation to being concretely demonstrated. A similar achievement was reached in the board game Go in 2017, when the program AlphaGo defeated world top-ranked Ke Jie. === Large language models === The AI boom of the 2020s brought about the widespread emergence of generative AI; in particular, chatbots such as ChatGPT, Gemini, and Claude based on large language models (LLMs) have become increasingly pervasive in everyday society. These systems are notable for the fact that they are able to respond to a wide range of prompts across contexts while producing strikingly human-like outputs – research has shown that humans are often unable to distinguish human-generated text from AI-generated text, and modern AI chatbots have formally been shown to pass the Turing test. As such, the anthropomorphic effects of AI are more powerful than ever. Given that LLMs have brought AI into the technological mainstream, considerable scientific effort has been devoted in recent years to understand existing and potential ramifications of AI in the public sphere; the prevalence and effects of anthropomorphism is one of those domains where much of this effort has been directed. == Current anthropomorphic attributions == === In the general public === Surveys have shown that a substantial portion of the public attributes human-like qualities to AI. In one sample of U.S. adults from 2024, two-thirds of people believed that ChatGPT is possibly conscious on some level, though other research has shown that the public still views the likelihood itself of AI consciousness as comparatively low. Another study conducted in 2025 found that women, people of color, and older individuals were most likely to anthropomorphize AI, as well as that – in general – humans view AIs as warm and competent, and anthropomorphic attributions to AI had increased by 34% in the past year. A YouGov poll reported that 46% of Americans believe that people should display politeness to AI chatbots by saying "please" and "thank you", demonstrating the application of social norms to AI. These beliefs extend to behavior, where majorities of AI users claim to always be polite to chatbots; of those who behave politely, most say they do so simply because it is the "nice" thing to do. In many recent cases, humans have developed robust interpersonal bonds with AI systems. For example: users of social chatbots like Replika and Character.ai have been documented to fall in love with the AIs, or to otherwise treat the AIs as intimate companions, and it has become increasingly common for individuals to use LLMs like ChatGPT as therapists. Chatbots are able to produce responses deeply attuned to users, as they are often designed to maximize agreeableness and mirror users' emotions; this can create compelling illusions of intimacy. === In the research community === In many cases, even AI researchers anthropomorphize AI systems in some capacity. Among the most extreme and well-publicized of these instances occurred in 2022, when engineer Blake Lemoine publicly claimed that Google's LLM LaMDA was conscious. Lemoine published the transcript of a conversation he had had with LaMDA regarding self identity and morality which he claimed was evidence of its sentience; he asserted that LaMDA was "a person" as defined by the United States Constitution and compared its mental capability to that of a 7- or 8-year-old. Lemoine's claims were widely dismissed by the scientific community and by Google itself, which described Lemoine's conclusions as "wholly unfounded" and fired him on the grounds that he had violated policies "to safeguard product information". It is much more common that AI researchers unintentionally imply humanness of AI through the ordinary use of anthropomorphic language to describe nonhuman agents. This kind of language, which Daniel Dennett coined the "intentional stance", is very common in everyday life in a variety of different contexts (e.g., "My computer doesn't want to turn on today"). For AI agents that may actually appear to very closely replicate some human abilities, however, the casual use of such anthropomorphic language in research has been scrutinized for being potentially misleading to the public. As early as 1976, Drew McDermott criticized the research community for the use of "wishful mnemonics", where AIs were referred to with terms like "understand" and "learn". In the LLM era, these criticisms have further intensified, with the negative effects of AI anthropomorphism in the public posing an especially salient danger given the elevated accessibility of modern AI. In some cases, the use of anthropomorphic language for AI is not unintentional, but is willfully used by researchers in order to promote better understanding of the brain – the idea being that, as AI can be functionally similar in some ways to the human brain, we may gain new insights and ideas from treating AI as a kind of model of the brain's workings. In particular, deep neuronal networks (DNNs) are often explicitly compared to the human brain, and significant advances in DNN research have stirred considerable enthusiasm about the ability of AI to emulate the human abilities. Caution has been urged in this domain as well, however; the use of anthropomorphic language can mask important differences that fundamentally distinguish AI from human intelligence. When it comes to DNNs, for example, it has been pointed out that they are still structurally quite different

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  List of search appliance vendors

  A search appliance is a type of computer which is attached to a corporate network for the purpose of indexing the content shared across that network in a way that is similar to a web search engine. It may be made accessible through a public web interface or restricted to users of that network. A search appliance is usually made up of: a gathering component, a standardizing component, a data storage area, a search component, a user interface component, and a management interface component. == Vendors of search appliances == Fabasoft Google InfoLibrarian Search Appliance™ Maxxcat Searchdaimon Thunderstone == Former/defunct vendors of search appliances == Black Tulip Systems Google Search Appliance Index Engines Munax Perfect Search Appliance

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  Digital Cinema Initiatives

  Digital Cinema Initiatives, LLC (DCI) is a consortium of major motion picture studios, formed to establish specifications for a common systems architecture for digital cinema systems. The organization was formed in March 2002 by Metro-Goldwyn-Mayer, Paramount Pictures, Sony Pictures, 20th Century Studios, Universal Studios, Walt Disney Studios and Warner Bros. Entertainment The primary purpose of DCI is to establish and document specifications for an open architecture for digital cinema that ensures a uniform and high level of technical performance, reliability and quality. By establishing a common set of content requirements, distributors, studios, exhibitors, d-cinema manufacturers and vendors can be assured of interoperability and compatibility. Because of the relationship of DCI to many of Hollywood's key studios, conformance to DCI's specifications is considered a requirement by software developers or equipment manufacturers targeting the digital cinema market. == Specification == On July 20, 2005, DCI released Version 1.0 of its "Digital Cinema System Specification", commonly referred to as the "DCI Specification". The document describes overall system requirements and specifications for digital cinema. Between March 28, 2006, and March 21, 2007, DCI issued 148 errata to Version 1.0. DCI released Version 1.1 of the DCI Specification on April 12, 2007, incorporating the previous 148 errata into the DCI Specification. On April 15, 2007, at the annual NAB Digital Cinema Summit, DCI announced the new version, as well as some future plans. They released the "Stereoscopic Digital Cinema Addendum" to begin to establish 3-D technical specifications in response to the popularity of 3-D stereoscopic films. It was also announced "which studios would take over the leadership roles in DCI after the current leadership term expires at the end of September." Subsequently, between August 27, 2007, and February 1, 2008, DCI issued 100 errata to Version 1.1. So, DCI released Version 1.2 of the DCI Specification on March 7, 2008, again incorporating the previous 100 errata into the specification document. An additional 96 errata were issued by August 30, 2012, so a revised Version 1.2 incorporating those additional errata was approved on October 10, 2012. DCI approved DCI Specification Version 1.3 on June 27, 2018, integrating the 45 errata issued to the previous version into a new document. On July 20, 2020, fifteen years to the day after Version 1.0, DCI issued a new DCI Specification Version 1.4 that assimilated 29 errata issued since Version 1.3. On October 13, 2021, DCI approved a new DCI Specification Version 1.4.1 that integrated the 23 errata that had been issued to DCI Specification Version 1.4. For the convenience of users, DCI also created an online HTML version of DCI Specification, Version 1.4.1. Due to the HTML conversion process, the footnotes in the DCSS now appear as endnotes. The PDF version contains pagination and page numbers whereas the HTML version does not. DCI Specification Version 1.4.2, dated June 15, 2022, includes revisions and refinements respecting Object-Based Audio Essence (OBAE), also known as Immersive Audio Bitstream (IAB). Version 1.4.2 also implements post-show log record collection utilizing SMPTE 430-17 SMS-OMB Communications Protocol Specification. Additionally, Version 1.4.2 incorporated two prior addenda: the Digital Cinema Object-Based Audio Addendum, dated October 1, 2018 and the Stereoscopic Digital Cinema Addendum, Version 1.0, dated July 11, 2007. Users using Version 1.4.2 no longer need to refer to the separate addenda. Previous DCSS versions are archived on the DCI web site. Based on many SMPTE and ISO standards, such as JPEG 2000-compressed image and "broadcast wave" PCM/WAV sound, the DCI Specification explains the route to create an entire Digital Cinema Package (DCP) from a raw collection of files known as the Digital Cinema Distribution Master (DCDM), as well as the specifics of its content protection, encryption, and forensic marking. The DCI Specification also establishes standards for the decoder requirements and the presentation environment itself, such as ambient light levels, pixel aspect and shape, image luminance, white point chromaticity, and those tolerances to be kept. Even though it specifies what kind of information is required, the DCI Specification does not include specific information about how data within a distribution package is to be formatted. Formatting of this information is defined by the Society of Motion Picture and Television Engineers (SMPTE) digital cinema standards and related documents. == Image and audio capability overview == === 2D image === 2048×1080 (2K) at 24 frame/s or 48 frame/s, or 4096×2160 (4K) at 24 frame/s In 2K, for Scope (2.39:1) presentation 2048×858 pixels of the imager is used In 2K, for Flat (1.85:1) presentation 1998×1080 pixels of the imager is used In 4K, for Scope (2.39:1) presentation 4096×1716 pixels of the imager is used In 4K, for Flat (1.85:1) presentation 3996×2160 pixels of the imager is used 12 bits per color component (36 bits per pixel) via dual HD-SDI (encrypted) 10 bits only permitted for 2K at 48 frame/s CIE XYZ color space, gamma-corrected TIFF 6.0 container format (one file per frame) JPEG 2000 compression From 0 to 5 or from 1 to 6 wavelet decomposition levels for 2K or 4K resolutions, respectively Compression rate of 4.71 bits/pixel (2K @ 24 frame/s), 2.35 bits/pixel (2K @ 48 frame/s), 1.17 bits/pixel (4K @ 24 frame/s) 250 Mbit/s maximum image bit rate === Stereoscopic 3D image === 2048×1080 (2K) at 48 frame/s - 24 frame/s per eye (4096×2160 4K not supported) In 2K, for Scope (2.39:1) presentation 2048×858 pixels of the imager is used In 2K, for Flat (1.85:1) presentation 1998×1080 pixels of the imager is used Optionally, in the HD-SDI link only: 12 bit color, YCxCz 4:2:2 (i.e. chroma subsampling in XYZ space), each eye in separate stream === Audio === 24 bits per sample, 48 kHz or 96 kHz Up to 16 channels WAV container, uncompressed PCM DCI has additionally published a document outlining recommended practice for High Frame Rate digital cinema. This document discloses the following proposed frame rates: 60, 96, and 120 frames per second for 2D at 2K resolution; 48 and 60 for stereoscopic 3D at 2K resolution; 48 and 60 for 2D at 4K resolution. The maximum compressed bit rate for support of all proposed frame rates should be 500 Mbit/s. == Related information == The idea for DCI was originally mooted in late 1999 by Tom McGrath, then COO of Paramount Pictures, who applied to the U.S. Department of Justice for anti-trust waivers to allow the joint cooperation of all seven major motion picture studios. Universal Pictures made one of the first feature-length DCPs created to DCI specifications, using their film Serenity. Although it was not distributed theatrically, it had one public screening on November 7, 2005, at the USC Entertainment Technology Center's Digital Cinema Laboratory in the Pacific Theatre, Hollywood. Inside Man (2006) was Universal's first DCP commercial release, and, in addition to 35mm film distribution, was delivered via hard drive to 20 theatres in the United States along with two trailers. The Academy Film Archive houses the Digital Cinema Initiatives, LLC Collection, which includes film and digital elements from DCI's Standard Evaluation Material (StEM), a 12-minute production shot on 35mm and 65mm film, created for vendors and standards organizations to test and evaluate image compression and digital projection technologies.

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  Media preservation

  Preservation of documents, pictures, recordings, digital content, etc., is a major aspect of archival science. It is also an important consideration for people who are creating time capsules, family history, historical documents, scrapbooks and family trees. Common storage media are not permanent, and there are few reliable methods of preserving documents and pictures for the future. == Paper/prints (photos) == Color negatives and ordinary color prints may fade away to nothing in a relatively short period if not stored and handled properly. This happens even if the negatives and prints are kept in the dark, because ambient light is not the determining factor, but heat and humidity are. The color degradation is the result of the dyes used in the color processes. Because color processing results in a less stable image than traditional black-and-white processing, black-and-white pictures from the 1920s are more likely to survive long-term than color films and photographs from after the middle 20th century. Black-and-white photographic films using silver halide emulsions are the only film types that have proven to last for archival storage. The determining factors for longevity include the film base type, proper processing (develop, stop, fix and wash) and proper storage. Early films used a Cellulose nitrate base which was prone to decomposition and highly flammable. Nitrate film was replaced with acetate-base films. These Cellulose acetate films were later discovered to outgass acids (also referred to as vinegar syndrome). Acetate films were replaced in the early 1980s by polyester film base materials which have been determined to be more stable than film stocks with a nitrate or acetate base. Color prints made on most inkjet printers look very good at first but they have a very short lifespan, measured in months rather than in years. Even prints from commercial photo labs will start to fade in a matter of years if not processed properly and stored in cool, dry environments. == Documents/books == With documents for which the media are not so critical as what the documents contain, the information in documents can be copied by using photocopiers and image scanners. Books and manuscripts can also have their information saved without destruction by using a book scanner. Where the medium itself needs to be preserved, for example if a document is a crayon sketch by a famous artist on paper, a complex process of preservation may be used. Depending on the condition and importance of the item this can include gluing the media onto more stable media, or protective enclosing of the media. Polyester sleeves, acid-free folders, and pH buffered document boxes are common supportive protective enclosures whose selection must match the media's chemical and physical properties. Other considerations in preserving paper/books are: Damaging light, particularly UV light, which fades and destroys media over time by breaking down the molecules. Atmosphere contains small traces of sulfur dioxide and nitric acid which turn media yellow and break the fibers down. Humidity and moisture also aid in the breakdown of media. If there is too much, the document can be attacked by bacteria, and if too little, cellulose material breaks down. Temperature, particularly elevated ones, can destroy some media. Low temperatures can cause the water to form crystals which expands destroying the structure of paper-based documents. == Online photo albums == Although there are many websites that allow the upload of photographs and videos, digital preservation for the long-term is still considered an issue. There is a lack of confidence that such websites are capable of storing data for long periods of time (ex. 50 years) without data degradation or loss. == Optical media - CD, DVD, Blu-ray, M-Disc == Write-once optical media, such as CD-Rs and DVD-Rs, typically contain an organic dye that distinguishes data reading from data writing based on the dye's transparency along the disc. Conventional CDs and DVDs have finite shelf-life due to natural degradation of the dye; the newer M-DISC uses inorganic material technology to produce molded DVDs and Blu-Rays (up to 3-layer 100GB BDXL) with a claimed lifespan of 100-1000 years if stored correctly with most BD & BDXL rated read/writers enabling the higher power mode for the M-Disc format after 2011. The National Archives and Records Administration lists published life expectancies to be 10 or 25 years or more for normal CDs and DVDs and conservative life expectancies to be between 2 and 5 years. Storage environments, such as temperature and humidity, as well as handling conditions such as frequency of media use and compatibility between the recorder and media, affect media shelf-life. Improvements in media storage and migrations to new recording technologies can make certain formats obsolete within their respective lifespan. Technologists have pointed to internet streaming services, where services such as video-on-demand have contributed to the 33 percent decline in DVD sales the past 5 years, as a challenge for digital preservation. == Magnetic media - video cassettes, tapes, hard drives == Magnetic media such as audio and video tape and floppy disks also have limited life spans. Audio and video tapes require specific care and handling to ensure that the recorded information will be preserved. For information that must be preserved indefinitely, periodic transcription from old media to new ones is necessary, not only because the media are unstable but also because the recording technology may become obsolete. Magnetic media also deteriorates naturally with typical shelf lives between 10 and 20 years. Magnetic tape can degrade from binder hydrolysis or magnetic remanence decay. Binder hydrolysis, also known as sticky-shed syndrome, refers to the breakdown of binder, or glue, that holds the magnetic particles to the polyester base of the tape. Tapes which have been stored in hot, humid conditions are particularly vulnerable to this phenomenon and may suffer from accelerated degradation. Severe binder can cause the magnetic material to fall off or sheds from the base, leaving a pile of dust and clear backing. Archivists can bake the tape, which evaporates water molecules on the tape, to temporarily restore the binder before making a copy. Magnetic tape can also be destabilized by magnetic remanence decay, which refers to the weakening of the tape's magnetization over time. This weakens the affected tape's readability, leading to reduced sound clarity and volume or picture hue and contrast. Baking the tape will not restore magnetization. Media at risk include recorded media such as master audio recordings of symphonies and videotape recordings of the news gathered over the last 40 years. Threats to media that must be considered when archiving important record media include accidental erasure, physical loss due to disasters such as fires and floods, and media degradation. Along with the actual media being degraded over the years, the machines that are available to play back or reproduce the audio sources are becoming archaic themselves. Manufacturers and their support (parts, technical updates) for their machines have disappeared throughout the years. Even if the medium is vaulted and archived correctly, the mechanical properties of the machines have deteriorated to the point that they could do more harm than good to the tape being played. Many major film studios are now backing up their libraries by converting them to electronic media files, such as .AIFF or .WAV-based files via digital audio workstations. That way, even if the digital platform manufacturer goes out of business or no longer supports their product, the files can still be played on any common computer. There is a detailed process that must take place previous to the final archival product now that a digital solution is in place. Sample rates and their conversion and reference speed are both critical in this process. In floppy disks, the lubricants inside the plastic jackets of many older floppies promote the decay of the magnetic medium. Also, the alignment of the magnetic particles of the disk substrate may gradually degrade, leading to a loss of formatting and data. Early laser disk media were prone to degradation as the layers of the disk substrate were bonded with an adhesive that was vulnerable to decay and would crumble over time. This would lead the different layers of the disk to peel apart, damaging the pitted data surface and rendering the disk unreadable.

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  Croissant (metadata format)

  Croissant is a metadata format design to support sharing of datasets for machine learning applications. It is a platform-agnostic schema used to standardize metadata in data repositories like Hugging Face, kaggle, Dataverse and OpenML. == Structure == Croissant builds upon schema.org, uses primarily JSON-LD, and divides metadata in four "layers": Dataset Metadata, Resource, Structure and Semantic: The Dataset Metadata layer constrains which schema.org properties should be used, including additional properties, linking together the resources (files) of the dataset with general metadata, like licensing and citation information. The Resource layer describes the individual files and sets of those using two new classes, FileObject and FileSet. A FileSet may be a collection of related images. The Structure layer specifies how the files are organized in the dataset. A RecordSet class describes how resources are present, configurations that may very a lot between modality. This specification facilitates interoperability of the datasets. Finally, the Semantic layer adds information for practical reuse of the dataset, such as splits for train, test and validation subsets. It also provides a default extension for metadata related to responsible AI. The use of a standard machine-readable structure increases, for example, the discoverability of datasets in search engines such as Google Dataset Search. == History == Croissant was shared in arXiv in March 2024 and published in the proceedings of NeurIPS 2024. It started as community driven as a MLCommons Croissant Working Group, including stakeholders organizations from academia and industry, including Google, the open data institute, Sage Bionetworks and King's College London. Variations of Croissant are developed to support datasets in different areas of research, such as Geo-Croissant for geospatial datasets. Other technical extensions, such as support for RDF, soon followed.

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  Digital signal

  A digital signal is a signal that represents data as a sequence of discrete values; at any given time it can only take on, at most, one of a finite number of values. This contrasts with an analog signal, which represents continuous values; at any given time it represents a real number within an infinite set of values. Simple digital signals represent information in discrete bands of levels. All levels within a band of values represent the same information state. In most digital circuits, the signal can have two possible valid values; this is called a binary signal or logic signal. They are represented by two voltage bands: one near a reference value (typically termed as ground or zero volts), and the other a value near the supply voltage. These correspond to the two values zero and one (or false and true) of the Boolean domain, so at any given time a binary signal represents one binary digit (bit). Because of this discretization, relatively small changes to the signal levels do not leave the discrete envelope, and as a result are ignored by signal state sensing circuitry. As a result, digital signals have noise immunity; electronic noise, provided it is not too great, will not affect digital circuits, whereas noise always degrades the operation of analog signals to some degree. Digital signals having more than two states are occasionally used; circuitry using such signals is called multivalued logic. For example, signals that can assume three possible states are called three-valued logic. In a digital signal, the physical quantity representing the information may be a variable electric current or voltage, the intensity, phase or polarization of an optical or other electromagnetic field, acoustic pressure, the magnetization of a magnetic storage media, etcetera. Digital signals are used in all digital electronics, notably computing equipment and data transmission. == Definitions == The term digital signal has related definitions in different contexts. === In digital electronics === In digital electronics, a digital signal is a pulse amplitude modulated signal, i.e., a sequence of fixed-width electrical pulses or light pulses, each occupying one of a discrete number of levels of amplitude. A special case is a logic signal or a binary signal, which varies between a low and a high signal level. The pulse trains in digital circuits are typically generated by metal–oxide–semiconductor field-effect transistor (MOSFET) devices, due to their rapid on–off electronic switching speed and large-scale integration (LSI) capability. In contrast, bipolar junction transistors more slowly generate signals resembling sine waves. === In signal processing === In digital signal processing, a digital signal is a representation of a physical signal that is sampled and quantized. A digital signal is an abstraction that is discrete in time and amplitude. The signal's value only exists at regular time intervals, since only the values of the corresponding physical signal at those sampled moments are significant for further digital processing. The digital signal is a sequence of codes drawn from a finite set of values. The digital signal may be stored, processed or transmitted physically as a pulse-code modulation (PCM) signal. === In communications === In digital communications, a digital signal is a continuous-time physical signal, alternating between a discrete number of waveforms, representing a bitstream. The shape of the waveform depends on the transmission scheme, which may be either a line coding scheme allowing baseband transmission; or a digital modulation scheme, allowing passband transmission over long wires or over a limited radio frequency band. Such a carrier-modulated sine wave is considered a digital signal in literature on digital communications and data transmission, but considered as a bit stream converted to an analog signal in specific cases where the signal will be carried over a system meant for analog communication, such as an analog telephone line. In communications, sources of interference are usually present, and noise is frequently a significant problem. The effects of interference are typically minimized by filtering off interfering signals as much as possible and by using data redundancy. The main advantages of digital signals for communications are often considered to be noise immunity, and the ability, in many cases such as with audio and video data, to use data compression to greatly decrease the bandwidth that is required on the communication media. == Logic voltage levels == A waveform that switches representing the two states of a Boolean value (0 and 1, or low and high, or false and true) is referred to as a digital signal or logic signal or binary signal when it is interpreted in terms of only two possible digits. The two states are usually represented by some measurement of an electrical property: Voltage is the most common, but current is used in some logic families. Two ranges of voltages are typically defined for each logic family, which are frequently not directly adjacent. The signal is low when in the low range and high when in the high range, and in between the two ranges the behavior can vary between different types of gates. The clock signal is a special digital signal that is used to synchronize many digital circuits. The image shown can be considered the waveform of a clock signal. Logic changes are triggered either by the rising edge or the falling edge. The rising edge is the transition from a low voltage (level 1 in the diagram) to a high voltage (level 2). The falling edge is the transition from a high voltage to a low one. Although in a highly simplified and idealized model of a digital circuit, we may wish for these transitions to occur instantaneously, no real-world circuit is purely resistive, and therefore no circuit can instantly change voltage levels. This means that during a short, finite transition time, the output may not properly reflect the input, and will not correspond to either a logically high or low voltage. == Modulation == To create a digital signal, a signal must be modulated with a control signal to produce it. The simplest modulation, a type of unipolar encoding, is simply to switch on and off a DC signal so that high voltages represent a '1' and low voltages are '0'. In digital radio schemes, one or more carrier waves are amplitude, frequency or phase modulated by the control signal to produce a digital signal suitable for transmission. Asymmetric Digital Subscriber Line (ADSL) over telephone wires, does not primarily use binary logic; the digital signals for individual carriers are modulated with different-valued logics, depending on the Shannon capacity of the individual channel. == Clocking == Digital signals may be sampled by a clock signal at regular intervals by passing the signal through a flip-flop. When this is done, the input is measured at the clock edge and the signal from that time. The signal is then held steady until the next clock. This process is the basis of synchronous logic. Asynchronous logic also exists, which uses no single clock, and generally operates more quickly, and may use less power, but is significantly harder to design.

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  The Culture of Connectivity

  The Culture of Connectivity: A Critical History of Social Media is a book by José van Dijck published by Oxford University Press in 2013 on social media platforms and their history. The author considers the histories of five social media platforms: Facebook, Twitter, Flickr, YouTube, and Wikipedia. She focuses on how their technological, social and cultural dimensions contribute to their current status.

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  Battleboarding

  Battleboarding, also known as versus debating and "who would win" debating, is an activity that involves discussing and debating around hypothetical fights between individuals; most popularly, fictional characters. These debates are often held in forums, blogs, sites and wikis, known as versus sites or battle boards. Netizens who engage in battleboarding online are often called "battleboarders". The earliest iterations of battleboarding first appeared in various online boards and forums, though its origins can be traced back to magazines, television shows, and comic book letter columns. Eventually, the online activity grew, becoming one of the most popular internet activities today, and spawning many online communities dedicated solely for battleboarding. It soon evolved into its own subculture, and even went on to inspire other media. == History == === Origins === Before the advent of the internet, articles about hypothetical fights were published in magazines. These articles range from topics like sports, comics and anime, such as Black Belt Magazine issue May 1997 which discussed about a hypothetical match between Muhammad Ali and Bruce Lee, and Wizard Magazine #133 which discussed about various hypothetical fights between American comic characters against Japanese anime characters. During that time, many comic book publishers also conceptualized and published "versus" storylines like Batman Versus Predator and Justice League/Avengers. Many films also capitalized on the concept of characters from different franchises fighting each other, such as Frankenstein Meets the Wolf Man (1934), King Kong vs. Godzilla (1962), Freddy vs Jason (2003), and Alien vs. Predator (2004). Another inspiration behind battleboarding were television shows and documentaries whose premise involved hypothetical fights concerning a variety of subjects like zoology, paleontology, and military history. These include shows such as Animal Face-Off (which pitted animals against each other), Deadliest Warrior (which pitted historical warriors, oftentimes from different time periods, against each other), and Jurassic Fight Club (which was about analyzing cases where different types of dinosaurs fought one another). Death Battle, a web series about pitting characters against each other that began in 2010, is a similar show that soon inspired many battleboarding communities and fandoms. Death Battle, as with many other battleboarding series and websites before it, utilised "calcs", which are mathematical equations that try to calculate how strong a character or weapon is. Other popular web series about the subject include Super Power Beat Down and Grudge Match. === Forums and sites === Many internet forums about movies, comics, anime, and video games often held discussions about hypothetical fights between characters from these media. These discussions would be the first iteration of online battleboarding. A notable early battleboarding website was stardestroyer.net (founded 1998), created by Michael Wong. The website focuses in large part on match-ups between the Star Wars and Star Trek franchises, and also includes a forum covering this as well as other more general battleboarding topics, usually related to science fiction and space opera. In addition to the forums, several webpages written by the administrators and contributors were embedded on the site. These attempted to mathematically quantify the capabilities of Star Wars technology and prove their superiority to their Star Trek equivalents, such as Wong's "Star Wars vs Star Trek: Technology Overview" and Brian Young's "Turbolaser Commentaries." stardestroyer.net had a notable impact on early battleboarding culture and also influenced official products. Curtis Saxton, author of several officially-licensed Star Wars technical reference books, thanked Wong, Young, and several other stardestroyer.net contributors by name in the acknowledgements section of Star Wars: Attack of the Clones Incredible Cross-Sections (2002), referring to them as "prominent among the hundreds of people contributing to constructive debates about Star Wars technicalities over the years, resulting in the consensus of conceptual and physical foundations applied in these pages." Saxton's books in the Incredible Cross-Sections series contain specific numbers about the capabilities of Star Wars ships original to these publications and not used in any other official sources. In an interview conducted by TheForce.Net, Saxton claimed to have been offered the job of writing reference books by a DK employee familiar with his "Star Wars Technical Commentaries" webpage (1995–2001), where Saxton attempted to calculate the firepower, speed, and durability of Star Wars spaceships using his background as an astrophysics student. One of the oldest and longest-running battleboarding forum is Comic Vine's "battle forum", whose first post was in 2007. Comic Vine also has one of the largest impacts on battleboarding, creating many common rules and terminologies such as "bloodlusted", "morals are off", "speed equalized", and many others. Another long-running battle forum is a subreddit called r/whowouldwin, where redditors can post and debate fights about real or fictional individuals. Verdicts of these match-ups are often chosen by using evidences of a character's power, weakness, or feat, such as movie clips, comic book panel scans, and excerpts from related literature; all of which are posted and categorized in a separate subreddit called r/respectthreads. Other influential battle forums include Fanverse, where users can post their own calcs about a character's power level. The popularity of battle forums inspired the creation of websites dedicated only for battleboarding. These include The Outskirts Battle Dome, a website that popularized the use of "power levels" in battleboarding; the aforementioned stardestroyer.net; and Space Battles, a website whose forums and threads are filled with posts about hypothetical fights between characters as well as other related topics. Another influential battleboarding site is the now defunct Fact Pile, and its sister site, FactPileTopia. Fact Pile is one of the first battleboarding site that actually listed down and documented winners of their match-ups. The site closed down in 2016 along with its forum, wikia, and YouTube channel. Besides these, blogs about battleboarding were also created, such as dreager1.com. === Wikis === Nowadays, the most popular battleboarding communities can be seen in Fandom, with two of the oldest and most popular being Deadliest Fiction and VS Battles Wiki. Deadliest Fiction is a Deadliest Warrior-inspired fanon created in July 2010 by a group of historians, academics, and pop culture enthusiasts. Being one of the most influential and accurate battleboarding sites around, Deadliest Fiction allows users to create hypothetical match-ups in the form of blogs, where other users can vote and debate around who will win in the comment section. Once a verdict is reached, the site allows the user to create a simulated fanfiction of how the fight would happen. The same year in October, a similar battleboarding site named VS Battles Wiki was created. In the VS Battles Wiki, users can create profiles and power levels of characters, post match-ups in its threads and forums, and list down the winners and losers of these threads in said character profiles. The wiki is considered the most active wiki battleboarding site today, with over 1 million visitors per month. However, throughout the years, the VS Battles Wiki has had its share of controversies, such as alleged inaccuracies in its profiles. There have also been websites and fanfiction wikis inspired by the battleboarding internet show Death Battle. These include the long-running G1 Death Battle Fan Blog, r/deathbattlematchups, and the popular Death Battle Fanon Wiki and DBX Fanon Wiki. Death Battle also released its own dice and card game, complete with rules and effects taken from battleboarding. == Subculture == In its rise in popularity, battleboarding has given birth to a unique online subculture with its own rules, activities, and terminologies. Several of these influences have become present in other online communities and popular media. Some of the common slang and terminologies used in battleboarding subculture includes: Battle Field Removal: Often abbreviated to "BFR", this is a rule that a fight can end if one character is taken out of a battlefield. This rule is used for characters who have the powers to teleport or transport enemies without actually killing them. Battle Royale: A term originating from Comic Vine in which multiple characters are pitted against each other. The name is probably derived from the film Battle Royale or the video game genre of the same name. Bloodlusted: A hypothetical situation wherein the characters are pitted against each other while in a furious, berserker-like state. Calc: These are calculations battl

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  Mobile cloud computing

  Mobile Cloud Computing (MCC) is the combination of cloud computing and mobile computing to bring rich computational resources to mobile users, network operators, as well as cloud computing providers. The ultimate goal of MCC is to enable execution of rich mobile applications on a plethora of mobile devices, with a rich user experience. MCC provides business opportunities for mobile network operators as well as cloud providers. More comprehensively, MCC can be defined as "a rich mobile computing technology that leverages unified elastic resources of varied clouds and network technologies toward unrestricted functionality, storage, and mobility to serve a multitude of mobile devices anywhere, anytime through the channel of Ethernet or Internet regardless of heterogeneous environments and platforms based on the pay-as-you-use principle." == Architecture == MCC uses computational augmentation approaches (computations are executed remotely instead of on the device) by which resource-constraint mobile devices can utilize computational resources of varied cloud-based resources. In MCC, there are four types of cloud-based resources, namely distant immobile clouds, proximate immobile computing entities, proximate mobile computing entities, and hybrid (combination of the other three model). Giant clouds such as Amazon EC2 are in the distant immobile groups whereas cloudlet or surrogates are member of proximate immobile computing entities. Smartphones, tablets, handheld devices, and wearable computing devices are part of the third group of cloud-based resources which is proximate mobile computing entities. Vodafone, Orange and Verizon have started to offer cloud computing services for companies. == Challenges == In the MCC landscape, an amalgam of mobile computing, cloud computing, and communication networks (to augment smartphones) creates several complex challenges such as Mobile Computation Offloading, Seamless Connectivity, Long WAN Latency, Mobility Management, Context-Processing, Energy Constraint, Vendor/data Lock-in, Security and Privacy, Elasticity that hinder MCC success and adoption. === Open research issues === Although significant research and development in MCC is available in the literature, efforts in the following domains is still lacking: Architectural issues: A reference architecture for heterogeneous MCC environment is a crucial requirement for unleashing the power of mobile computing towards unrestricted ubiquitous computing. Energy-efficient transmission: MCC requires frequent transmissions between cloud platform and mobile devices, due to the stochastic nature of wireless networks, the transmission protocol should be carefully designed. Context-awareness issues: Context-aware and socially-aware computing are inseparable traits of contemporary handheld computers. To achieve the vision of mobile computing among heterogeneous converged networks and computing devices, designing resource-efficient environment-aware applications is an essential need. Live VM migration issues: Executing resource-intensive mobile application via Virtual Machine (VM) migration-based application offloading involves encapsulation of application in VM instance and migrating it to the cloud, which is a challenging task due to additional overhead of deploying and managing VM on mobile devices. Mobile communication congestion issues: Mobile data traffic is tremendously hiking by ever increasing mobile user demands for exploiting cloud resources which impact on mobile network operators and demand future efforts to enable smooth communication between mobile and cloud endpoints. Trust, security, and privacy issues: Trust is an essential factor for the success of the burgeoning MCC paradigm. It is because the data along with code/component/application/complete VM is offloaded to the cloud for execution. Moreover, just like software and mobile application piracy, the MCC application development models are also affected by the piracy issue. Pirax is known to be the first specialized framework for controlling application piracy in MCC requirements == MCC research groups and activities == Several academic and industrial research groups in MCC have been emerging since last few years. Some of the MCC research groups in academia with large number of researchers and publications include: MDC, Mobile and Distributed Computing research group is at Faculty of Computer and Information Science, King Saud University. MDC research group focuses on architectures, platforms, and protocols for mobile and distributed computing. The group has developed algorithms, tools, and technologies which offer energy efficient, fault tolerant, scalable, secure, and high performance computing on mobile devices. MobCC lab, Faculty of Computer Science and Information Technology, University Malaya. The lab was established in 2010 under the High Impact Research Grant, Ministry of Higher Education, Malaysia. It has 17 researchers and has track of 22 published articles in international conference and peer-reviewed CS journals. ICCLAB, Zürich University of Applied Sciences has a segment working on MCC. The InIT Cloud Computing Lab is a research lab within the Institute of Applied Information Technology (InIT) of Zürich University of Applied Sciences (ZHAW). It covers topic areas across the entire cloud computing technology stack. Mobile & Cloud Lab, Institute of Computer Science, University of Tartu. Mobile & Cloud Lab conducts research and teaching in the mobile computing and cloud computing domains. The research topics of the group include cloud computing, mobile application development, mobile cloud, mobile web services and migrating scientific computing and enterprise applications to the cloud. SmartLab, Data Management Systems Laboratory, Department of Computer Science, University of Cyprus. SmartLab is a first-of-a-kind open cloud of smartphones that enables a new line of systems-oriented mobile computing research. Mobile Cloud Networking: Mobile Cloud Networking (MCN) was an EU FP7 Large-scale Integrating Project (IP, 15m Euro) funded by the European Commission. The MCN project was launched in November 2012 for the period of 36 month. The project was coordinated by SAP Research and the ICCLab at the Zurich University of Applied Science. In total 19 partners from industry and academia established the first vision of Mobile Cloud Computing. The project was primarily motivated by an ongoing transformation that drives the convergence between the Mobile Communications and Cloud Computing industry enabled by the Internet and is considered the first pioneer in the area of Network Function Virtualization.

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  Gnowit

  Gnowit (pronounced "know it") is a Canadian software company that provides automated, near-real-time monitoring of legislative, regulatory, and political activity across Canada. Its platform aggregates and analyzes information from government publications, parliamentary debates, committee, and proceedings to provide searchable alerts and reports for organizations monitoring public policy and regulatory developments. The system uses natural-language processing and machine learning techniques to organize and filter large volumes of public information.; the company reports that new publication documents are captured and millions of items are added to its repository daily. == History, Founders and Leadership == Gnowit was co-founded in Ottawa in 2010 by Shahzad Khan and Mohammad Al-Azzouni; Khan serves as chief executive officer. Khan holds a PhD in Computer Science from the University of Cambridge, has more than two decades of experience in AI/ML and computational linguistics, and has authored or co-authored 37 peer-reviewed publications and five patents. Traditionally, companies performed this analysis manually; Gnowit has delivered efficiencies achieved through AI innovations. The company has participated in several Canadian startup and accelerator programs, including Carleton University's Lead To Win initiative, the University of Ottawa's Startup Garage, the Invest Ottawa incubator, and the League of Innovators' BOOST program. === Kubernetes validation (2019–2020) === As part of a Canada's Centre of Excellence in Next Generation Networks (CENGN) project, Gnowit validated a containerized version of its web-intelligence software on Kubernetes. Between 2019 and 2020, Gnowit participated in a project with Canada’s Centre of Excellence in Next Generation Networks (CENGN) to test and scale its platform using containerized infrastructure based on Kubernetes. The initiative focused on improving scalability and supporting the company’s transition from a monolithic software architecture to a cloud-native deployment model. == Products and services == Gnowit markets several modules for public-affairs, compliance, and market-intelligence teams. Legislative & Regulatory Monitoring (vAnalyst). vAnalyst is a monitoring platform that tracks legislative and regulatory activity across Canadian federal, provincial, and territorial jurisdictions. The system aggregates parliamentary debates, bills, committee proceedings, and regulatory publications and provides searchable alerts and reporting tools. The product monitors more than two million web sources to surface relevant items quickly. Parliamentary Live (vAnalyst). Monitors live video feeds from parliamentary sessions and committees with same-day transcripts, AI-generated summaries, witness summaries, and motion detection; municipal coverage is offered as an option. Gnowit can avail transcripts up to two weeks before official releases. These transcripts enable users to navigate and review lengthy parliamentary sittings and committee discussions through searchable text. Municipal Monitoring (vAnalyst). The platform also tracks council meetings, agendas, bylaws, and other municipal government publications from hundreds of Canadian municipalities. The platform aggregates these sources into a single searchable interface for reviewing local government decisions. Curation Edge (analyst service). Curation Edge is an add-on service in which expert analysts work and collaborate with clients to develop a tailored curation guide and deliver daily newsletters or briefs on legislation and media. These reports provide concise summaries, relevant links, and optional metadata, prioritizing key updates with additional context and analysis. The service is customizable, including branding and formatting for executive audiences, and is intended to reduce information overload, support decision-making, and streamline the synthesis and distribution of information. === Coverage and sources === Gnowit monitors sources span Canadian government materials across federal, provincial, and territorial jurisdictions Hansard transcripts (All Jurisdictions, including committees), order papers, committee transcripts, gazettes, bills, acts and regulations, consultations, regulatory-agency publications, and global news media as well as press releases and council-meeting materials from hundreds of municipalities. == Partnerships and support == Gnowit reports collaborations with Canadian academic and ecosystem partners, including: Algonquin College Carleton University McGill University University of Ottawa Université du Québec en Outaouais (UQO) Queen's University The company also participated in the accelerator program at Invest Ottawa and has received support from Canadian research and innovation programs, including: NRC Industrial Research Assistance Program (NRC-IRAP) Mitacs Ontario Centre of Innovation (OCI) (formerly OCE) Gnowit has also referenced membership in the Southern Ontario Smart Computing Innovation Platform (Government of Canada profile: FedDev Ontario – SOSCIP overview). == Technology == Gnowit develops technology intended to support timely decision-making by delivering updates from monitored web sources as they are published. The platform applies artificial intelligence (AI) and machine learning (ML) techniques to monitor, capture, clean, analyze, filter, and organize text, and to generate concise briefs. Its technical approach combines Boolean queries, shallow language processing techniques, and machine learning classifiers within a self-service interface. The company has described its longer-term development framework in relation to a belief–desire–intention (BDI) model of intelligent agents on the web. Gnowit and its founder are listed as inventors/assignees on patents concerning multi-document clustering, salient-content extraction, and sentiment analysis methods that are consistent with these features: US 9,600,470 – Method and system relating to re-labelling multi-document clusters (assignee: Whyz Technologies Ltd.). US 9,336,202 – Method and system relating to salient content extraction for information retrieval (assignee: Whyz Technologies Ltd.). CA 2,865,184 C – Method and system relating to re-labelling multi-document clusters. CA 2,865,186 C – Procédé et système concernant l'analyse de sentiment d'un contenu (sentiment analysis; French record). CA 2,865,187 C – Method and system relating to salient content extraction for information retrieval. == Research and community == In January 2025, Gnowit personnel contributed to regulatory NLP by co-authoring a peer-reviewed paper at the 1st Regulatory NLP Workshop (RegNLP 2025), co-located with COLING in Abu Dhabi. Titled Unifying Large Language Models and Knowledge Graphs for Efficient Regulatory Information Retrieval and Answer Generation, the work introduces PolicyInsight, a framework that joins a dynamic policy data model and knowledge graph with LLMs to monitor policy texts, detect changes, and support retrieval and answer generation; the author list includes Shahzad Khan (CEO, Gnowit Inc.). (ACL Anthology, aclweb.org). Similar information-retrieval technologies are widely used for competitive intelligence, policy monitoring, and media analysis. == White paper == Gnowit has published a practical guide, Automated Government Information Monitoring, which outlines how GR and regulatory teams can design a monitoring and briefing workflow and describes Gnowit's automation features and export options (PDF, email, dashboards, CSV/JSON/XML/API).

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  Frictionless sharing

  Frictionless sharing refers to the transparent or automatic dissemination of user activity across social media platforms, typically without requiring explicit action from the user each time content is shared. The concept gained prominence in 2011 after Mark Zuckerberg announced a series of new features for Facebook at the F8 developers conference, framing the changes as enabling “real-time serendipity in a friction-less experience.” == History and concept == Before 2011, the term “frictionless sharing” was occasionally used in academic and technical contexts to describe sharing of resources with minimal effort, such as through social bookmarking or Creative Commons licensing to reduce barriers to reuse of research data. The concept took on a broader cultural meaning when Facebook introduced its Timeline interface and new “social apps” in 2011. These features enabled third-party applications to automatically publish user activity to the platform—effectively shifting sharing from a deliberate act to a passive process. For example, integrating music streaming service Spotify meant that any song a user listened to could automatically appear in a Facebook “Ticker,” allowing friends to see the activity and click through to play the song themselves. == Zuckerberg’s vision == Zuckerberg articulated a vision of a Web in which sharing occurs by default rather than by choice: “You read, you watch, you listen, you buy—and everyone you know will hear all about it on Facebook.” This “frictionless” model assumes ongoing consent after an initial opt-in. Once users connect an app to their profile, any future activity with that app may be automatically shared. This shift from intentional posting to ambient sharing represented a significant evolution in how personal data is distributed online. == Criticism and debate == Many commentators and users have raised concerns about frictionless sharing. While some criticism centers on online privacy, others focus on how automatic updates can flood news feeds and erode the social value of sharing. Critics argue that when sharing becomes automatic, it dilutes the personal curation that makes social media exchanges meaningful. According to Slate, this approach risks “killing taste,” because users typically choose to share only select content they find worth highlighting, rather than everything they consume. AL.com similarly observed that the frictionless model encourages over-sharing, overwhelming both users and their networks with minor or trivial activities. For example, integrating multiple platforms—such as Twitter, Foursquare, Pinterest, Spotify, and others—can create an incessant stream of updates that some users may find intrusive or irritating. This can lead to what critics describe as “narcissistic” or noisy timelines, potentially undermining the “social” nature of social media. == Business model and data implications == For Facebook, frictionless sharing offers clear business advantages. More frequent and detailed sharing provides valuable data that can be used to refine targeted advertising and personalize content delivery. The model also encourages users to spend more time on the platform, reinforcing its position as a central hub of online social activity. Other technology companies have experimented with similar approaches. Google has introduced forms of cross-platform integration that facilitate automatic activity sharing, though with a more explicit opt-in structure compared to Facebook. This approach has been described as “friction with consent,” allowing users to manually enable or disable integrations on a per-service basis.

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