AI Content Understanding

AI Content Understanding — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Braina

    Braina

    Braina is a virtual assistant and speech-to-text dictation application for Microsoft Windows developed by Brainasoft. Braina uses natural language interface, speech synthesis, and speech recognition technology to interact with its users and allows them to use natural language sentences to perform various tasks on a computer. The name Braina is a short form of "Brain Artificial". Braina is marketed as a Microsoft Copilot alternative. It provides a voice interface for several locally run and cloud large language models, including the latest LLMs from providers such as OpenAI, Anthropic, Google, xAI, Meta, Mistral, etc; while improving data privacy. Braina also allows responses from its in-house large language models like Braina Swift and Braina Pinnacle. It has an "Artificial Brain" feature that provides persistent memory support for supported LLMs. == Features == Braina provides is able to carry out various tasks on a computer, including automation. Braina can take commands inputted through typing or through dictation to store reminders, find information online, perform mathematical operations, open files, generate images from text, transcribe speech, and control open windows or programs. Braina adapts to user behavior over time with a goal of better anticipating needs. === Speech-to-text dictation === Braina Pro can type spoken words into an active window at the location of a user's cursor. Its speech recognition technology supports more than 100 languages and dialects and is able to isolate the recognition of a user's voice from disturbing environmental factors such as background noise, other human voices, or external devices. Braina can also be taught to dictate uncommon legal, medical, and scientific terms. Users can also teach Braina uncommon names and vocabulary. Users can edit or correct dictated text without using a keyboard or mouse by giving built-in voice commands. === Text-to-speech === Braina can read aloud selected texts, such as e-books. === Custom commands and automation === Braina can automate computer tasks. It lets users create custom voice commands to perform tasks such as opening files, programs, websites, or emails, as well as executing keyboard or mouse macros. === Transcription === Braina can transcribe media file formats such as WAV, MP3, and MP4 into text. === Notes and reminders === Braina can store and recall notes and reminders. These can include scheduled or unscheduled commands, checklist items, alarms, chat conversations, memos, website snippets, bookmarks, contacts. === Image and Video generation === Braina can generate AI images and videos from text and image inputs using generative cloud AI models. These include Black Forest Labs' FLUX.2, Google's Veo, Imagen, and Nano Banana Pro, Kuaishou's Kling, Alibaba's Wan, ByteDance's Seedance and Seedream, MiniMax's Hailuo, OpenAI's GPT Image, and Tongyi Lab's Z Image Turbo. == Platforms == In addition to the desktop version for Windows operating systems, Braina is also available for the iOS and Android operating systems. The mobile version of Braina has a feature allowing remote management of a Windows PC connected via Wi-Fi. == Distributions == Braina is distributed in multiple modes. These include Braina Lite, a freeware version with limitations, and premium versions Braina Pro, Pro Plus, and Pro Ultra. Some additional features in the Pro version include dictation, custom vocabulary, video transcription, automation, custom voice commands, and persistent LLM memory. == Reception == TechRadar has consistently listed Braina as one of the best dictation and virtual assistant apps between 2015 and 2024.

    Read more →
  • Social television

    Social television

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

    Read more →
  • Social network game

    Social network game

    A social network game (sometimes simply referred to as a social media game, social gaming, or online social game) is a type of online game that is played through social networks or social media. They typically feature gamification systems with multiplayer gameplay mechanics. Social network games were originally implemented as browser games. As mobile gaming took off, the games moved to mobile as well. While they share many aspects of traditional video games, social network games often employ additional ones that make them distinct. Traditionally they are oriented to be social games and casual games. The first cross-platform "Facebook-to-Mobile" social network game was developed in 2011 by a Finnish company Star Arcade. Social network games are amongst the most popular games played in the world, with several products with tens of millions of players. (Lil) Green Patch, Happy Farm, and Mob Wars were some of the first successful games of this genre. FarmVille, Mafia Wars, Kantai Collection, and The Sims Social are more recent examples of popular social network game. Major companies that made or published social network games include Zynga, Wooga and Bigpoint Games. == Demographics == As of 2010, it was reported that 55 percent of the social network gaming demographic in the United States consisted of women while in the United Kingdom, women made up nearly 60 percent of the demographic. In addition, most social gamers were around the 30 to 59 age range, with the average social gamer being 43 years old. Social gaming may appeal more to the older demographic because it is free, easier to advance through in a short period, does not involve as much violence as traditional video games, and is easier to grasp. Other games target certain demographics that use social media, such as Pot Farm creating a community by involving elements of cannabis subculture in its gameplay. == Technology and platforms == A social network video game is a client-server application. The client in the web era was implemented with a mix of web technologies like Flash, HTML5, PHP and JavaScript. When mobile games moved to mobile, social game front ends were developed using mobile platform technologies like Java, Objective-C, Swift and C++. The back end was a mix of programming languages and systems, including PHP, Ruby, C++ and go. Where social network video games diverged from traditional game development was the combination of real-time analytics to continuously optimize game mechanics to drive growth, revenue, and engagement. == Distinct features == The following table outlines common characteristics of social games, mentioned by Björk at the 2010 GCO Games Convention Online: A social network game may employ any of the following features: asynchronous gameplay, which allows rules to be resolved without needing players to play at the same time. gamification, which video game mechanics such as achievements and points are applied to those experienced when playing games in order to motivate and engage users. community, as one of the most distinct features of social video games is in leveraging the player's social network. Quests or game goals may only be possible if a player "shares" with friends connected by the social network hosting the game or gets them to play, as well as "neighbors" or "allies". a lack of victory conditions: there are generally no victory conditions since most developers count on users playing their games often. The game never ends and no one is ever declared winner. Instead, many casual games have "quests" or "missions" for players to complete. This is not true for board game-like social games, such as Scrabble. a virtual currency which players usually must purchase with real-world money. With the in-game currency, players can buy upgrades that would otherwise take much longer to earn through in-game achievements. In many cases, some upgrades are only available with the virtual currency. == Engagement strategies == Since social network games are often less challenging than console games and they have relatively shorter game play, they use different techniques to stretch game play and tools to retain users. Continuous goals: The games assign specific goals for users to achieve. As they advance in the game, the goals become more challenging and time-consuming. They also provide frequent feedback with their performance. Every action will translate towards a certain goal that will be used to attain higher gaming capitals. Gaming capitals: Players are encouraged to earn different badges, trophies, and accolades that indicate their progress and accomplishments. Some achievements are unlocked just by advancing in the game while others may significantly alter the rationale behind the game and require extensive investment from players. The ways of gaining gaming capital are not limited to playing games but the games-related productive activities that are appreciated in the player's social circle too. By accumulating gaming capitals, they provide an intrinsic benefit to gamers as there is an avenue to boost their accomplishment and showcase their expertise of the game. The achievements are visible to their network of friends. Gaming capitals are a way for developers to increase replay value provides extended play time, and players get more value from the game. Motivation for collecting gaming capitals: 1. Legitimization: refers to society's willingness to approve or condone certain behavior. Collecting is about channeling one's materialistic desires into more meaningful pursuits. Game achievements serve a similar purpose, allowing players to justify the hours spent playing the game. 2. Self-extension: Gathering and controlling meaningful objects or experiences can work to gain one an improved sense of self. The collector's goal to complete a collection is symbolically about completing the self too. Events timed to real world: Popular games such as Dragon City and Wild Ones require users to wait a certain time period before their "energy bars" replenish. Without energy, they are unable to conduct any form of action. Gamers are forced to wait and return after their energy replenishes to continue playing. == Monetization == Social network games frequently monetize based on virtual good transactions, but other games are emerging that utilize newer economic models. === Virtual goods === Gamers will be able to purchase in game items like power-ups, avatar accessories, or decorative items users purchase within the game itself. This is realized by monetize products that do not technically exist. Virtual goods account for over 90% of all revenue generated by the world's top social game developers. Designers optimize user experience through additional gameplay, missions, and quests, without having to worry about overhead or unused stock. == Advertising == The following are common ways of advertising in social network games: === Banner advertisements === As banner ads within social networks tend to be where ad response is low, they tend to be priced at bottom-of-the-barrel CPMs of around $2. However, because social games generate so many page views, they are the biggest part of advertising revenue for the social gaming industry. === Video ads === Videos are the ad format with the most revenue per view. They tend to be higher-priced, either by CPMs ($35+ CPM in social games) or cost-per-completed-view. According to studies, video ads result in highest brand recall thus a good return on investment for advertisers. Video ads are shown either in in-game interstitials (e.g. when the game is loading a new screen) or through incentive-based advertising, i.e. you will get either an in-game reward or Facebook credits for watching an advertisement. === Product placement === A brand or product will be injected in a game in some way. Due to the variety of ways in which product placement can be accomplished in any media, and because the category is nascent, this category is not standardized at all, but some examples include branded in-game goods or even in-game quests. For example, in a game where you run a restaurant, you might be asked to collect ingredients to make a Starbucks Frappuccino, and receive in-game rewards for doing so. As these product placement deals are non-standard, they are largely charged with a production fee, which can be $350,000 to $750,000 depending on the type of placement and the popularity of the game. === Lead generation offers === Another form of advertising that is prevalent in many social games are lead generation offers. In this form of advertising, companies, usually from different industries, aim to convince players to sign up for their goods or services and in exchange, players will receive virtual gifts or advance in the game as a reward. === Sponsorship === ==== White label games ==== Applications that are built once, then individualized and licensed again and again. Developer can create a quality app focused on fun while leaving the edge

    Read more →
  • Social media stock bubble

    Social media stock bubble

    The social media bubble is a hypothesis stating that there was a speculative boom and bust phenomenon in the field of social media in the 2010s, particularly in the United States. The Wall Street Journal defined a bubble as stocks "priced above a level that can be justified by economic fundamentals," but this bubble includes social media. Social networking services (SNS) have seen huge growth since 2006, but some investors believed around 2014-2015, that the "bubble" was similar to the dot-com bubble of the late 1990s and early 2000s. In 2015, Mark Cuban, owner of the Dallas Mavericks NBA team and star of the TV show, Shark Tank, sounded an alarm on his personal blog over the social media bubble, calling it worse than the tech bubble in 2000 due to the lack of liquidity in social media stocks. A year prior, however, Cuban told CNBC that he did not believe social media stocks were on the verge of a bubble. In a letter to investors in 2014, David Einhorn, who runs the hedge-fund Greenlight Capital, wrote that "we are witnessing our second tech bubble in 15 years." He went on to write, "What is uncertain is how much further the bubble can expand, and what might pop it." Einhorn cited several factors supporting the existence an over-exuberance including "rejection of conventional valuation methods" and "huge first day IPO pops for companies that have done little more than use the right buzzwords and attract the right venture capital." Since those claims, services like Facebook, Twitter, Instagram, and Snapchat have grown to become multi-billion-dollar corporations generating enormous revenues, though some continue to lose money. == History of social networking services == Social networking services have grown and evolved with time since the launch of SixDegrees.com in 1997. Cutting edge at its time, SixDegrees.com allowed users to create a profile, invite friends, and connect within its platform. At its peak, SixDegrees.com had more than 3.5 million users. Between 1997 and 2001 more social sites aimed at allowing users to connect with others for personal, professional, or dating reasons. Friendster and MySpace were next to enter the social SNS arena, followed by Facebook in 2004. Even though MySpace had a following of more than 300 million users, it could not compete with Facebook, which now has overtaken the social networking world. However, as development of SNS started to emerge, a market saturation began to take effect. Some classrooms have begun to incorporate technology in daily learning as well as social channels specific to student's course work. Traditional social media sites are used, as are educational oriented sites such as ShowMe and Educreations Interactive Whiteboard. == Controversies == While SNS continue to play an influential role in helping people form real-world connections via the Internet, renewed concerns over the social media bubble have surfaced due to recent controversies. These threats include growing concerns about breaches in data, the rise of bot accounts, and the sharing of fake news on SNS platforms. There are also concerns that big data figures associated with these SNS are inflated or fake, as well as worries about the role the platforms played in national elections (see Russian interference in the 2016 United States elections). These issues have resulted in a lack of trust among the sites' users.

    Read more →
  • Quantexa

    Quantexa

    Quantexa is a UK-based software company that develops artificial intelligence-based applications for data analytics and decision-making. The company was founded in 2016 and is headquartered in London, with operations in North America, Europe, and the Asia-Pacific region. As of 2025, Quantexa reported a valuation of $2.6 billion and provides services to organizations in over 70 countries. Investors include Warburg Pincus, HSBC, and the Ontario Teachers’ Pension Plan. == History == Quantexa was founded in London in 2016 by several co-founders, including Jamie Hutton, Richard Seewald, Imam Hoque, Felix Hoddinott, and Vishal Marria, who also serves as the company's chief executive officer. The company was established to develop tools intended to address limitations in traditional data analysis methods, particularly those related to identifying hidden connections across large datasets. The name "Quantexa" is derived from the company's focus on quantitative methods and data analysis. In 2023, Quantexa acquired Dublin-based AI firm Aylien. In April 2023, the company completed a Series E funding round, raising $129 million at a valuation of approximately $1.8 billion, marking its entry into "unicorn" status. In October 2024, the company reported annual recurring revenue (ARR) exceeding $100 million. In early 2025, Quantexa participated in the World Economic Forum's Unicorn Program, which supports high-growth technology companies. In March 2025, Quantexa completed a Series F funding round of $175 million, led by Teachers' Venture Growth, the venture arm of the Ontario Teachers' Pension Plan. That August, the company was reported to be considering a 2026 IPO. The company formed a partnership with Zurich in October 2025, the first insurer to add its AI-based Decision Intelligence platform to enhance fraud detection.

    Read more →
  • Customer data management

    Customer data management

    Customer data management (CDM) is the ways in which businesses keep track of their customer information and survey their customer base in order to obtain feedback. CDM includes a range of software or cloud computing applications designed to give large organizations rapid and efficient access to customer data. Surveys and data can be centrally located and widely accessible within a company, as opposed to being warehoused in separate departments. CDM encompasses the collection, analysis, organizing, reporting and sharing of customer information throughout an organization. Businesses need a thorough understanding of their customers’ needs if they are to retain and increase their customer base. Efficient CDM solutions provide companies with the ability to deal instantly with customer issues and obtain immediate feedback. As a result, customer retention and customer satisfaction can show marked improvement. According to a study by Aberdeen Group, "above-average and best-in-class companies... attain greater than 20% annual improvement in retention rates, revenues, data accuracy and partner/customer satisfaction rates." == Customer data management and cloud computing == Cloud computing offers an attractive choice for CDM in many companies due to its accessibility and cost-effectiveness. Businesses can decide who, within their company, should have the ability to create, adjust, analyze or share customer information. In December 2010, 52% of Information Technology (IT) professionals worldwide were deploying, or planning to deploy, cloud computing; this percentage is far higher in many countries. == Background == Customer data management, as a term, was coined in the 1990s, pre-dating the alternative term enterprise feedback management (EFM). CDM was introduced as a software solution that would replace earlier disc-based or paper-based surveys and spreadsheet data. Initially, CDM solutions were marketed to businesses as software, which were specific to one company, and often to one department within that company. This was superseded by application service providers (ASPs) where software was hosted for end user organizations, thus avoiding the necessity for IT professionals to deploy and support software. However, ASPs with their single-tenancy architecture were, in turn, superseded by software as a service (SaaS), engineered for multi-tenancy. By 2007 SaaS applications, giving businesses on-demand access to their customer information, were rapidly gaining popularity compared with ASPs. Cloud computing now includes SaaS and many prominent CDM providers offer cloud-based applications to their clients. In recent years, there has been a push away from the term EFM, with many of those working in this area advocating the slightly updated use of CDM. The return to the term CDM is largely based on the greater need for clarity around the solutions offered by companies, and on the desire to retire terminology veering on techno-jargon that customers may have a hard time understanding.

    Read more →
  • Backdoor (computing)

    Backdoor (computing)

    A backdoor is a typically covert method of bypassing normal authentication or encryption in a computer, product, embedded device (e.g. a home router), or its embodiment (e.g. part of a cryptosystem, algorithm, chipset, or even a "homunculus computer"—a tiny computer-within-a-computer such as that found in Intel's AMT technology). Backdoors are most often used for securing remote access to a computer, or obtaining access to plaintext in cryptosystems. From there it may be used to gain access to privileged information like passwords, corrupt or delete data on hard drives, or transfer information within compromised networks. In the United States, the 1994 Communications Assistance for Law Enforcement Act forces internet providers to provide backdoors for government authorities. In 2024, the U.S. government realized that China had been tapping communications in the U.S. using that infrastructure for months, or perhaps longer; China recorded presidential candidate campaign office phone calls—including employees of the then-vice president of the nation, and of the candidates themselves. A backdoor may take the form of a hidden part of a program, a separate program (e.g. Back Orifice may subvert the system through a rootkit), code in the firmware of the hardware, or parts of an operating system such as Windows, for example, device drivers. Trojan horses can be used to create vulnerabilities in a device. A Trojan horse may appear to be an entirely legitimate program, but when executed, it triggers an activity that may install a backdoor. Although some are secretly installed, other backdoors are deliberate and widely known. These kinds of backdoors have "legitimate" uses such as providing the manufacturer with a way to restore user passwords. Many systems that store information within the cloud fail to create accurate security measures. If many systems are connected within the cloud, hackers can gain access to all other platforms through the most vulnerable system. Default passwords (or other default credentials) can function as backdoors if they are not changed by the user. Some debugging features can also act as backdoors if they are not removed in the release version. In 1993, the United States government attempted to deploy an encryption system, the Clipper chip, with an explicit backdoor for law enforcement and national security access. The chip was unsuccessful. Recent proposals to counter backdoors include creating a database of backdoors' triggers and then using neural networks to detect them. == Overview == The threat of backdoors surfaced when multiuser and networked operating systems became widely adopted. Petersen and Turn discussed computer subversion in a paper published in the proceedings of the 1967 AFIPS Conference. They noted a class of active infiltration attacks that use "trapdoor" entry points into the system to bypass security facilities and permit direct access to data. The use of the word trapdoor here clearly coincides with more recent definitions of a backdoor. However, since the advent of public key cryptography the term trapdoor has acquired a different meaning (see: Trapdoor function), and thus the term "backdoor" is now preferred, only after the term trapdoor went out of use. More generally, such security breaches were discussed at length in a RAND Corporation task force report published under DARPA sponsorship by J.P. Anderson and D.J. Edwards in 1970. While initially targeting the computer vision domain, backdoor attacks have expanded to encompass various other domains, including text, audio, ML-based computer-aided design, and ML-based wireless signal classification. Additionally, vulnerabilities in backdoors have been demonstrated in deep generative models, reinforcement learning (e.g., AI GO), and deep graph models. These broad-ranging potential risks have prompted concerns from national security agencies regarding their potentially disastrous consequences. A backdoor in a login system might take the form of a hard coded user and password combination which gives access to the system. An example of this sort of backdoor was used as a plot device in the 1983 film WarGames, in which the architect of the "WOPR" computer system had inserted a hardcoded password-less account which gave the user access to the system, and to undocumented parts of the system (in particular, a video game-like simulation mode and direct interaction with the artificial intelligence). Although the number of backdoors in systems using proprietary software (software whose source code is not publicly available) is not widely credited, they are nevertheless frequently exposed. Programmers have even succeeded in secretly installing large amounts of benign code as Easter eggs in programs, although such cases may involve official forbearance, if not actual permission. == Examples == === Worms === Many computer worms, such as Sobig and Mydoom, install a backdoor on the affected computer (generally a PC on broadband running Microsoft Windows and Microsoft Outlook). Such backdoors appear to be installed so that spammers can send junk e-mail from the infected machines. Others, such as the Sony/BMG rootkit, placed secretly on millions of music CDs through late 2005, are intended as DRM measures—and, in that case, as data-gathering agents, since both surreptitious programs they installed routinely contacted central servers. A sophisticated attempt to plant a backdoor in the Linux kernel, exposed in November 2003, added a small and subtle code change by subverting the revision control system. In this case, a two-line change appeared to check root access permissions of a caller to the sys_wait4 function, but because it used assignment = instead of equality checking ==, it actually granted permissions to the system. This difference is easily overlooked, and could even be interpreted as an accidental typographical error, rather than an intentional attack. In January 2014, a backdoor was discovered in certain Samsung Android products, like the Galaxy devices. The Samsung proprietary Android versions are fitted with a backdoor that provides remote access to the data stored on the device. In particular, the Samsung Android software that is in charge of handling the communications with the modem, using the Samsung IPC protocol, implements a class of requests known as remote file server (RFS) commands, that allows the backdoor operator to perform via modem remote I/O operations on the device hard disk or other storage. As the modem is running Samsung proprietary Android software, it is likely that it offers over-the-air remote control that could then be used to issue the RFS commands and thus to access the file system on the device. === Object code backdoors === Harder to detect backdoors involve modifying object code, rather than source code—object code is much harder to inspect, as it is designed to be machine-readable, not human-readable. These backdoors can be inserted either directly in the on-disk object code, or inserted at some point during compilation, assembly linking, or loading—in the latter case the backdoor never appears on disk, only in memory. Object code backdoors are difficult to detect by inspection of the object code, but are easily detected by simply checking for changes (differences), notably in length or in checksum, and in some cases can be detected or analyzed by disassembling the object code. Further, object code backdoors can be removed (assuming source code is available) by simply recompiling from source on a trusted system. Thus for such backdoors to avoid detection, all extant copies of a binary must be subverted, and any validation checksums must also be compromised, and source must be unavailable, to prevent recompilation. Alternatively, these other tools (length checks, diff, checksumming, disassemblers) can themselves be compromised to conceal the backdoor, for example detecting that the subverted binary is being checksummed and returning the expected value, not the actual value. To conceal these further subversions, the tools must also conceal the changes in themselves—for example, a subverted checksummer must also detect if it is checksumming itself (or other subverted tools) and return false values. This leads to extensive changes in the system and tools being needed to conceal a single change. As object code can be regenerated by recompiling (reassembling, relinking) the original source code, making a persistent object code backdoor (without modifying source code) requires subverting the compiler itself—so that when it detects that it is compiling the program under attack it inserts the backdoor—or alternatively the assembler, linker, or loader. As this requires subverting the compiler, this in turn can be fixed by recompiling the compiler, removing the backdoor insertion code. This defense can in turn be subverted by putting a source meta-backdoor in the compiler, so that when it detects that it is compiling itself

    Read more →
  • G.9972

    G.9972

    G.9972 (also known as G.cx) is a Recommendation developed by ITU-T that specifies a coexistence mechanism for networking transceivers capable of operating over electrical power line wiring. It allows G.hn devices to coexist with other devices implementing G.9972 and operating on the same power line wiring. G.9972 received consent during the meeting of ITU-T Study Group 15, on October 9, 2009, and final approval on June 11, 2010. G.9972 specifies two mechanisms for coexistence between G.hn home networks and broadband over power lines (BPL) Internet access networks: Frequency-division multiplexing (FDM), in which the available spectrum is divided into two parts: frequencies below 10 or 14 MHz (specific value can be selected by the access network) are reserved for the access network, while frequencies above them are reserved for the in-home network. Time-division multiplexing (TDM), in which the available channel time is split equally between both networks. 50% of time slots are allocated for the access network, and 50% are allocated to the in-home network.

    Read more →
  • Flutter (software)

    Flutter (software)

    Flutter is an open-source UI software development kit created by Google. It can be used to develop cross platform applications from a single codebase for the web, Fuchsia, Android, iOS, Linux, macOS, and Windows. First described in 2015, Flutter was released in May 2017. Flutter is used internally by Google in apps such as Google Pay and Google Earth as well as by other software developers including ByteDance and Alibaba. Flutter ships applications with its own rendering engine which directly outputs pixel data to the screen. This is in contrast to many other UI frameworks that rely on the target platform to provide a rendering engine, such as native Android apps which rely on the device-level Android SDK or iOS SDK which use the target platform's built-in UI stack. Flutter's control of its rendering pipeline simplifies multi-platform support as identical UI code can be used for all target platforms.One of Flutter’s key features is hot reload, which allows developers to see code changes instantly without restarting the application. == Architecture == The basic component in a Flutter program is a "widget", which can in turn consist of other widgets. A widget describes the logic, interaction, and design of a UI element with an implementation similar to React. Unlike other cross-platform toolkits such as React Native and Xamarin which draw widgets using native platform components, Flutter renders widgets itself on a per-pixel basis. Flutter has two types of widgets: stateless and stateful. Stateless widgets only update if their inputs change, meaning they otherwise won't need to be rebuilt when other elements of the screen change, while stateful widgets can call the setState() method to update an internal state and redraw. Although widgets are the primary method of constructing Flutter applications, they can also be bypassed in favor of directly drawing on a canvas. This feature has been occasionally used to implement game engines in Flutter. The Flutter framework contains two sets of widgets that conform to specific design languages: Material Design widgets implement Google's design language of the same name, and Cupertino widgets implement Apple's iOS Human interface guidelines. Flutter allows the developer to use either set of widgets on either platform. Developers can use Cupertino widgets on Android. Flutter apps are written in the Dart language. Release versions of Flutter apps on all platforms use ahead-of-time (AOT) compilation except for on the Web where code is transpiled to JavaScript or WebAssembly. Flutter inherits Dart's Pub package manager and software repository, which allows users to publish and use custom packages as well as Flutter-specific plugins. The Foundation library, written in Dart, provides basic classes and functions that are used to construct applications using Flutter, such as APIs to communicate with the engine. Flutter's engine, written primarily in C++, provides low-level rendering support using either Google's Skia graphics library or the custom "Impeller" graphics layer, which is enabled by default on iOS and Android API 29 and higher. The engine interfaces with platform-specific SDKs such as those provided by Android and iOS to implement features like accessibility, file and network I/O, native plugin support, etc. == History == The first version of Flutter was known as "Sky" and ran on the Android operating system. It was unveiled at the 2015 Dart developer summit with the stated intent of being able to render consistently at 120 frames per second. On December 4, 2018, Flutter 1.0 was released at the Flutter conference in London. On May 6, 2020, the Dart software development kit (SDK) version 2.8 and Flutter 1.17.0 were released, adding support for the Metal API. On March 3, 2021, Google released Flutter 2 during an online Flutter Engage event. It added a Canvas-based renderer for web in addition to the HTML-based renderer and early-access desktop application support for Windows, macOS, and Linux. It also shipped with Dart 2.0 which included support for null-safety. Null safety was initially optional as it was a breaking change and was made mandatory in Dart 3 released in 2023. On May 12, 2022, Flutter 3 and Dart 2.17 were released with support for all desktop platforms as stable. On October 27, 2024, a number of Flutter community developers announced Flock, a fork of Flutter intended to be easier to contribute to while still keeping in sync with all changes made in the upstream code base. In 2025, Google continued Flutter's evolution with enhanced modular architecture, foldable device support, and ARM IoT optimizations as outlined in the updated roadmap. === Major releases in Flutter === Prior to the Flutter 2.0 release in March of 2021, the Flutter framework was centered on mobile development. The developers of Flutter were primarily focused on the two main platforms, IOS and Android. Specifically, they wanted to deliver strong performance and improve access to native API and platform features and expand the widget system. With the release of Flutter 2.0, the framework moved beyond mobile and introduced support for the web platform. This marked a shift into a broader cross platform development environment. With this release, developers could produce applications for Web, Android and IOS from the same codebase. This release also brought the desktop platform closer to stable. There have been a number of improvements since then that have broadened platform support. They introduced enhancements to performance and workflow, redefined the developer’s toolkit, and added an improved rendering engine. "Flutter 2.10.0 release notes". docs.flutter.dev. Retrieved 2025-11-11.

    Read more →
  • Social media as a news source

    Social media as a news source

    Social media as a news source is defined as the use of online social media platforms such as Instagram, TikTok, and Facebook rather than the use of traditional media platforms like the newspaper or live TV to obtain news. Television had just begun to turn a nation of people who once listened to media content into watchers of media content between the 1950s and the 1980s when the popularity of social media had also begun creating a nation of media content creators. Almost half of Americans use social media as a news source, according to the Pew Research Center. As social media's role in news consumption grows, questions have emerged about its impact on knowledge, the formation of echo chambers, and the effectiveness of fact-checking efforts in combating misinformation. Social media platforms allow user-generated content and sharing content within one's own virtual network. Using social media as a news source allows users to engage with news in a variety of ways including: Consuming and discovering news Sharing or reposting news Posting one's own photos, videos, or reports of news (i.e., engage in citizen or participatory journalism) Commenting on news posts Using social media as a news source has become an increasingly popular way for people of all age groups to obtain current and important information. Just like many other new forms of technology there are going to be pros and cons. There are ways that social media positively affects the world of news and journalism but it is important to acknowledge that there are also ways in which social media has a negative effect on the news. With this accessibility, people now have more ways to consume false news, biased news, and even disturbing content. In 2019, the Pew Research Center created a poll that reported Americans are wary about the ways that social media sites share news and certain content. This wariness of accuracy grew as awareness that social media sites could be exploited by bad actors who concoct false narratives and fake news. == Relationship to traditional news sources == Unlike traditional news platforms such as newspapers and news shows, social media platforms allow people without professional journalistic backgrounds to create news and cover events that news agencies might not cover. Social media users may read a set of news that differs slightly from what newspaper editors prioritize in the print press. A 2019 study found that Facebook and Twitter users are more likely to share politics, public affairs, and visual media news. Typically social media users circulate more towards posting about negative news. A study of tweets found that while optimistic-sounding and neutral-sounding tweets were equally likely to express certainty or uncertainty, the pessimistic tweets were nearly twice as likely to appear certain of an outcome than uncertain. These results could imply that posts of a more pessimistic nature that are also written with an air of certainty are more likely to be shared or otherwise permeate groups on Twitter. A similar bias towards negativity has developed on Facebook, where internal memos revealed that an algorithm built to promote "meaningful social interaction" actually incentivized publishers to promote negative and sensational news. Biases towards negativity need to be considered when the utility of new media is addressed, as the potential for human opinion to overemphasize any particular news story is greater despite general improvement. In order to compete in this rapidly changing technological environment, there has been an upheaval of traditional news sources onto online spaces. The production and circulation of newspaper prints have continued to globally decline in accordance with the increasing presence of news outlets on social media. Prominent platforms such as Twitter and Facebook have been key in engaging users through the integration of journalistic news into their newsfeeds. This feature has now become a foundational part of these apps' interfaces. Social media incentivizes both legacy news brands and individual professional journalists to share their reporting and interact with audiences on social platforms to boost engagement. However, most people who consume news on social media report that accessing news is not their main motivation for being on social media, but rather, they see and consume news incidentally. Nonetheless, informational interviews reveal that these consumers rely on being informed through social media. Some news consumers attest that a news brand's participation in social media does not improve their trust in the brand and that more in-depth reporting and more transparency about biases would improve trust instead. == Use as a news source == Globally, data from 2020 shows that over 70% of adult participants from Kenya, South Africa, Chile, Bulgaria, Greece, and Argentina utilized social media for news while those from France, the UK, the Netherlands, Germany, and Japan were reportedly less than 40 percent. According to the Pew Research Center, 20% of adults in the United States in 2018 said they get their news from social media "often," compared to 16% who said they often get news from print newspapers, 26% who often get it from the radio, 33% who often get it from news websites, and 49% who often get it from TV. The same survey found that social media was the most popular way for American adults age 18–29 to get news, the second-to-last most popular way for Americans age 20–49 to get news, and the least popular way for American adults age 50-64 and 65+ to get the news. In 2019, the Pew Research Center found that over half of Americans (54%) either got their news "sometimes" or "often" from social media, and Facebook was the most popular social media site where American adults got their news. However, at least 50% off all respondents reported that the following were either a "very big problem" or a "moderately big problem" for getting news on social media: One-sided news (83%) Inaccurate news (81%) Censorship of the news (69%) Uncivil discussions about the news (69%) Harassment of journalists (57%) News organizations or personalities being banned (53%) Violent or disturbing news images or videos (51%) In a later survey from the same year, the Pew Research Center reported that 18% of American adults reported that the most common way they get news about politics and the election was from social media. Additional source information shows that from politics and the United States presidential election in 2016, the popularity of fake news had grown to global attention. With this information, the study explains that more than 60 percent of adults receive their news from social media, the most popular being Facebook. With the increase of fake news, and the large amount of adult participation on these social media sites, it made it much harder for those who were searching for news to find a source that they could find credible. Another study found that adult participants found their own friends on Facebook to be a more reliable source of information online compared to a professional news organization. Although, when news was posted by a news organization online, they were then found more reliable compared to when they are shared by their online friends. Showing that adult participants found that the news that was only posted on Facebook and social media was much more credible to them than compared to other forms of information spreading. The study further states that these outcomes have the potential explanation that the topic of the news article played a part in the ways they were affected. This could have affected the way adult participants interacted with the different news sources, such as their online friends compared to a news organization, prominently because depending on the story, they want to have the correct information about the news from the most credible source. === By young people === Social media platforms are some of the most easily accessible forms of news and with the growing generations, the technology is only going to grow. With that, the use of social media in younger generations is also going to grow alongside it. Technology in the hands of young kids can be a concern moving into the future. Globally, there is evidence that through social media, youth have become more directly involved in protests, social campaigns and generally, in the sharing of news across multiple platforms. The number of people who use social media platforms such as Twitter, Facebook, Instagram, or Snapchat as ways to seek information has increased significantly in recent years especially for people who are part of the younger generation.TikTok is a rapidly expanding platform that young adults can use to find news content on social media. TikTok is one of the sites that young adults and teens utilize to get news about trending themes and controversial topics. The younger generation accepts without hesitation the information that thei

    Read more →
  • Social recruiting

    Social recruiting

    Social recruiting (social hiring or social media recruitment) is recruiting candidates by using social platforms as talent databases or for advertising. Social recruiting uses social media profiles, blogs, and other Internet sites to find information on candidates. It also uses social media to advertise jobs either through HR vendors or through crowdsourcing where job seekers and others share job openings within their online social networks. Social recruiting's effectiveness and return on investment have been difficult to determine, since applicants do not usually apply through the social channels which first attracted them. In May 2013, Maximum Employment Marketing Group released the Social Recruitment Monitor, which ranks the reach, engagement, and interactivity of employers' social recruiting efforts around the world. == Social recruitment software == The social recruitment software market (a form of e-recruitment) is often included in the wider talent management software sector. Bersin & Associates valued the wider talent management market at over $2bn in 2007. Social recruitment increasingly sits at an intersection of a number of fast-moving areas including social networking, recruitment and now cloud computing. Additionally, mobile recruiting has become another hot topic, especially with the rise in tablet and smartphone usage. In 2012, there was a rise of tech companies using social recruiting applications to find and screen applicants. As more companies saw value in filling jobs by putting them on the social platforms where millions of people spend at least 37 minutes daily, there developed a much larger focus on social recruiting among the talent acquisition community. By mid-2013, many major enterprise companies such as Pepsi, Gap, AIG, and Oracle had begun effectively utilizing social recruiting software, making it clear that large corporations were open to automating or streamlining (and ultimately investing in) their social recruiting processes.

    Read more →
  • Why We Post

    Why We Post

    Why We Post is a research project funded by the European Research Council and launched in 2012 by Daniel Miller with the objective of examining the global impact of new social media. The study is based on ethnographic data collected through the course of 15 months in China, India, Turkey, Italy, United Kingdom, Trinidad, Chile and Brazil. The results of this project were released on 29 February 2016. This included the first three of eleven Open Access books (available via UCL Press), a five-week e-course (MOOC) on FutureLearn in English, also available in Chinese, Portuguese, Hindi, Tamil, Italian, Turkish, and Spanish on UCLeXtend. In addition a website containing key discoveries, stories and over 100 films is available in the same 8 languages.

    Read more →
  • Texture compression

    Texture compression

    Texture compression is a specialized form of image compression designed for storing texture maps in 3D computer graphics rendering systems. Unlike conventional image compression algorithms, texture compression algorithms are optimized for random access. Texture compression can be applied to reduce memory usage at runtime. Texture data is often the largest source of memory usage in a mobile application. == Tradeoffs == In their seminal paper on texture compression, Beers, Agrawala and Chaddha list four features that tend to differentiate texture compression from other image compression techniques. These features are: Decoding Speed It is highly desirable to be able to render directly from the compressed texture data and so, in order not to impact rendering performance, decompression must be fast. Random Access Since predicting the order that a renderer accesses texels would be difficult, any texture compression scheme must allow fast random access to decompressed texture data. This tends to rule out many better-known image compression schemes such as JPEG or run-length encoding. Compression Rate and Visual Quality In a rendering system, lossy compression can be more tolerable than for other use cases. Some texture compression libraries, such as crunch, allow the developer to flexibly trade off compression rate vs. visual quality, using methods such as rate–distortion optimization (RDO). Encoding Speed Texture compression is more tolerant of asymmetric encoding/decoding rates as the encoding process is often done only once during the application authoring process. Given the above, most texture compression algorithms involve some form of fixed-rate lossy vector quantization of small fixed-size blocks of pixels into small fixed-size blocks of coding bits, sometimes with additional extra pre-processing and post-processing steps. Block Truncation Coding is a very simple example of this family of algorithms. Because their data access patterns are well-defined, texture decompression may be executed on-the-fly during rendering as part of the overall graphics pipeline, reducing overall bandwidth and storage needs throughout the graphics system. As well as texture maps, texture compression may also be used to encode other kinds of rendering map, including bump maps and surface normal maps. Texture compression may also be used together with other forms of map processing such as mipmaps and anisotropic filtering. == Availability == Some examples of practical texture compression systems are S3 Texture Compression (S3TC), PVRTC, Ericsson Texture Compression (ETC) and Adaptive Scalable Texture Compression (ASTC); these may be supported by special function units in modern graphics processing units (GPUs). OpenGL and OpenGL ES, as implemented on many video accelerator cards and mobile GPUs, can support multiple common kinds of texture compression - generally through the use of vendor extensions. == Supercompression == A compressed-texture can be further compressed in what is called "supercompression". Fixed-rate texture compression formats are optimized for random access and are much less efficient compared to image formats such as PNG. By adding further compression, a programmer can reduce the efficiency gap. The extra layer can be decompressed by the CPU so that the GPU receives a normal compressed texture, or in newer methods, decompressed by the GPU itself. Supercompression saves the same amount of VRAM as regular texture compression, but saves more disk space and download size. == Neural Texture Compression == Random-Access Neural Compression of Material Textures (Neural Texture Compression) is a Nvidia's technology which enables two additional levels of detail (16× more texels, so four times higher resolution) while maintaining similar storage requirements as traditional texture compression methods. The key idea is compressing multiple material textures and their mipmap chains together, and using a small neural network, that is optimized for each material, to decompress them.

    Read more →
  • Internet Security Alliance

    Internet Security Alliance

    Internet Security Alliance (ISA) was founded in 2001 as a non-profit collaboration between Carnegie Mellon University's CyLab and Electronic Industries Alliance, a federation of trade associations. The Internet Security Alliance is focused on cyber security, acting as a forum for information sharing and leadership on information security, and lobbying for corporate security interests. == International operations == The Internet Security Alliance operates with a global membership to provide international security for its partners. The organization's membership includes companies located on four continents, and the Executive Committee always includes at least one non-U.S.-based company. The Internet Security Alliance believes that international communication is crucial for long-term greater information security, as it allows for a more realistic approach to addressing the many challenges faced by users of the Internet. == Publications == Published in 2009, The Financial Impact of Cyber Risk is the first known guidance document to attempt to approach the financial impact of cyber risks from the perspective of core business functions. It claims to provide guidance to CFOs and their colleagues responsible for legal issues, business operations and technology, privacy and compliance, risk assessment and insurance, and corporate communications.

    Read more →
  • Kruskal count

    Kruskal count

    The Kruskal count (also known as Kruskal's principle, Dynkin–Kruskal count, Dynkin's counting trick, Dynkin's card trick, coupling card trick or shift coupling) is a probabilistic concept originally demonstrated by the Russian mathematician Evgenii Borisovich Dynkin in the 1950s or 1960s discussing coupling effects and rediscovered as a card trick by the American mathematician Martin David Kruskal in the early 1970s as a side-product while working on another problem. It was published by Kruskal's friend Martin Gardner and magician Karl Fulves in 1975. This is related to a similar trick published by magician Alexander F. Kraus in 1957 as Sum total and later called Kraus principle. Besides uses as a card trick, the underlying phenomenon has applications in cryptography, code breaking, software tamper protection, code self-synchronization, control-flow resynchronization, design of variable-length codes and variable-length instruction sets, web navigation, object alignment, and others. == Card trick == The trick is performed with cards, but is more a magical-looking effect than a conventional magic trick. The magician has no access to the cards, which are manipulated by members of the audience. Thus sleight of hand is not possible. Rather the effect is based on the mathematical fact that the output of a Markov chain, under certain conditions, is typically independent of the input. A simplified version using the hands of a clock performed by David Copperfield is as follows. A volunteer picks a number from one to twelve and does not reveal it to the magician. The volunteer is instructed to start from 12 on the clock and move clockwise by a number of spaces equal to the number of letters that the chosen number has when spelled out. This is then repeated, moving by the number of letters in the new number. The output after three or more moves does not depend on the initially chosen number and therefore the magician can predict it.

    Read more →