AI Face Kissing Free Online

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Mobile Passport Control

Mobile Passport Control (MPC) is a mobile app that enables eligible travelers entering the United States to submit their passport information and customs declaration form to Customs and Border Protection via smartphone or tablet and go through the inspections process using an expedited lane. It is available to "U.S. citizens, U.S. lawful permanent residents, Canadian B1/B2 citizen visitors and returning Visa Waiver Program travelers with approved ESTA". The app is available on iOS and Android devices and is operational at 34 US airports, 14 international airports offering preclearance facilities, and 4 seaports. The use of Mobile Passport Control operations have increased threefold from 2016 to 2017. == History == Mobile Passport Control operations were launched in Atlanta at the Hartsfield-Jackson International Airport in 2016 and is now available at 34 U.S. airports, 14 international airports that offer preclearance and 4 U.S. cruise ports. The Mobile Passport app is authorized by CBP and sponsored by the Airports Council International-North America, Boeing, and the Port of Everglades. Airside Mobile, Inc. secured a Series A funding of $6 million in the fall of 2017. == How it works == During the customs process at the Federal Inspection Service (FIS) area of a U.S. airport, travelers arriving from international locations typically wait in long lines before presenting passports and paperwork and verbally answering questions made by CBP officials. Eligible travelers who have downloaded the Mobile Passport app can expedite this process by submitting information regarding their passport and trip details, and a newly-taken selfie, via their mobile device to CBP officials, then access an expedited line. Mobile Passport Control users will be required to show their physical passport(s) and briefly talk to a CBP officer. == Locations == === US airports === Atlanta (ATL) Baltimore (BWI) Boston (BOS) Charlotte (CLT) Chicago (ORD) Dallas/Ft Worth (DFW) Denver (DEN) Detroit (DTW) as of 7/2024 Ft. Lauderdale (FLL) Honolulu (HNL) Houston (HOU and IAH) Kansas City (MCI) Las Vegas (LAS) Los Angeles (LAX) Miami (MIA) Minneapolis (MSP) New York (JFK) Newark (EWR) Oakland (OAK) Orlando (MCO) Palm Beach (PBI) Philadelphia (PHL) Phoenix (PHX) Pittsburgh (PIT) Portland (PDX) Sacramento (SMF) San Diego (SAN) San Francisco (SFO) San Jose (SJC) San Juan (SJU) Seattle (SEA) Tampa (TPA) Washington Dulles (IAD) === International Preclearance locations === Abu Dhabi (AUH) Aruba (AUA) Bermuda (BDA) Calgary (YYC) Dublin (DUB) Edmonton (YEG) Halifax (YHZ) Montreal (YUL) Nassau (NAS) Ottawa (YOW) Shannon (SNN) Toronto (YYZ) Vancouver (YVR) Winnipeg (YWG) Sepinggan (BPN) === Seaports === Fort Lauderdale (PEV) Miami (MSE) San Juan (PUE) West Palm Beach (WPB)
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Digital journalism

Digital journalism, also known as netizen journalism or online journalism, is a contemporary form of journalism where editorial content is distributed via the Internet, as opposed to publishing via print or broadcast. What constitutes digital journalism is debated amongst scholars. However, the primary product of journalism, which is news and features on current affairs, is presented solely or in combination as text, audio, video, or some interactive forms like storytelling stories or newsgames and disseminated through digital media technology. Fewer barriers to entry, lowered distribution costs and diverse computer networking technologies have led to the widespread practice of digital journalism. It has democratized the flow of information that was previously controlled by traditional media including newspapers, magazines, radio and television. Most readers expect online journalists to be reliable and competent, but these journalists often fail to meet this standard because they have very short deadlines and do not have enough resources to produce decent work. Some have asserted that a greater degree of creativity can be exercised with digital journalism when compared to traditional journalism and traditional media. The digital aspect may be central to the journalistic message and remains, to some extent, within the creative control of the writer, editor and/or publisher. It has been acknowledged that reports of its growth have tended to be exaggerated. In fact, a 2019 Pew survey showed a 16% decline in the time spent on online news sites since 2016. In the United States, reports issued by the Federal Communications Commission (FCC) in 2011 and by the Government Accountability Office (GAO) and the Congressional Research Service (CRS) in 2023 found that increases in newsroom staffing at digital-native news websites from 2008 to 2020 were not offsetting cuts in newsroom staffing among newspapers (which numbered in the tens of thousands of jobs), and that newspapers and television (which had been seeing declining newsroom staffing alongside newspapers) still employed the majority of payrolled newsroom staff in the United States in 2022 while online-only news websites employed less than 10%. The GAO and CRS reports noted further that the reduction in subscription and advertising revenue for the U.S. newspaper industry from 2000 to 2020 that constituted the overwhelming majority of its inflation-adjusted total revenue was not being offset by digital circulation or online advertising despite almost two-thirds of U.S. advertising spending in total by 2020 being online. Also, while the FCC report noted that local television stations in the United States had become some of the largest providers of local news online, the FCC found in a 2021 working paper that inflation-adjusted advertising revenue for television stations fell nationally from 2010 to 2018. == Overview == Digital journalism flows as journalism flows and is difficult to pinpoint where it is and where it is going. In partnership with digital media, digital journalism uses facets of digital media to perform journalist tasks, for example, using the internet as a tool rather than a singular form of digital media. There is no absolute agreement as to what constitutes digital journalism. Mu Lin argues that, "Web and mobile platforms demand us to adopt a platform-free mindset for an all-inclusive production approach – create the [digital] contents first, then distribute via appropriate platforms." The repurposing of print content for an online audience is sufficient for some, while others require content created with the digital medium's unique features like hypertextuality. Fondevila Gascón adds multimedia and interactivity to complete the digital journalism essence. For Deuze, online journalism can be functionally differentiated from other kinds of journalism by its technological component which journalists have to consider when creating or displaying content. Digital journalistic work may range from purely editorial content like CNN (produced by professional journalists) online to public-connectivity websites like Slashdot (communication lacking formal barriers of entry). The difference of digital journalism from traditional journalism may be in its re-conceptualised role of the reporter in relation to audiences and news organizations. The expectations of society for instant information was important for the evolution of digital journalism. However, it is likely that the exact nature and roles of digital journalism will not be fully known for some time. Some researchers even argue that the free distribution of online content, online advertisement and the new way recipients use news could undermine the traditional business model of mass media distributors that is based on single-copy sales, subscriptions and the selling of advertisement space. == History == The first type of digital journalism, called teletext, was invented in the UK in 1970. Teletext is a system allowing viewers to choose which stories they wish to read and see it immediately. The information provided through teletext is brief and instant, similar to the information seen in digital journalism today. The information was broadcast between the frames of a television signal in what was called the vertical blanking interval or VBI. American journalist Hunter S. Thompson relied on early digital communication technology beginning by using a fax machine to report from the 1971 US presidential campaign trail as documented in his book Fear and Loathing on the Campaign Trail. After the invention of teletext was the invention of videotex, of which Prestel was the world's first system, launching commercially in 1979 with various British newspapers, such as the Financial Times lining up to deliver newspaper stories online through it. Videotex closed down in 1986 due to failing to meet end-user demand. American newspaper companies took notice of the new technology and created their own videotex systems, the largest and most ambitious being Viewtron, a service of Knight-Ridder launched in 1981. Others were Keycom in Chicago and Gateway in Los Angeles. All of them had closed by 1986. Next came computer Bulletin Board Systems. In the late 1980s and early 1990s, several smaller newspapers started online news services using BBS software and telephone modems. The first of these was the Albuquerque Tribune in 1989. Computer Gaming World in September 1992 broke the news of Electronic Arts' acquisition of Origin Systems on Prodigy, before its next issue went to press. Online news websites began to proliferate in the 1990s. An early adopter was The News & Observer in Raleigh, North Carolina which offered online news as Nando. Steve Yelvington wrote on the Poynter Institute website about Nando, owned by The N&O, by saying "Nando evolved into the first serious, professional news site on the World Wide Web". It originated in the early 1990s as "NandO Land". It is believed that a major increase in digital online journalism occurred around this time when the first commercial web browsers, Netscape Navigator (1994) and Internet Explorer (1995). By 1996, most news outlets had an online presence. Although journalistic content was repurposed from original text/video/audio sources without change in substance, it could be consumed in different ways because of its online form through toolbars, topically grouped content, and intertextual links. A twenty-four-hour news cycle and new ways of user-journalist interaction web boards were among the features unique to the digital format. Later, portals such as AOL and Yahoo! and their news aggregators (sites that collect and categorize links from news sources) led to news agencies such as The Associated Press to supplying digitally suited content for aggregation beyond the limit of what client news providers could use in the past. Also, Salon, was founded in 1995. In 2001, the American Journalism Review called Salon the Internet's "preeminent independent venue for journalism." In 2008, for the first time, more Americans reported getting their national and international news from the internet, rather than newspapers. Young people aged 18 to 29 now primarily get their news via the Internet, according to a Pew Research Center report. Audiences to news sites continued to grow due to the launch of new news sites, continued investment in news online by conventional news organizations, and the continued growth in internet audiences overall. Sixty-five percent of youth now primarily access the news online. Mainstream news sites are the most widespread form of online news media production. As of 2000, the vast majority of journalists in the Western world now use the internet regularly in their daily work. In addition to mainstream news sites, digital journalism is found in index and category sites (sites without much original content but multiple links to existing news sites), meta- and comment sites (sites about
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Account verification

Account verification is the process of verifying that a new or existing account is owned and operated by a specified real individual or organization. A number of websites, for example social media websites, offer account verification services. Verified accounts are often visually distinguished by check mark icons or badges next to the names of individuals or organizations. Account verification can enhance the quality of online services, mitigating sockpuppetry, bots, trolling, spam, vandalism, fake news, disinformation and election interference. == History == Account verification was introduced by Twitter in June 2009, initially as a feature for public figures and accounts of interest, individuals in "music, acting, fashion, government, politics, religion, journalism, media, sports, business and other key interest areas". A similar verification system was adopted by Google+ in 2011, Facebook page in October 2015 (Available in United States, Canada, United Kingdom, Australia and New Zealand) Facebook profile and Facebook page in 2018 (Available in Worldwide) Instagram in 2014, and Pinterest in 2015. On YouTube, users are able to submit a request for a verification badge once they obtain 100,000 or more subscribers. It also has an "official artist" badge for musicians and bands. In July 2016, Twitter announced that, beyond public figures, any individual would be able to apply for account verification. This was temporarily suspended in February 2018, following a backlash over the verification of one of the organisers of the far-right Unite the Right rally due to a perception that verification conveys "credibility" or "importance". In March 2018, during a live-stream on Periscope, Jack Dorsey, co-founder and CEO of Twitter, discussed the idea of allowing any individual to get a verified account. Twitter reopened account verification applications in May 2021 after revamping their account verification criteria. This time offering notability criteria for the account categories of government, companies, brands, and organizations, news organizations and journalists, entertainment, sports and activists, organizers, and other influential individuals. Instagram began allowing users to request verification in August 2018. In April 2018, Mark Zuckerberg, co-founder and CEO of Facebook, announced that purchasers of political or issue-based advertisements would be required to verify their identities and locations. He also indicated that Facebook would require individuals who manage large pages to be verified. In May 2018, Kent Walker, senior vice president of Google, announced that, in the United States, purchasers of political-leaning advertisements would need to verify their identities. In November 2022, Elon Musk included a blue verification check mark with a paid Twitter Blue monthly membership. Prior to Musk's acquisition of Twitter, Twitter offered this check mark at no charge to confirmed high profile users. On December 19, 2022, Twitter introduced two new check mark colors: gold for accounts from official businesses and organizations, and grey for accounts from governments or multilateral organizations. The type of check mark can be confirmed by visiting the profile page, then clicking or tapping on the check mark. == Techniques == === Identity verification services === Identity verification services are third-party solutions which can be used to ensure that a person provides information which is associated with the identity of a real person. Such services may verify the authenticity of identity documents such as drivers licenses or passports, called documentary verification, or may verify identity information against authoritative sources such as credit bureaus or government data, called nondocumentary verification. === Identity documents verification === The uploading of scanned or photographed identity documents is a practice in use, for example, at Facebook. According to Facebook, there are two reasons that a person would be asked to send a scan of or photograph of an ID to Facebook: to show account ownership and to confirm their name. In January 2018, Facebook purchased Confirm.io, a startup that was advancing technologies to verify the authenticity of identification documentation. === Biometric verification === === Behavioral verification === Behavioral verification is the computer-aided and automated detection and analysis of behaviors and patterns of behavior to verify accounts. Behaviors to detect include those of sockpuppets, bots, cyborgs, trolls, spammers, vandals, and sources and spreaders of fake news, disinformation and election interference. Behavioral verification processes can flag accounts as suspicious, exclude accounts from suspicion, or offer corroborating evidence for processes of account verification. === Bank account verification === Identity verification is required to establish bank accounts and other financial accounts in many jurisdictions. Verifying identity in the financial sector is often required by regulation such as Know Your Customer or Customer Identification Program. Accordingly, bank accounts can be of use as corroborating evidence when performing account verification. Bank account information can be provided when creating or verifying an account or when making a purchase. === Postal address verification === Postal address information can be provided when creating or verifying an account or when making and subsequently shipping a purchase. A hyperlink or code can be sent to a user by mail, recipients entering it on a website verifying their postal address. === Telephone number verification === A telephone number can be provided when creating or verifying an account or added to an account to obtain a set of features. During the process of verifying a telephone number, a confirmation code is sent to a phone number specified by a user, for example in an SMS message sent to a mobile phone. As the user receives the code sent, they can enter it on the website to confirm their receipt. === Email verification === An email account is often required to create an account. During this process, a confirmation hyperlink is sent in an email message to an email address specified by a person. The email recipient is instructed in the email message to navigate to the provided confirmation hyperlink if and only if they are the person creating an account. The act of navigating to the hyperlink confirms receipt of the email by the person. The added value of an email account for purposes of account verification depends upon the process of account verification performed by the specific email service provider. === Multi-factor verification === Multi-factor account verification is account verification which simultaneously utilizes a number of techniques. === Multi-party verification === The processes of account verification utilized by multiple service providers can corroborate one another. OpenID Connect includes a user information protocol which can be used to link multiple accounts, corroborating user information. == Account verification and good standing == On some services, account verification is synonymous with good standing. Twitter reserves the right to remove account verification from users' accounts at any time without notice. Reasons for removal may reflect behaviors on and off Twitter and include: promoting hate and/or violence against, or directly attacking or threatening other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or disease; supporting organizations or individuals that promote the above; inciting or engaging in the harassment of others; violence and dangerous behavior; directly or indirectly threatening or encouraging any form of physical violence against an individual or any group of people, including threatening or promoting terrorism; violent, gruesome, shocking, or disturbing imagery; self-harm, suicide; and engaging in other activity on Twitter that violates the Twitter Rules. In April 2023, Blue ticks were removed from all Twitter accounts that had not subscribed to Twitter Blue.
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Festival of International Virtual & Augmented Reality Stories

Festival of International Virtual & Augmented Reality Stories (FIVARS) is a Canadian media festival for story-driven works using extended reality (XR) and immersive media, including virtual reality, augmented reality, WebXR, live VR performance, projection mapping and spatialized audio. Founded in Toronto in 2015, it has been described as Canada's first dedicated virtual and augmented reality stories festival, the first Canadian festival of its kind, and Canada's original festival dedicated to immersive storytelling. FIVARS has described itself as "the original and longest-running festival wholly dedicated to Virtual and Augmented Reality Stories", while third-party XR coverage has called it one of the longest-running events dedicated to immersive content. FIVARS is produced by Constant Change Media Group, Inc., with its partner event VRTO. == History == FIVARS began in 2015, with preview screenings at the Camp Wavelength music festival on Toronto Island and an inaugural festival held in Toronto in September 2015. Contemporary coverage described the first edition as a virtual reality film festival held at UG3 Live in Toronto. The festival continued with a second edition in 2016. L'Express described the 2016 festival as presenting Canadian and international interactive works in virtual and augmented reality narrative forms. FIVARS's 2016 festival was also listed in a York University Future Cinema course page as a public event students could attend. In 2017, the third annual FIVARS festival was held at the House of VR in Toronto. In 2018, the festival was held at the Matador Ballroom, which NOW Magazine reported was reopening for FIVARS from September 14 to 16. The festival's own history states that the 2018 edition included 36 works from 12 countries and that Stephanie Greenall took over as co-producer that year. In 2019, FIVARS moved to the Toronto Media Arts Centre for its fifth anniversary and listed official selections in passive and interactive immersive-experience categories. The festival also held talks and panels at the Toronto Media Arts Centre. During the COVID-19 pandemic, FIVARS moved part of its programming online. In 2020, Voices of VR reported that Malicki-Sanchez and WebXR developer James Baicoianu used JanusXR code to create a platform for presenting 360-degree video through the web. The festival's history states that its 2020 online festival included 39 selections from 16 countries and was produced by Malicki-Sanchez and Greenall. In 2021, FIVARS introduced a dual-event structure with FIVARS in FEB and FIVARS in FALL. The fall 2021 edition used a hybrid format, with an in-person component in West Hollywood from October 15 to 17 and an online WebXR component from October 22 to November 2. In 2022, FIVARS held hybrid programming with pop-up viewing locations in Los Angeles and Toronto. The fall 2022 edition was listed by blogTO as the festival's tenth edition, with an in-person component at Stackt - an outdoor arts park built from shipping containers in Toronto and online programming. The 2023 festival was presented as a hybrid exhibition of 65 immersive stories, with an in-person Toronto component and an online component. The FIVARS Online Festival was later listed among the Innovator of the Year nominees for the 2024 Poly Awards. FIVARS stated that the nominees for that recognition were producer and designer Keram Malicki-Sanchez and developer James Baicoianu. The 2024 edition was listed as FIVARS 2024 (Toronto + Online), with an in-person Toronto event from October 3 to 8 and an online component beginning October 10. The festival also published a 2024 official selections list covering virtual reality, augmented reality, spherical video, spatial web and related immersive formats. In 2025, FIVARS and VRTO were held together at OCAD University. The 2026 edition is scheduled for June 15 to 19, 2026, at OCAD University in Toronto, with OCAD University as presenting sponsor and first-time venue host. FIVARS has featured official selections from more than forty countries across six continents. == Organization == FIVARS was founded in 2015 by Keram Malicki-Sánchez. Joseph Ellsworth was the festival's original technical director and helped operate FIVARS during its early years. Malicki-Sánchez remains executive director and festival director. Jessy Blaze joined Malicki-Sánchez as co-producer in 2016 and served until Stephanie Greenall took over the role in 2018. Greenall served as co-producer and associate producer from 2018 to 2022. Aimee Reynolds took over from Greenall in 2022 and has served as associate producer of FIVARS and VRTO since 2022. == Immersive Media Awards == FIVARS presents People's Choice awards for interactive works and immersive video or passive immersive works. Juried award categories have included the Grand Jury Prize, Impact Award, Technical Achievement, Excellence in Experience Design, Excellence in Visual Design, Excellence in Sound Design, and Outstanding Performance. === 2015 === On Monday, September 21, the festival announced People's Choice awards for two categories at the Cadillac Lounge, a music venue and restaurant in Toronto. People's Choice Best Interactive Experience: Apollo 11 Best Immersive Video: SONAR === 2016 === People's Choice Best Interactive Experience: Pearl (Patrick Osborne) Best Immersive Video: Help (Justin Lin) Juried Grand Jury Award: Real (Connor Hair and Alex Meader) === 2017 === People's Choice Best Interactive: Alteration Best Immersive (Passive): Guardian of the Guge Kingdom Juried Impact Award: Priya's Shakti / Priya's Mirror (Dan Goldman) Grand Jury Prize: Manifest 99 === 2018 === People's Choice Best Interactive: Museum of Symmetry (Paloma Dawkins) Best Immersive (Passive): Going Home (David Beier) Juried Impact Award: The Hidden (Annie Lukowski, BJ Schwartz) Grand Jury Prize: Battlescar (Nico Casavecchia, Martin Allais) === 2019 === People's Choice Best Interactive: After Dan Graham (David Han/Friend Generator) Best Immersive (Passive): 2nd Step (Joerg Courtial) Juried Technical Achievement: tx-reverse Excellence in Experience Design: Battlescar (Nico Casavecchia, Martin Allais) Excellence in Sound Design: Unheard (Zhechuan Zhang) Excellence in Visual Design: Ex Anima (Pierre Zandrowicz) Impact Award: State Power (Jeff Stanzler) Grand Jury Prize: The Industry (Mirka Duijn) === 2020 === People's Choice Best Interactive: Gravity VR (Fabito Rychter, Amir Admoni) Best Immersive (Passive): Warsaw Rising (Tomasz Dobosz) Juried Technical Achievement: The Cosmic Laughter of Cucci Binaca (Jonathan Sims) Excellence in Experience Design: Sleeping Eyes (Sojung Bahng, Sungeun Lee) Excellence in Sound Design: Symphony of Noise VR (Michaela Pnacekova) Excellence in Visual Design: Hominidae (Brian Andrews) Impact Award: Indirect Actions (Maranatha Hay) Grand Jury Prize: Minimum Mass (Raqi Syed, Areito Echevarria) === 2021 === FIVARS in FEB – People's Choice Best Interactive: CLAWS (created by Evan Neiden; directed by John Ertman) Best Immersive (Passive): Inside COVID 19 (Gary Yost, Adam Loften) FIVARS in FALL – People's Choice Best Interactive: Samsara (director: Hsin-Chien Huang) Best Immersive (Passive): The Invasion of Normandy Omaha Beach (director: Uli Futschik) Juried Technical Achievement: Dark Threads (director: Jonathon Corbiere) Excellence in Experience Design: Andy's World (director: Liquan Liu) Excellence in Sound Design: Symphony (director: Igor Cortadellas) Excellence in Visual Design: Mind VR Exploration (director: Deng Zuyun) Outstanding Performance: Lori Kovachevich, Lena's Journey (director: Wes Evans) Impact Award: Om Devi: Sheroes Revolution (director: Claudio Casale) Grand Jury Prize: Montegelato (director: Davide Rapp) === 2022 === FIVARS in FEB – People's Choice Best Interactive: Severance Theory: Welcome to Respite (Lyndsie Scoggin, United States) Best Immersive (Passive): Beescapes (Alan Nguyen, Australia) FIVARS in FALL – People's Choice Best Interactive: Namuanki (Kevin Mack, United States) Best Immersive (Passive): Reimagined Vol. 1: Nyssa (Julie Cavaliere, United States) Juried (Whole Year) Technical Achievement: Namuanki (Kevin Mack, United States) Excellence in Experience Design: Unframed: Hand Puppets, Paul Klee (Martin Charrière, Switzerland) Excellence in Visual Design: The Last Dance (Toshiaki Hanzaki, Japan) Excellence in Sound Design: Kingdom of Plants with David Attenborough (Iona McEwan, UK and USA) Outstanding Performance: Ari Tarr, OffRail (Ari Tarr, United States) Impact Award: Tearless (Gina Kim, South Korea) Grand Jury Prize: Klaxon. My dear sweet Friend (Nikita Shokhov, United States) === 2023 === People's Choice Best Interactive: PULSAR Best Immersive (Passive): Behind the Dish Juried Technical Achievement: VFC Excellence in Experience Design: Broken Spectre Excellence in Visual Design: Night Creatures Excellence in Sound Design: VFC Outstanding Performance: Origins Impact Award: LOU Grand Jury Prize: Stay Alive, My Son === 2024 ==
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AVS Video Editor

AVS Video Editor is a video editing software published by Online Media Technologies Ltd. It is a part of AVS4YOU software suite which includes video, audio, image editing and conversion, disc editing and burning, document conversion and registry cleaner programs. It offers the opportunity to create and edit videos with a vast variety of video and audio effects, text and transitions; capture video from screen, web or DV cameras and VHS tape; record voice; create menus for discs, as well as to save them to plenty of video file formats, burn to discs or publish on Facebook, YouTube, Flickr, etc. == Description == === Interface === The layout consists of the timeline or storyboard view, preview pane and media library (transitions, video effects, text or disc menus) collections. The storyboard view shows the sequence of video clips with the transitions between them and used to change the order of clips or add transitions. Timeline view consists of main video, audio, effects, video overlay and text lines for editing. Once on the timeline video can be duplicated, split, muted, frozen, cropped, stabilized, its speed can be slowed down or increased, audio and color corrected. === Importing footage === Video, audio and image files necessary for video project can be imported into the program from computer hard disk drive. User can also capture video from computer screen, web or mini DV camera, as well as from VHS tape, record voice. === Output (web, device, disc, format) === AVS Video Editor gives the opportunity to save video to a computer hard drive to one of the video formats: AVI, DVD, Blu-ray, MOV, MP4, M4V, MPEG, WMV, MKV, WebM, M2TS, TS, FLV, SWF, RM, 3GP, GIF, DPG, AMV, MTV; burn to DVD or Blu-ray disc with menus; create a video for mobile players, mobile phones or gaming consoles and upload it right to the device. The most popular devices such as Apple iPod, Apple iPhone, Apple iPad, Sony PSP, Samsung Galaxy, Android and BlackBerry smartphones and tablets are supported. There is also an option to create a video that can be streamed via web and save it into Flash or WebM format or for the popular web services: YouTube, Facebook, Telly (Twitvid), Dailymotion, Flickr and Dropbox. === Features === Single and multithread modes: if a computer supports multi-threading, video creation process is performed faster in multithread mode, especially on a multi-core system. Customization of the output file settings, such as bitrate, frame rate, frame size, video and audio codecs, etc. Transitions - help video clips smoothly go into one another, dissolve or overlap two video or image files. Fade in and fade out video and audio files - dissolve a video to and from a blank image, reduce the audio volume at the end of the video and increase at the beginning. Slideshow creation - create a presentation of a series of still images. Voice recording Projects - once a project is created and saved, the next time saving video to some other format will be fast, projects are also used if a user do not have a possibility to create, edit and save video all at once. Video overlay option - superpose video image over the video clip that is being edited. Disk menu and chapters creation - an option for DVD and Blu-ray video. Freeze frame - make a still shot from a video clip. Stabilization feature - reduce jittering or blurring caused by shaky motions of a camera. Enhanced deinterlacing method - increase video quality for interlaced input file - spots and blurred areas are compensated. Scene detection - search and separate one scene of the video from the other. Loop DVD and SWF - output SWF and DVD video are played back continuously. Caching for processing high definition files - create a duplicate video file smaller in size to use it on the preview window and accelerate processing of HD files. Chroma key option - add video overlay half transparent so that only part of it is visible and all the rest disappears to reveal the video underneath. Capture video material from DV tapes, VHS tapes, web cameras, etc. Movie closing credits - add information on movie editing, e.g. crew, cast, data, etc. Creeping line, subtitles, text - add different captions (static and animated), shapes and images to video. Speech balloons and other graphic objects - geometrical shapes to highlight an object in the video. Zoom effect - magnify or reduce the view of the image. Rotate effect - rotate video image at different degrees, e.g. 90, 180, etc. Grayscale and old movie effects - create a black and white video image. Old movie adds also scratches, noise, shake and dust to video, as if it's being played on an old projector. Blur and sharpen effects - visually smooth and soften an image, or make video image better focused. Snow and particles effects - adds snow or various objects (bubbles, flowers, leaves, butterflies etc.) that are moving, flying or falling on the video. Pan and zoom Timer, countdown effects - add a timepiece that measures or counts down a time interval to the video being edited. Snapshots - capture a particular moment of a video clip. Sound track replacement - mute audio track from video and add another one. Audio amplify, noise removal, equalizer, etc. - make video sound louder, attenuate the noise, change frequency pattern of the audio, make some other audio adjustments. Trim and multi-trim options - change video clip duration cutting out unnecessary parts or detect scenes and cut out parts in any place of the video clip. Color correction (brightness, temperature, contrast, saturation, gamma, etc.) effects - allow adjustment of tonal range, color, and sharpness of video files. Crop scale effect - get rid of mattes that appear after changing aspect ratio of a video file. Adjusting the Playback Speed Volume and balance - change sound volume in the output video. Change volume value proportion for main video and added soundtrack, completely mute main video audio and leave added soundtrack only, etc. === Utilities embedded into AVS Video Editor === AVS Mobile Uploader is used to transfer edited and converted media files to portable devices via Bluetooth, Infrared or USB connection. AVS Video Burner is used to burn converted video files to different disc types: CD, DVD, Blu-ray. AVS Video Recorder is used to capture video from analog video sources and supports different types of devices: capture card, web camera (webcam), DV camera, HDV camera. AVS Video Uploader is used to transfer video files to popular video-sharing websites, like Facebook, Dailymotion, YouTube, Photobucket, TwitVid, MySpace, Flickr. AVS Screen Capture is used to capture any actions on the desktop to make presentations or video tutorials more vivid and easily comprehensible. == Important upgrades == The initial release of AVS Video Editor was in 2003 when the program was offered inside AVS software bundles together with AVS Video Tools, AVS Audio Tools and DVD Copy software. In 2005 the program is offered as a part of multifunctional AVS4YOU software suite. AVS Video Editor is frequently updated. The main updates include adding several important features for video editing
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Software-defined mobile network

Software-defined mobile networking (SDMN) is an approach to the design of mobile networks where all protocol-specific features are implemented in software, maximizing the use of generic and commodity hardware and software in both the core network and radio access network (RAN). == History == Through the 20th century, telecommunications technology was driven by hardware development, with most functions implemented in special-purpose equipment. In the early 2000s, generally available CPUs became cheap enough to enable commercial software-defined radio (SDR) technology and softswitches. SDMN extends these trends into the design of mobile networks, moving nearly all network functions into software. The term "software-defined mobile network" first appeared in public literature in early 2014, used independently by Lime Microsystems and researchers from University of Oulu, Finland. == Limitations of hardware-based mobile networks == Mobile networks based on special-purpose hardware suffer from the following limitations: They have limited provisions for upgrades and usually must be replaced entirely when new standards are introduced. The individual components are not scalable in terms of performance and capacity, because the capacity of a component is fixed by the hardware implementation. Specialized equipment and its associated specialized software require vendor-specific training for the mobile operator's staff. Specialized hardware systems are usually supported and serviced by a single vendor, resulting in vendor lock-in. == Characteristics of SDMN designs == === Use of software-defined radio === SDR is an important element of SDMN, because it replaces protocol-specific radio hardware with protocol-agnostic digital transceivers. While many earlier digital radio systems used field-programmable gate arrays (FPGAs) or special-purposed digital signal processors (DSPs) for calculations on baseband radio waveforms, the SDMN approach moves all of the baseband processing into general-purpose CPUs. SDMN radio systems also use hardware with publicly-documented interfaces that is designed to be readily reproducible by multiple manufacturers. === Commodity components === SDMN designs avoid the use of components that are specialized as to their functions or that are available from only a single vendor. This is true of both the hardware and software elements of the network. === Software switching and transcoding === The telephony switches of SDMN networks are software-based, including software transcoding for speech codecs. === Centralized, distributed, or hybrid? === A new SDN architecture for wireless distribution systems (WDSs) is explored that eliminates the need for multi-hop flooding of route information and therefore enables WDNs to easily expand. The key idea is to split network control and data forwarding by using two separate frequency bands. The forwarding nodes and the SDN controller exchange link-state information and other network control signaling in one of the bands, while actual data forwarding takes place in the other band. == Advantages of SDMN == The SDMN approach has many advantages over hardware-based mobile network designs. Because SDMN hardware is protocol-agnostic, upgrades are software-only, even across technology generations. In the radio network, these changes can even be made on a site-by-site basis. Because SDMN hardware is designed to be easily sourced and reproduced: SDMN equipment can be serviced by a wider range of vendors, lowering maintenance costs. SDMN equipment can be manufactured anywhere in the world, lowering production costs. Because SDMN software is based on commodity operating systems and development tools: Support staff can be trained more quickly because they are already familiar with the underlying software systems. Many aspects of the SDMN can be monitored and managed with pre-existing tools, because they are already available in the commodity operating systems. Because SDMN network components run on general purpose computers, the network components can be scaled up in capacity by adding more computing power.
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Algorithmic amplification

Algorithmic amplification is the process by which automated ranking and recommendation systems on digital platforms increase the visibility of certain content beyond its initial audience. Major platforms including Facebook, YouTube, TikTok, and X (formerly Twitter) use such systems to determine what appears in users' feeds and search results. The term is used in research on social media and digital media regulation to describe how platform design choices influence the distribution of online information. Unlike chronological feeds, algorithmic systems evaluate content using signals such as engagement rates, viewing duration, and predicted relevance to individual users. Content that performs strongly on these metrics may be promoted to progressively larger audiences through feeds, search rankings, or autoplay systems. The process is distinct from content moderation, which involves removing, labelling, or restricting content under platform rules, although the two can interact in practice. The concept is closely connected to the attention economy. Research has linked algorithmic amplification to the spread of misinformation and the circulation of political content, as well as to effects on young users' mental health. The scale and direction of those effects remain debated, in part because independent researchers have limited access to the internal workings of platform recommendation systems. Governments in the European Union, United Kingdom, United States, and China have pursued differing regulatory approaches to recommendation algorithms. The EU's Digital Services Act and the UK's Online Safety Act 2023 impose obligations on large platforms related to recommendation system transparency and risk, while China became the first country to enact binding legislation specifically targeting such systems. Internal documents and whistleblower testimony reported by the BBC in 2026 described how competitive pressure between Meta and TikTok led to trade-offs between engagement and user safety in the design of their recommendation systems. == Terminology == The term algorithmic amplification is used in media studies, platform governance scholarship and regulatory literature to describe how automated systems influence the distribution of content beyond what organic user sharing alone would produce. It is distinct from viral spread, which refers primarily to user-driven sharing behaviour, and from algorithmic bias, which describes systematic errors or unfairness in algorithmic outputs. The related term algorithmic curation is used for the broader process of selecting and ordering content, of which amplification is one possible outcome. The phrase also appears in regulatory and legislative discussion of recommendation systems. The European Union's Digital Services Act (DSA) identifies recommendation systems as a potential source of systemic risk, and the term appears frequently in academic and policy commentary on the regulation. In the United States, proposals including the Filter Bubble Transparency Act and the Kids Online Safety Act (KOSA) have used it to frame requirements around recommendation system transparency. In the United Kingdom, the House of Commons Science, Innovation and Technology Committee used the term in a 2025 report on how recommendation algorithms contributed to the spread of misinformation during the 2024 Southport riots. A Joint Declaration on AI and Freedom of Expression adopted in October 2025 by four international freedom of expression mandate holders, including the UN Special Rapporteur on Freedom of Opinion and Expression and the OSCE Representative on Freedom of the Media, stated that recommender systems and other AI-powered curation tools exert "a large hidden influence and gatekeeper role" over what information people access and consume. == Background == Early internet platforms typically displayed content in reverse-chronological order or through keyword-based search systems. Although the term is most often applied to social media, the underlying logic predates social media itself. A 2021 overview traced the origins of modern recommendation systems to the early 1990s, when they were first used experimentally for personal email and information filtering. The 1992 Tapestry mail system and the 1994 GroupLens news filtering system were early milestones before recommendation systems spread into e-commerce and other online services. As user bases and content volumes grew during the 2000s, major platforms including Google, YouTube, and Facebook developed machine-learning systems to personalise content delivery and prioritise material predicted to generate engagement. Facebook introduced its News Feed in 2006, which gradually shifted from chronological presentation towards algorithmically ranked content. YouTube altered its recommendation system in 2012 to prioritise watch time rather than clicks, a change the platform said was prompted by concerns that click-based metrics encouraged misleading thumbnails and low-quality videos. TikTok, launched internationally in 2018, adopted a model in which its primary content surface, the For You feed, is driven almost entirely by algorithmic recommendation rather than by a user's social graph. An internal document obtained by The New York Times in 2021 showed that the platform's algorithm optimised for retention and time spent, using signals such as watch duration, replays, likes, and comments to score and rank videos. Algorithmic recommendation also became central to platforms outside social media. Spotify's personalised features, including Discover Weekly, Release Radar, and Home recommendations, use behavioural signals and inferred "taste profiles" to surface tracks and artists beyond a listener's existing library. An ethnographic study of music curators at streaming platforms described this blend of algorithmic and human editorial selection as an "algo-torial" model of gatekeeping. Amazon adopted item-based collaborative filtering for product recommendations in 1998, and its recommendation engine has been described as one of the earliest large-scale deployments of recommendation technology in e-commerce. The same dynamics operate on adult content platforms. Law professor Amy Adler has argued that from 2007 onwards the pornography industry migrated to algorithm-driven streaming platforms, most of which are controlled by a single near-monopoly company, Aylo (formerly MindGeek). These platforms use algorithmic search engines, suggestions, rigid categorisation of content, and AI-driven search term optimisation in ways that produce the same distorting effects found on mainstream speech platforms, including filter bubbles, feedback loops, and the tendency of algorithmic recommendations to alter individual preferences. == Mechanisms == Recommendation systems commonly combine collaborative filtering, which predicts a user's preferences from the behaviour of similar users, with machine-learning models that predict which content a user is likely to engage with from their prior activity. In a common two-stage design, a platform first generates a set of candidate items from a large content pool and then ranks them using a scoring model with objectives such as predicted engagement or user satisfaction. Small changes in ranking criteria can shift exposure at scale, particularly when applied repeatedly across multiple browsing sessions. These systems typically rely on signals including engagement rates, viewing duration, click-through rates, and network relationships between users. Modern recommendation pipelines continuously update predictions as new behavioural data arrives, allowing platforms to adjust rankings in near real time. Users' revealed preferences, expressed through behaviour such as clicks and viewing time, do not always align with their stated preferences, expressed through explicit feedback such as surveys or content controls. Popularity signals can create feedback dynamics in which early engagement increases the likelihood that content will be shown to additional users. Experimental research on online cultural markets has demonstrated how such feedback processes can produce unequal visibility outcomes even when initial differences in content quality are small. == Beneficial and public-interest uses == Recommendation systems can help users navigate large volumes of content by surfacing material predicted to match their interests or needs, which can improve discoverability on platforms with large content libraries. In public health communication, platforms can help health authorities distribute timely information at scale, though the same recommendation systems also risk amplifying misinformation alongside official guidance. Sociologist Zeynep Tufekci has argued that the shift from independent blogs to large centralised platforms transferred gatekeeping power from traditional media to corporate algorithms. In the case of the Egyptian uprising of 2011, she noted that ordinary users
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Pridgen v University of Calgary

Pridgen v University of Calgary was freedom of speech case which took place in Alberta, Canada, in 2010. The case deals with two university students, Keith and Steven Pridgen, who were found guilty and punished by the University of Calgary in 2008, on grounds of "non-academic misconduct". The University of Calgary defines "non-academic misconduct" as:(a) conduct which causes injury to a person and/or damage to University property and/or the property of any member of the University community; (b) unauthorized removal and/or unauthorized possession of University property; and (c) conduct which seriously disrupts the lawful educational and related activities of other students and/or University staff.The Court of the Queen's Bench of Alberta found the University of Calgary to be wrong in prosecuting ten students, including the Pridgen brothers, in regards to comments made about a professor on Facebook. The key ruling in this case was that the universities are not exempt from, and that these students were in fact protected under, section 2(b) of the Charter of Rights and Freedoms. This case is notable as it highlights the jurisdiction of the Charter in terms of both new media technologies and university institutions in Canada. == Background == Keith and Steven Pridgen were undergraduate students at the University of Calgary in 2008. The twin brothers shared a Law and Society class being taught by Aruna Mitra. Professor Mitra was teaching this class for the first time in her career, and many of the students were very critical of her knowledge of the course. A Facebook page entitled “I NO Longer Fear Hell, I Took a Course with Aruna Mitra” was created, and many students began posting comments. In particular, Steven Pridgen's comment on November 13, 2007, read: “Somehow I think she just got lazy and gave everybody a 65....that's what I got. Does anybody know how to apply to have it remarked?” Many students had similar concerns to Pridgen's and after having their work re-marked, a number of them did in fact receive higher grades. Keith Pridgen also commented on August 26, 2008: “Hey fellow LWSO. Homees.. So I am quite sure Mitra is NO LONGER TEACHING ANY COURSES WITH THE U OF C !!!!! Remember when she told us she was a long-term professor? Well, Actually she was only sessional and picked up our class at the last moment because another prof wasn't able to do it ...lucky us. Well, anyways I think we should all congratulate ourselves for leaving a Mitra-free legacy for future students!” On September 4, 2008, Aruna Mitra complained about the Facebook page to the Interim Dean of the Faculty of Communication and Culture at the University of Calgary. Dean Tettey called a meeting for the ten students who posted material about Mitra on the Facebook page. The meeting took place on September 18, 2008, and included four professors from the department as well as the Dean. At this meeting, all ten students, including the Pridgen brothers, were found guilty of non-academic misconduct. On November 20, 2008, the Appellant's received a letter from Dean Tettey advising them that their comments “clearly caused unwarranted professional and personal injury to Prof. Mitra and clearly meets the criteria for non-academic misconduct as outlined in the University of Calgary Calendar”. Keith Pridgen was put on probation for 24 months, and both brothers were required to write a letter of apology to Prof. Mitra and refrain from posting or circulating defamatory material regarding any faculty members of the University of Calgary. The Pridgen brothers appealed the decision to the University of Calgary Review Committee and later to the Board of Governors of the University of Calgary however neither of these attempts succeeded in having the decision overturned. == Opinion of the Court == Eight main issues to be determined were laid out by the Honourable Madam Justice J. Strekaf: (a) Does the Charter apply to the disciplinary proceedings taken by the Respondent; (b) If, so were the Applicants' Charter rights infringed; (c) Were the actions taken by the University ultra vires the jurisdiction of the Province of Alberta; (d) Did the Board of Governors err in refusing to hear the Applicants appeals; (e) Were the Applicants' denied a fair hearing; (f) Did the Review Committee provide adequate reasons for its decisions; (g) Did the Review Committee err in concluding that the activities of the Applicants constituted non-academic misconduct; and (h) What, if any, remedy should be granted to the Applicants. The Court determined from previous cases that "a non-government entity may still be subject to the Charter of Rights and freedoms when implementing a specific government policy or program". Justice Strekaf distinguished that the University was acting as agent of the provincial government in providing accessible post-secondary education services to students in Alberta pursuant to the provisions of the PSL Act. Justice Strekaf felt there was sufficient evidence to show that universities in Alberta have some level of reliance on government funds and therefore they are not a "Charter free zone". Justice Strekaf concluded that comments made by Keith and Steven Pridgen, regarding Professor Mitra, on Facebook did not constitute academic misconduct and the Pridgen brothers' right to freedom of expression, under section 2(b) of the Charter, was infringed by the University of Calgary Review Committee.
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European Cloud Partnership

The European Cloud Partnership (ECP) is an advisory group set up by the European Commission as part of the European Cloud Computing Strategy to provide guidance on the development of cloud computing in the European Union. The ECP is led by a steering board composed of representatives of the IT and telecom industry as well as European government policymakers. == History == After publishing a document, "Unleashing the Potential of Cloud Computing in Europe", the European Commission set up the European Cloud Partnership in 2012, with a steering board including both government and industry representatives. The ECP's first meeting was held on 19 November 2012; it was chaired by the President of Estonia Toomas Hendrik Ilves. In 2013 the ECP began drafting its charter. That year, as information about the PRISM scandal came to light, the ECP emphasized the need for Europe to develop its own cloud infrastructure, rather than depend on that of the United States. It completed a report titled "Trusted Cloud Europe" in February 2014 defining its policy, and outlining a process for effective public and private sector participation in cloud computing development in Europe. The report recommended that the commission identify technical, legal and operational best practices, and promote these through certifications and guidelines, and facilitate recognition across national boundaries. The report also recommended that the commission identify cloud computing stakeholders and help them work together through consultations and workshops. In March 2014, the European Commission invited external parties to submit opinions, take part in a discussion forum and complete an online survey in response to the report.
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Gaumina

Gaumina is the largest interactive agency in the Baltics, providing services of web design, web development, online advertising, video, multimedia, mobile and viral. The company works on projects for Procter & Gamble, Nokia, Nissan, Unilever, YX Energi, 7 Up, Vodafone, MTV, Dunnes Stores, Philip Morris, FIBA Europe as well as Irish public sector. == History == Founded in 1998, Gaumina accounts for 39 percent of the Lithuanian interactive market and has completed more than 2,000 online projects. Since 2004 the company has been operating in the UK and Ireland as Gaumina.co.uk. In 2007 Gaumina gained wide media coverage for winning three awards in three days. A website developed by Gaumina won the Best Social Networking website award at the same the Irish Golden Spiders awards. A website developed by Gaumina was named among the 21 best European multimedia projects of 2007 in the final of Europrix Top Talent Award in Austria. The company was also named one of the winners of the national Innovation Prize 2007, awarding the Lithuania's most innovative companies, in the category of Innovative Enterprise. The agency was named "Digital Agency of the Year" by International advertising festival Golden Hammer in September 2008. The agency also won the main prize at the best at Best Use of Film, Digital Animation or Motion Graphics category by the Irish Golden Spider awards in November 2008. Gaumina is currently managed by CEO Darius Bagdžiūnas.
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Fingerprint scanner

Fingerprint scanners are a type of biometric security device that identify an individual by identifying the structure of their fingerprints. They are used in police stations, security industries, smartphones, and other mobile devices. == Fingerprints == People have patterns of friction ridges on their fingers, these patterns are called the fingerprints. Fingerprints are uniquely detailed, durable over an individual's lifetime, and difficult to alter. Due to the unique combinations, fingerprints have become an ideal means of identification. == Types of fingerprint scanners == There are four types of fingerprint scanners: Optical scanners take a visual image of the fingerprint using a digital camera. Capacitive or CMOS scanners use capacitors and thus electric current to form an image of the fingerprint. This type of scanner tends to excel in terms of precision. Ultrasonic fingerprint scanners use high frequency sound waves to penetrate the epidermal (outer) layer of the skin. Thermal scanners sense the temperature differences on the contact surface, in between fingerprint ridges and valleys. All fingerprint scanners are susceptible to spoofing through fingerprints replicated using photographs and 3D printing. == Construction forms == Each type of fingerprint sensor can take two basic forms: the stagnant and the moving fingerprint scanner. Stagnant: The scanning module is mounted statically, and the user is required to swipe their fingers across it. This is cheaper but also less reliable than the moving form. Imaging can be less than ideal if the finger is not dragged over the scanning area at constant speed. Moving: The scanning module is mounted on a movable surface, while the user's finger can remain static. Because this layout allows the scanning module to pass the fingerprint at a constant speed, this method is generally more reliable. == Form factors == === Peripherals === Add-on fingerprint readers for PCs initially appeared in the late 1990's in the form of PCMCIA modules. Microsoft released a model in its IntelliMouse line with an integrated fingerprint reader in 2005. === Integrated readers === Laptops with built-in readers emerged around the same time as peripheral readers with devices such as NECs MC/R730F. IBM produced laptops with integrated readers starting in 2004. Apple introduced fingerprint scanners to their devices under the name Touch ID in 2013. These were initially released on the iPhone 5S, with the technology remaining exclusive to iPhones until the release of the 2016 MacBook Pro. On both laptops and smartphones, the fingerprint sensor usually uses a USB or I2C interface internally.
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ISO/IEC 11801

International standard ISO/IEC 11801 Information technology — Generic cabling for customer premises specifies general-purpose telecommunication cabling systems (structured cabling) that are suitable for a wide range of applications (analog and ISDN telephony, various data communication standards, building control systems, factory automation). It is published by ISO/IEC JTC 1/SC 25/WG 3 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). It covers both balanced copper cabling and optical fibre cabling. The standard was designed for use within commercial premises that may consist of either a single building or of multiple buildings on a campus. It was optimized for premises that span up to 3 km, up to 1 km2 office space, with between 50 and 50,000 persons, but can also be applied for installations outside this range. A major revision was released in November 2017, unifying requirements for commercial, home and industrial networks. == Classes and categories == The standard defines several link/channel classes and cabling categories of twisted-pair copper interconnects, which differ in the maximum frequency for which a certain channel performance is required: Class A: Up to 100 kHz using Category 1 cable and connectors Class B: Up to 1 MHz using Category 2 cable and connectors Class C: Up to 16 MHz using Category 3 cable and connectors Class D: Up to 100 MHz using Category 5e cable and connectors Class E: Up to 250 MHz using Category 6 cable and connectors Class EA: Up to 500 MHz using category 6A cable and connectors (Amendments 1 and 2 to ISO/IEC 11801, 2nd Ed.) Class F: Up to 600 MHz using Category 7 cable and connectors Class FA: Up to 1 GHz (1000 MHz) using Category 7A cable and connectors (Amendments 1 and 2 to ISO/IEC 11801, 2nd Ed.) Class BCT-B: Up to 1 GHz (1000 MHz) using with coaxial cabling for BCT applications. (ISO/IEC 11801-1, Edition 1.0 2017-11) Class I: Up to 2 GHz (2000 MHz) using Category 8.1 cable and connectors (ISO/IEC 11801-1, Edition 1.0 2017-11) Class II: Up to 2 GHz (2000 MHz) using Category 8.2 cable and connectors (ISO/IEC 11801-1, Edition 1.0 2017-11) The standard link impedance is 100 Ω. (The older 1995 version of the standard also permitted 120 Ω and 150 Ω in Classes A−C, but this was removed from the 2002 edition.) The standard defines several classes of optical fiber interconnect: OM1: Multimode, 62.5 μm core; minimum modal bandwidth of 200 MHz·km at 850 nm OM2: Multimode, 50 μm core; minimum modal bandwidth of 500 MHz·km at 850 nm OM3: Multimode, 50 μm core; minimum modal bandwidth of 2000 MHz·km at 850 nm OM4: Multimode, 50 μm core; minimum modal bandwidth of 4700 MHz·km at 850 nm OM5: Multimode, 50 μm core; minimum modal bandwidth of 4700 MHz·km at 850 nm and 2470 MHz·km at 953 nm OS1: Single-mode, maximum attenuation 1 dB/km at 1310 and 1550 nm OS1a: Single-mode, maximum attenuation 1 dB/km at 1310, 1383, and 1550 nm OS2: Single-mode, maximum attenuation 0.4 dB/km at 1310, 1383, and 1550 nm Grandfathered === OM5 === OM5 fiber is designed for wideband applications using SWDM multiplexing of 4–16 carriers (40G=4λ×10G, 100G=4λ×25G, 400G=4×4λ×25G) in the 850–953 nm range. === Category 7 === Class F channel and Category 7 cable are backward compatible with Class D/Category 5e and Class E/Category 6. Class F features even stricter specifications for crosstalk and system noise than Class E. To achieve this, shielding was added for individual wire pairs and the cable as a whole. Unshielded cables rely on the quality of the twists to protect from EMI. This involves a tight twist and carefully controlled design. Cables with individual shielding per pair such as Category 7 rely mostly on the shield and therefore have pairs with longer twists. The Category 7 cable standard was ratified in 2002, and primarily introduced to support 10 gigabit Ethernet over 100 m of copper cabling. Like the earlier standards, it contains four twisted copper wire pairs rated for transmission frequencies of up to 600 MHz. However, in 2006, Category 6A was ratified for Ethernet to allow 10 Gbit/s while still using the conventional 8P8C connector. Care is required to avoid signal degradation by mixing cable and connectors not designed for that use, however similar. Most manufacturers of active equipment and network cards have chosen to support the 8P8C for their 10 gigabit Ethernet products on copper and not GG45, ARJ45, or TERA connectors as Class F would have originally called for. Therefore, the Category 6 specification was revised to Category 6A to permit this use; products therefore require a Class EA channel (ie, Cat 6A). As of 2019, some equipment has been introduced which has connectors supporting the Class F (Category 7) channel. Note, however, that Category 7 is not recognized by the TIA/EIA. === Category 7A === Class FA (Class F Augmented) channels and Category 7A cables, introduced by ISO 11801 Edition 2 Amendment 2 (2010), are defined at frequencies up to 1000 MHz. The intent of the Class FA was to possibly support the future 40 gigabit Ethernet: 40GBASE-T. Simulation results have shown that 40 gigabit Ethernet may be possible at 50 meters and 100 gigabit Ethernet at 15 meters. In 2007, researchers at Pennsylvania State University predicted that either 32 nm or 22 nm circuits would allow for 100 gigabit Ethernet at 100 meters. However, in 2016, the IEEE 802.3bq working group ratified the amendment 3 which defines 25GBASE-T and 40GBASE-T on Category 8 cabling specified to 2000 MHz. The Class FA therefore does not support 40G Ethernet. As of 2025, there is no equipment that has connectors supporting the Class FA (Category 7A) channel. Category 7A is not recognized in TIA/EIA. === Category 8 === Category 8 was ratified by the TR43 working group under ANSI/TIA 568-C.2-1. It is defined up to 2000 MHz and only for distances up to 30 m or 36 m, depending on the patch cords used. ISO/IEC JTC 1/SC 25/WG 3 developed the equivalent standard ISO/IEC 11801-1:2017/COR 1:2018, with two options: Class I channel (Category 8.1 cable): minimum cable design U/FTP or F/UTP, fully backward compatible and interoperable with Class EA (Category 6A) using 8P8C connectors; Class II channel (Category 8.2 cable): F/FTP or S/FTP minimum, interoperable with Class FA (Category 7A) using TERA or GG45. == Abbreviations for twisted pairs == Annex E, Acronyms for balanced cables, provides a system to specify the exact construction for both unshielded and shielded balanced twisted pair cables. It uses three letters—U for unshielded, S for braided shielding, and F for foil shielding—to form a two-part abbreviation in the form of xx/xTP, where the first part specifies the type of overall cable shielding, and the second part specifies shielding for individual cable elements. Common cable types include U/UTP (unshielded cable); U/FTP (individual pair shielding without the overall screen); F/UTP, S/UTP, or SF/UTP (overall screen without individual shielding); and F/FTP, S/FTP, or SF/FTP (overall screen with individual foil shielding). == 2017 edition == In November 2017, a new edition was released by ISO/IEC JTC 1/SC 25 "Interconnection of information technology equipment" subcommittee. It is a major revision of the standard which has unified several prior standards for commercial, home, and industrial networks, as well as data centers, and defines requirements for generic cabling and distributed building networks. The new series of standards replaces the former 11801 standard and includes six parts: == Versions == ISO/IEC 11801:1995 (Ed. 1) ISO/IEC 11801:2000 (Ed. 1.1) – Edition 1, Amendment 1 ISO/IEC 11801:2002 (Ed. 2) ISO/IEC 11801:2008 (Ed. 2.1) – Edition 2, Amendment 1 ISO/IEC 11801:2010 (Ed. 2.2) – Edition 2, Amendment 2 ISO/IEC 11801-1:2017, -1:2017/Cor 1:2018, -2:2017, -3:2017, -3:2017/Amd 1:2021, -3:2017/Cor 1:2018, -4:2017, -4:2017/Cor 1:2018, -5:2017, -5:2017/Cor 1:2018, -6:2017, -6:2017/Cor 1:2018 (As of September 2023, this set is current.)
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Scikit-learn

scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project. == Overview == The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project derives from its role as a "scientific toolkit for machine learning", originally developed and distributed as a third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in Computer Science and Automation in Saclay, France, took leadership of the project and released the first public version of the library on February 1, 2010. In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries. In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub. At that time, the project had over 1,400 contributors and the documentation received 42 million visits in 2018. According to a 2022 Kaggle survey of nearly 24,000 respondents from 173 countries, scikit-learn was identified as the most widely used machine learning framework. == Features == Large catalogue of well-established machine learning algorithms and data pre-processing methods (i.e. feature engineering) Utility methods for common data-science tasks, such as splitting data into train and test sets, cross-validation and grid search Consistent way of running machine learning models (estimator.fit() and estimator.predict()), which libraries can implement Declarative way of structuring a data science process (the Pipeline), including data pre-processing and model fitting == Examples == Fitting a random forest classifier: == Implementation == scikit-learn is largely written in Python, and uses NumPy extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in Cython to improve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR. In such cases, extending these methods with Python may not be possible. scikit-learn integrates well with many other Python libraries, such as Matplotlib and plotly for plotting, NumPy for array vectorization, Pandas dataframes, SciPy, and many more. == History == scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010. The project released its first stable version, 1.0.0, on September 24, 2021. The release was the result of over 2,100 merged pull requests, approximately 800 of which were dedicated to improving documentation. Development continues to focus on bug fixes, efficiency and feature expansion. The latest version, 1.8, was released on December 10, 2025. This update introduced native Array API support, enabling the library to perform GPU computations by directly using PyTorch and CuPy arrays. This version also included bug fixes, improvements and new features, such as efficiency improvements to the fit time of linear models. == Applications == Scikit-learn is widely used across industries for a variety of machine learning tasks such as classification, regression, clustering, and model selection. The following are real-world applications of the library: === Finance and Insurance === AXA uses scikit-learn to speed up the compensation process for car accidents and to detect insurance fraud. Zopa, a peer-to-peer lending platform, employs scikit-learn for credit risk modelling, fraud detection, marketing segmentation, and loan pricing. BNP Paribas Cardif uses scikit-learn to improve the dispatching of incoming mail and manage internal model risk governance through pipelines that reduce operational and overfitting risks. J.P. Morgan reports broad usage of scikit-learn across the bank for classification tasks and predictive analytics in financial decision-making. === Retail and E-Commerce === Booking.com uses scikit-learn for hotel and destination recommendation systems, fraudulent reservation detection, and workforce scheduling for customer support agents. HowAboutWe uses it to predict user engagement and preferences on a dating platform. Lovely leverages the library to understand user behaviour and detect fraudulent activity on its platform. Data Publica uses it for customer segmentation based on the success of past partnerships. Otto Group integrates scikit-learn throughout its data science stack, particularly in logistics optimization and product recommendations. === Media, Marketing, and Social Platforms === Spotify applies scikit-learn in its recommendation systems. Betaworks uses the library for both recommendation systems (e.g., for Digg) and dynamic subspace clustering applied to weather forecasting data. PeerIndex used scikit-learn for missing data imputation, tweet classification, and community clustering in social media analytics. Bestofmedia Group employs it for spam detection and ad click prediction. Machinalis utilizes scikit-learn for click-through rate prediction and relational information extraction for content classification and advertising optimization. Change.org applies scikit-learn for targeted email outreach based on user behaviour. === Technology === AWeber uses scikit-learn to extract features from emails and build pipelines for managing large-scale email campaigns. Solido applies it to semiconductor design tasks such as rare-event estimation and worst-case verification using statistical learning. Evernote, Dataiku, and other tech companies employ scikit-learn in prototyping and production workflows due to its consistent API and integration with the Python ecosystem. === Academia === Télécom ParisTech integrates scikit-learn in hands-on coursework and assignments as part of its machine learning curriculum. == Awards == 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize: Awarded in recognition of scikit-learn's impact as a major free software breakthrough in machine learning and its role in the digital transformation of science and industry. 2022 Open Science Award for Open Source Research Software: Awarded by the French Ministry of Higher Education and Research as part of the second National Plan for Open Science. The project was recognized in the "Community" category for its technical quality, its large international contributor network, and the quality of its documentation.
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Line splice

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

Bluelight is a web-forum, research portal, online community, and non-profit organisation dedicated to harm reduction in drug use. Its userbase includes current and former substance users, academic researchers, drug policy activists, and mental health advocates. It is believed to be the largest online international drug discussion website in the world. As of November 2025, the website claims over 475,900 registered members, the Discord community claims over 11,900 members, and additional members utilise other platforms such as Telegram. Bluelight has been utilised by academic researchers as a primary source of data in numerous publications. Researchers also utilise the site to advertise research studies, recruit study participants, and better understand the world of substance use. Research groups and organisations that have partnered with Bluelight to recruit study participants include Imperial College London, Johns Hopkins University, Health Canada, Karlstad University, Curtin University, Macquarie University, Columbia University, University of Pennsylvania, University of Michigan, Toronto Metropolitan University (then known as Ryerson University), and MAPS. Researchers have found that the most common reasons for substance users to visit Bluelight.org and similar online communities are to learn "how to use drugs safely" and "how to help others use drugs safely." Bluelight neither condemns or condones drug use, instead advocating for the principle of responsible drug use; educating and allowing individuals to make informed decisions regarding their drug use, providing information on local drug misuse services, and providing them with other drug harm reduction resources and public safety notices. == History == Bluelight.org was originally formed in 1997 as a message board on bluelight.net called the MDMA Clearinghouse. The board was created as a side project by the owner of West Palm Beach design company Bluelight Designs. 200–300 users joined the site between 1998 and 1999, but the site's servers were heavily limited and could only store a few threads at a time; this led to the creation of 'The New Bluelight' forum in May 1999 and the registration of the bluelight.nu domain in June 1999. The site began to explode in popularity in the early 2000s with the rise of MDMA in the club scene, amassing nearly 7,000 members by the year 2000 and 59,000 by the start of 2006. The site switched to the bluelight.ru domain in October 2005, and switched again to bluelight.org in January 2014. In early 2024, Bluelight was re-structured and the forum became a subsidiary of the newly formed Australian non-profit organisation & registered charity Bluelight Communities Ltd. == Partnerships == In the early 2000s, Bluelight worked with reagent test supplier EZ-Test to promote the sale of drug checking kits. In 2007, Bluelight partnered with the Multidisciplinary Association for Psychedelic Studies (MAPS), a non-profit organisation working to raise awareness and understanding of psychedelic drugs through education, clinical research, and advocacy. MAPS utilised Bluelight to recruit participants for its first MDMA-assisted psychotherapy trial for PTSD. In 2013, the official MAPS forums were migrated to Bluelight. Bluelight's other partners include Erowid, a non-profit organisation dedicated to education surrounding psychoactive drugs; TripSit, a harm reduction education website; Pill Reports, a web-based database for drug checking results that was initially formed as an offshoot of the site; and the Global Drug Survey, an independent research organisation focused on collecting data about substance use. == Notable users == Alan Woods – funded the site's maintenance costs from 1999 until his death in 2008 Hamilton Morris John McAfee – created an infamous series of troll posts about the stimulant MDPV
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