AI For Student Recruitment

AI For Student Recruitment — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Friendica

  Friendica (formerly Friendika, originally Mistpark) is a free and open-source software distributed social network. It forms one part of the Fediverse, an interconnected and decentralized network of independently operated servers. == Features == Friendica users can connect with others via their own Friendica server, but may also fully integrate contacts from other platforms including Diaspora, Pump.io, GNU social, email, Discourse and more recently ActivityPub (including Mastodon, Pleroma and Pixelfed) and Bluesky into their 'newsfeed'. In addition to these two way connections, users can also use Friendica as a publishing platform to post content to WordPress, Tumblr, Insanejournal and Libertree. Posting to Google+ was also supported until that service was shut down. In addition, RSS feeds can be ingested. Because users are distributed across many servers, their "addresses" consist of a username, the "@" symbol, and the domain name of the Friendica instance in the same manner email addresses are formed. Twitter support was available but was deprecated due to API changes under Elon Musk's leadership rendering it unusable. Most of the functionality from major microblogging and social networking platforms are available in Friendica; for example, tagging users and groups via "@ mentions"; direct messages; hashtags; photo albums; "likes"; "dislikes"; comments; and re-shares of publicly visible posts. Published items can be edited and updated across the network. Comprehensive settings for privacy and the public visibility of posts allow users to regulate who can read which contributions, or see specific information about the user. Users can also create multiple profiles, allowing different groups of people (such as friends, or work mates) to see a different profile entirely when viewing the same page. User accounts can be downloaded or deleted, and can be imported to a different Friendica server if so required. Public forums can be created under different accounts, which can be switched between if the accounts are registered with the same email address. == Development == There is no corporation behind Friendica. The developers work on a voluntary basis and the project is run informally; the platform itself is used for the communication between the developers. There are different forums within Friendica, such as "Friendica Developers" and "Friendica Support". The source code of Friendica is hosted on GitHub. == Installation == The developers aim to make installation of the software as simple as possible for technical laymen. They argue that decentralization on small servers is a key condition for the freedom of users and their self-determination. The difficulty level is similar to an installation of WordPress. However, the installing on shared hosting is sometimes difficult because of missing PHP5 modules. Some volunteers also run public servers so that newcomers can also avoid the installation of their own software. == List of clients == Friendica implements multiple client-server API variants simultaneously. Along with endpoints needed to use enhanced Friendica features, it also implements the API used by GNU social, Twitter and since version 2021.06 also the one used by Mastodon. As a result, most GNU social and Mastodon clients can be used for Friendica. Examples of Friendica compatible clients include: Raccoon for Friendica, Friendiqa, Fedilab, AndStatus, Twidere and DiCa for Android, friendly for Sailfish OS, friclicli (CLI client), choqok and Friendiqa for Linux and Friendica Mobile for Windows 10. == Reception == Friendica was cited in January 2012 by Infoshop News as an "alternative to Google+ and Facebook" to be used on the Occupy Nigeria movement. In January 2012 Free Software Foundation Europe's blog cited Friendica as a reasonable alternative to centralized and controlled social networks such as Facebook or Google+. Biblical Notes writer J. Randal Matheny described Friendica in January 2012 as "One social networking option flying under the radar until recently deserves consideration as an already stable platform with a wide range of options, applications, plug-ins, and possibilities for opening up the Internet." In February 2012, the German computer magazine c't wrote: "Friendica demonstrates how decentralized social networks can become widely accepted." Another German publication, the professional magazine t3n listed Friendica as a Facebook rival in an online article in March 2012 about Facebook alternatives. It compared Friendica with similar social networks like Diaspora and identi.ca. MSN Tech & Gadgets contributor Emma Boyes wrote about Friendica in May 2012: "why you'll love it: you can use it to access all the other social networks and get recommendations of new friends and groups to join. Friendica is open source and decentralised. There's no corporation behind it and there are extensive privacy settings. You can choose from a variety of user interfaces and it boasts some cool features—for instance, being able to key in a list of your interests and use the 'profile match' feature to recommend other users who share them with you. A word of warning, though, the site is not as user-friendly as the others on this list, so it may be this one is one for the geeks." == Later reviews == Acquisition of Twitter by Elon Musk had revitalized public interest in Fediverse technologies in April 2022. Friendica received favorable reviews, with a PCMag article describing it as "mostly comparable to Facebook", drawing a parallel to Google+ and highlighting using it "for planning events, and its multiple profile feature means you can show a different face to your friends, coworkers, and family". The September 2022 issue of Linux Magazine contains a detailed comparison and walk-through of registering to and using basic functions of Diaspora, Friendica and Mastodon. They describe Friendica as "intuitive" and highlight the "huge choice of account settings" and that "Friendica does not require any specific hardware, so you can use an old computer system as a server." == Vulnerabilities == In September 2020, a hotfix was released to patch a security vulnerability that could leak sensitive information from the server environment since versions released in April 2019 (develop branch) and June 2019 (stable).

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  Audience capture

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

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  Solid-state electronics

  Solid-state electronics are semiconductor electronics: electronic equipment that use semiconductor devices such as transistors, diodes and integrated circuits (ICs). The term is also used as an adjective for devices in which semiconductor electronics that have no moving parts replace devices with moving parts, such as the solid-state relay, in which transistor switches are used in place of a moving-arm electromechanical relay, or the solid-state drive (SSD), a type of semiconductor memory used in computers to replace hard disk drives, which store data on rotating disks. == History == The term solid-state became popular at the beginning of the semiconductor era in the 1960s to distinguish this new technology. A semiconductor device works by controlling an electric current consisting of electrons or holes moving within a solid crystalline piece of semiconducting material such as silicon, while the thermionic vacuum tubes it replaced worked by controlling a current of electrons or ions in a vacuum within a sealed tube. Although the first solid-state electronic device was the cat's whisker detector, a crude semiconductor diode invented around 1904, solid-state electronics started with the invention of the transistor in 1947. Before that, all electronic equipment used vacuum tubes, because vacuum tubes were the only electronic components that could amplify—an essential capability in all electronics. The transistor, which was invented by John Bardeen and Walter Houser Brattain while working under William Shockley at Bell Laboratories in 1947, could also amplify, and replaced vacuum tubes. The first transistor hi-fi system was developed by engineers at GE and demonstrated at the University of Philadelphia in 1955. In terms of commercial production, The Fisher TR-1 was the first "all transistor" preamplifier, which became available mid-1956. In 1961, a company named Transis-tronics released a solid-state amplifier, the TEC S-15. The replacement of bulky, fragile, energy-hungry vacuum tubes by transistors in the 1960s and 1970s created a revolution not just in technology but in people's habits, making possible the first truly portable consumer electronics such as the transistor radio, cassette tape player, walkie-talkie and quartz watch, as well as the first practical computers and mobile phones. Other examples of solid state electronic devices are the microprocessor chip, LED lamp, solar cell, charge coupled device (CCD) image sensor used in cameras, and semiconductor laser. Also during the 1960s and 1970s, television set manufacturers switched from vacuum tubes to semiconductors, and advertised sets as "100% solid state" even though the cathode-ray tube (CRT) was still a vacuum tube. It meant only the chassis was 100% solid-state, not including the CRT. Early advertisements spelled out this distinction, but later advertisements assumed the audience had already been educated about it and shortened it to just "100% solid state". LED displays can be said to be truly 100% solid-state.

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  Personal media

  Personal media are media of communication which are used by an individual rather than by a corporation or institution. They are generally contrasted with mass media which are produced by teams of people and broadcast to a general population. In other words, personal media allow individuals, as opposed to corporate entities, to contribute knowledge and opinion to the public. The term dates from the 1980s. New technologies such as social media and self-publishing are creating a variety of modes for modern media. Marika Lüders suggests a two-dimensional model for classifying such media with one dimension being the degree of interaction between the senders and receivers; and the other dimension being the level of institutionalisation and professionalism. Katherine Nashleanas links the concept of personal media to the notion of 'control' by an individual as opposed to a centralised authority. She argues that although personal media including the fax have been available to the general public since the 1960s, more recent technologies such as the smartphone confer greater control over content production and distribution to their users.

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  Alerts.in.ua

  alerts.in.ua is an online service that visualizes information about air alerts and other threats on the map of Ukraine. == History == The idea of the site appeared in the first weeks of the 2022 Russian invasion of Ukraine, during the development of other projects related to alerting the population about alarms. So, on March 2, 2022, the "Lviv Siren" bot was created, which reported on air alarms in Lviv on Twitter. Later, the idea arose to monitor alarms all over Ukraine and display them on a map. However, the lack of a single official source reporting alarms made this task much more difficult. On March 15, 2022, the Ajax Systems company announced the creation of the official Telegram channel "Air Alarm". This channel receives signals from the "Air Alarm" application and instantly publishes messages about the start and end of alarms in different regions of Ukraine. This immediately solved the problem with the source of information and gave impetus to the further implementation of the project. On March 22, 2022, the first version of the "Air Alarm Map" website was published, located on the war.ukrzen.in.ua domain. The map quickly gained popularity in social networks. It, like several other similar projects, began to be widely distributed by the mass media: Suspilne, Novyi Kanal, UNIAN, DW, Fakty ICTV, Vikna TV, Ukrainian Radio, STB, Espresso, dev.ua, itc.ua and state bodies: Center for Countering Disinformation at the National Security and Defense Council of Ukraine, Verkhovna Rada of Ukraine, Khmelnytska OVA, etc. On April 8, 2022, the site moved to the alerts.in.ua domain, where it is still available today. On August 25, 2022, the service began monitoring local official channels in addition to the main "Air Alarm". On September 11, 2022, the English version of the site was published. On March 22, 2023, its own Android application was published. The project is actively developing and has its own community. == Description == The main part of the site is a map of Ukraine, on which the regions where an air alert or other threats have been declared are highlighted in real time. As of October 16, 2022, 5 types of threats are supported: Air alarm. The threat of artillery fire. The threat of street fighting. Chemical threat. Nuclear threat. Additionally, based on media reports, information is published about other dangerous events, such as explosions, demining, etc. On the site, you can view the history of announced alarms with links to sources. Alarm statistics for different time periods are also available. For developers, there is an API that allows you to develop your own services based on information about declared alarms. The site is available in Ukrainian, English, Polish and Japanese. == Use == The map is used by: To monitor the situation in the country and the region. To illustrate the alarms announced in the mass media: TSN, Ukrainian truth, Channel 24, Suspilne, RBC Ukraine, Gromadske, Glavkom. As a map of alarms in mobile applications, there is Alarm and AirAlert. As an API for its services, including alternative alarm maps, Telegram, Viber channels, Discord bots, IoT projects, etc. == Statistics == 89.5% of users use the map from a mobile phone, 10% from a PC and 1% from a tablet. Top 6 countries by visit: Ukraine, United States, Poland, Germany, Great Britain and Japan . == Alternative projects == eMap was created by the developer Vadym Klymenko. AlarmMap is an online from the Ukrainian office of Agroprep. The official map of air alarms was developed by Ajax Systems together with the developer Artem Lemeshev, Stfalcon with the support of the Ministry of Statistics.

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  Algorithmic radicalization

  Algorithmic radicalization is the concept that recommender algorithms on popular social media sites, such as YouTube and Facebook, drive users toward progressively more extreme content over time, leading to the development of radicalized extremist political views. Algorithms meticulously record user interactions, encompassing likes, dislikes and the duration of time watching content, with the objective of generating an endless stream of media designed to sustain user engagement. The phenomenon of echo chamber channels has been demonstrated to exacerbate the polarization of consumers, primarily through the reinforcement of media preferences and the validation of one's existing beliefs. Algorithmic radicalization remains a controversial phenomenon as it is often not in the best interest of social media companies to remove echo chamber channels. To what extent recommender algorithms are actually responsible for radicalization remains disputed. Studies have found contradictory results regarding the promotion of extremist content by algorithms. == Social media echo chambers and filter bubbles == Social media platforms learn the interests and likes of the user to modify their experiences in their feed to keep them engaged and scrolling, known as a filter bubble. An echo chamber is formed when users come across beliefs that magnify or reinforce their thoughts and form a group of like-minded users in a closed system. Echo chambers spread information without any opposing beliefs and can possibly lead to confirmation bias. According to group polarization theory, an echo chamber can potentially lead users and groups towards more extreme radicalized positions. According to the National Library of Medicine, "Users online tend to prefer information adhering to their worldviews, ignore dissenting information, and form polarized groups around shared narratives. Furthermore, when polarization is high, misinformation quickly proliferates." == By site == === Facebook === Facebook's algorithm focuses on recommending content that makes the user want to interact. They rank content by prioritizing popular posts by friends, viral content, and sometimes divisive content. Each feed is personalized to the user's specific interests which can sometimes lead users towards an echo chamber of troublesome content. Users can find their list of interests the algorithm uses by going to the "Your ad Preferences" page. According to a Pew Research study, 74% of Facebook users did not know that list existed until they were directed towards that page in the study. It is also relatively common for Facebook to assign political labels to their users. In recent years, Facebook has started using artificial intelligence to change the content users see in their feed and what is recommended to them. A document known as The Facebook Files has revealed that their AI system prioritizes user engagement over everything else. The Facebook Files has also demonstrated that controlling the AI systems has proven difficult to handle. In an August 2019 internal memo leaked in 2021, Facebook has admitted that "the mechanics of our platforms are not neutral", concluding that in order to reach maximum profits, optimization for engagement is necessary. In order to increase engagement, algorithms have found that hate, misinformation, and politics are instrumental for app activity. As referenced in the memo, "The more incendiary the material, the more it keeps users engaged, the more it is boosted by the algorithm." According to a 2018 study, "false rumors spread faster and wider than true information... They found falsehoods are 70% more likely to be retweeted on Twitter than the truth, and reach their first 1,500 people six times faster. This effect is more pronounced with political news than other categories." === YouTube === YouTube has been around since 2005 and has more than 2.5 billion monthly users. YouTube discovery content systems focus on the user's personal activity (watched, favorites, likes) to direct them to recommended content. YouTube's algorithm is accountable for roughly 70% of users' recommended videos and what drives people to watch certain content. According to a 2022 study by the Mozilla Foundation, users have little power to keep unsolicited videos out of their suggested recommended content. This includes videos about hate speech, livestreams, etc. YouTube has been identified as an influential platform for spreading radicalized content. Al-Qaeda and similar extremist groups have been linked to using YouTube for recruitment videos and engaging with international media outlets. In a research study published by the American Behavioral Scientist Journal, they researched "whether it is possible to identify a set of attributes that may help explain part of the YouTube algorithm's decision-making process". The results of the study showed that YouTube's algorithm recommendations for extremism content factor into the presence of radical keywords in a video's title. In February 2023, in the case of Gonzalez v. Google, the question at hand is whether or not Google, the parent company of YouTube, is protected from lawsuits claiming that the site's algorithms aided terrorists in recommending ISIS videos to users. Section 230 is known to generally protect online platforms from civil liability for the content posted by its users. Multiple studies have found little to no evidence to suggest that YouTube's algorithms direct attention towards far-right content to those not already engaged with it. === TikTok === TikTok is a platform that recommends videos to a user's 'For You Page' (FYP), making every users' page different. With the nature of the algorithm behind the app, TikTok's FYP has been linked to showing more explicit and radical videos over time based on users' previous interactions on the app. Since TikTok's inception, the app has been scrutinized for misinformation and hate speech as those forms of media usually generate more interactions to the algorithm. Various extremist groups, including jihadist organizations, have utilized TikTok to disseminate propaganda, recruit followers, and incite violence. The platform's algorithm, which recommends content based on user engagement, can expose users to extremist content that aligns with their interests or interactions. As of 2022, TikTok's head of US Security has put out a statement that "81,518,334 videos were removed globally between April – June for violating our Community Guidelines or Terms of Service" to cut back on hate speech, harassment, and misinformation. Studies have noted instances where individuals were radicalized through content encountered on TikTok. For example, in early 2023, Austrian authorities thwarted a plot against an LGBTQ+ pride parade that involved two teenagers and a 20-year-old who were inspired by jihadist content on TikTok. The youngest suspect, 14 years old, had been exposed to videos created by Islamist influencers glorifying jihad. These videos led him to further engagement with similar content, eventually resulting in his involvement in planning an attack. Another case involved the arrest of several teenagers in Vienna, Austria, in 2024, who were planning to carry out a terrorist attack at a Taylor Swift concert. The investigation revealed that some of the suspects had been radicalized online, with TikTok being one of the platforms used to disseminate extremist content that influenced their beliefs and actions. == Self-radicalization == The U.S. Department of Justice defines 'Lone-wolf' (self) terrorism as "someone who acts alone in a terrorist attack without the help or encouragement of a government or a terrorist organization". Through social media outlets on the internet, 'Lone-wolf' terrorism has been on the rise, being linked to algorithmic radicalization. Through echo-chambers on the internet, viewpoints typically seen as radical were accepted and quickly adopted by other extremists. These viewpoints are encouraged by forums, group chats, and social media to reinforce their beliefs. == References in media == === The Social Dilemma === The Social Dilemma is a 2020 docudrama about how algorithms behind social media enables addiction, while possessing abilities to manipulate people's views, emotions, and behavior to spread conspiracy theories and disinformation. The film repeatedly uses buzz words such as 'echo chambers' and 'fake news' to prove psychological manipulation on social media, therefore leading to political manipulation. In the film, Ben falls deeper into a social media addiction as the algorithm found that his social media page has a 62.3% chance of long-term engagement. This leads into more videos on the recommended feed for Ben and he eventually becomes more immersed into propaganda and conspiracy theories, becoming more polarized with each video. == Proposed solutions == === United States: Weakening Section 230 protections === In the Communications Decency Act, Section 230 states t

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  FreePBX Distro

  The FreePBX Distro was a freeware unified communications software system that consisted of FreePBX, a graphical user interface (GUI) for configuring, controlling and managing Asterisk PBX software. The FreePBX Distro included packages that offer VoIP, PBX, Fax, IVR, voice-mail and email functions. The FreePBX Distro Linux distribution was based on CentOS, which maintains binary compatibility with Red Hat Enterprise Linux. FreePBX has contributed to the popularity of Asterisk. As a result of CentOS Linux being discontinued and the last version of CentOS 7 going out of support on June 30, 2024, FreePBX 17 has moved over to and is supported on Debian Linux. FreePBX will no longer be providing a pre-configured FreePBX Distro, but will provide a script to install FreePBX on a fresh install of Debian Linux. In-place migration will not be possible, but will be possible by restoring a backup on the new version from the previous version. As FreePBX 16 will be supported until the release of FreePBX 18, FreePBX on this distribution will still work and be supported, however, there will be no further support for the underlying operating system. == Installation == The Official FreePBX Distro is installed from a ISO image available by web download, that includes the system CentOS, Asterisk, FreePBX GUI and assorted dependencies. This can then either be burned to DVD or written to a USB stick for installation == Support for telephony hardware == The FreePBX Distro has built-in support for cards from multiple vendors, including Digium, OpenVox, Alto, Rhino Equipment, Xorcom and Sangoma. The FreePBX Distro supports a large number of phone models via open-source modules. Supported VoIP phone manufacturers include Algo, AND, AudioCodes, Cisco, Cyberdata, Digium, Grandstream, Mitel/Aastra, Nortel/Avaya, Panasonic, Polycom, Sangoma, Snom, Xorcom and Yealink. == Development == FreePBX made its debut in 2004 as the AMP project (Asterisk Management Portal). The FreePBX Distro was released in 2011 as an turnkey solution for building a PBX using Asterisk, CentOS and FreePBX. FreePBX has over 1 million active production PBXs and over 20,000 new systems added each month. The core telephony engine is Asterisk, as configured by the Open Source FreePBX GUI. The last stable release is FreePBX Distro Stable SNG7-PBX16-64bit-2302-1 based on these main components: FreePBX 16 CentOS 7.8 Asterisk 16, 18, 19 (20 supported by upgrade once installed)

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  International Teletraffic Congress

  The International Teletraffic Congress (ITC) is the first international conference in networking science and practice. It was created in 1955 by Arne Jensen to initially cater to the emerging need to understand and model traffic in telephone networks using stochastic methodologies, and to bring together researchers with these considerations as a common theme. Up through World War II, teletraffic research was done mainly by engineers and mathematicians working in telephone companies. Most of their work was published in local or company journals. In 1955, however, the field acquired a formal, international, institutional structure, with the organization of the first International Teletraffic Congress (ITC). Over the years, it has broaden its scope to address a wide spectrum ranging from the mathematical theory of traffic processes, stochastic system modelling and analysis, traffic and performance measurements, network management, traffic engineering to network capacity planning and cost optimization, including network economics and reliability for various types of networks. ITC served as a forum for all theoretical fundamentals and engineering practices for large-scale deployment and operation of telecommunications networks. Since its inception, ITC witnessed the evolution of communications and networking: the influence of computer science on telecommunication, the advent of the Internet and the massive deployment of mobile communications and optics, the appearance of peer-to-peer networking and social networks, the ever increasing speed and flexibility of new communication technologies, networks, user devices, and applications, and the ever changing operation challenges arising from this development. ITC documented this evolution with contemporary measurement studies, performance analyses of new technologies, recommendations for provisioning and configuration, and greatly contributed to the methodological toolbox of network scientists. Today, with its conferences, specialist seminars, regional seminars, training courses and publications, the ITC aims at a worldwide forum for all questions related to network and service performance, management, and assessment, both present and futuristic. The notion of traffic is broadly used to encompass data traffic from the MAC layer all the way to application traffic in the application layer. The scope of ITC is thus ranging all issues embedding operations, design, planning, economics and performance analysis of current and emerging communication networks and services, to be addressed by applying a variety of tools from different fields, such as Stochastic Processes, Information theory, Control theory, Signal and Processing, Game theory and optimization techniques, Statistical methodologies and Artificial Intelligence techniques. The target audience of such issues is experts from research organizations, universities, equipment vendors and suppliers, network operators, service providers, system integrators and international technical organizations, guaranteeing a well-balanced contribution from theory, application, and practice. The general goal remains to bring researchers and practitioners together toward operational understanding of all types of current and future networks. The ITC is ruled by the International Advisory Council (IAC) which gathers a number of technical experts, from universities and the research arms of key corporations in the industry, from countries having a strong tradition in teletraffic development. The IAC responsibilities are to disseminate information on teletraffic which is of interest for the whole community and: to select the locations of Plenary Congresses and to ensure their high-level technical programme to support Specialist Seminars on specific topics of current interest to promote Regional Seminars for the dissemination of teletraffic concepts in developing countries to facilitate the liaison activity with the ITU through participation in the standardization process and in the Development Programme The technical program and the organization of each ITC event remains within the responsibilities of the hosting country, but with significant IAC support to guarantee that the event is consistent with the quality standards established during the previous congresses. The ITC Plenary Congresses were scheduled tri-annually from 1955 until 1995 when the interval became bi-annual to account for the ever-accelerating development of network technologies, products and services and the associated dramatic increases in network demands. Similarly, to better cover the impact of dramatic changes undergoing in the field of computer and communication systems, networks and usage, it has been decided to hold the Plenary Congress on an annual basis from 2009. == Content == Teletraffic science is the traditional term for all theoretical fundamentals and engineering practices to describe data flows in telecommunication networks, the performance of the usage of network resources, procedures for sizing of resources and engineering the networks for given traffic load and quality of service requirements. For more than 50 years of the 20th century, traffic or teletraffic has been identified primarily with telephone networks. With the huge development of computers, stored program control of network nodes and computer communication, the traditional teletraffic science field naturally extended to computer networks, mobile and wireless/optical networks, and for a wide spectrum of new applications. The convergence between the voice network, the Internet, the television and mobility raised new questions that request new models and tools to be developed. In addition, the development of community networks, home networking, multiple access networking technologies, and the advent of pervasive and ambient communications dictates new challenges to be addressed. Today, ITC addresses the emerging paradigms such as an increasing diversity of distributed applications and services over various media like mobile/optical networks, enabling new markets and economy. ITC has steered the evolutions in communications since its creation in 1955 and remains at the forefront of innovation regarding modeling and performance. The scientific roots of communications traffic are based on the theory of probability and stochastic processes, modelling and performance evaluation. Modelling is the key for the mathematical description and quantitative performance analysis. Traffic flows are described by stochastic processes with complex dependencies which have to be validated by traffic measurements. Modelling also includes operational properties of resource control reflected by service strategies such as queueing disciplines, admission control, and routing. The results of such performance analyses are used for resource dimensioning (sizing), resource management, and network optimization while providing targeted Quality of Service. Teletraffic science is closely related to methods of operation research (queueing theory, optimization, forecasting) and computational sciences (simulation technology distributed systems). In this context, ITC represents a wide community of researchers and practitioners and is regularly organizing events like Congresses, Specialist Seminars and Workshops in order to discuss the latest changes in the modelling, design and performance of communication systems, networks and services. === The evolution of technologies of the 20th century === ITC has been witnessing the change of communication and networking technologies which are reflected in the proceedings and programs of the congresses. The specialist seminars and the motto of the congresses thereby reflect the hot topics of that time and the evolution. Selected topics of the 70's, 80's and 90's were 1998: Traffic Issues related to Multimedia and Nomadic Communications 1995: Traffic Modeling and Measurement in Broadband and Mobile Communications 1990: Broadband Technologies: Architectures, Applications, Control and Performance 1986: ISDN Traffic Issues 1984: Fundamentals of Teletraffic Theory 1977: Modeling of SPC Exchanges and Data Networks === Recent topics in the 21st century === With the rise of the Internet, new networking paradigms and technologies but also new challenges emerged: 2020: Teletraffic in the era of beyond-5G and AI 2019: Networked Systems and Services 2018: Teletraffic in the Smart World 2017: Ubiquitous, software-based, and sustainable networks and services 2016: Digital Connected World 2015: Traffic, Performance and Big Data 2014: Towards a Sustainable World 2013: Energy Efficient and Green Networking 2010: Multimedia Applications - Traffic, Performance and QoE 2009: Network Virtualization - Concepts and Performance 2008: Future Internet Design and Experimental Facilities 2008: Quality of Experience 2002: Internet Traffic Engineering and Traffic Management == Arne Jensen Lifetime Achievement Awards == The Arne Jensen Lifetime A

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  Structural similarity index measure

  The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. It is also used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is a perception-based model that considers image degradation as perceived change in structural information, while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. This distinguishes from other techniques such as mean squared error (MSE) or peak signal-to-noise ratio (PSNR) that instead estimate absolute errors. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects in the visual scene. Luminance masking is a phenomenon whereby image distortions (in this context) tend to be less visible in bright regions, while contrast masking is a phenomenon whereby distortions become less visible where there is significant activity or "texture" in the image. == History == The predecessor of SSIM was called Universal Quality Index (UQI), or Wang–Bovik index, which was developed by Zhou Wang and Alan Bovik in 2001. This evolved, through their collaboration with Hamid Sheikh and Eero Simoncelli, into the current version of SSIM, which was published in April 2004 in the IEEE Transactions on Image Processing. In addition to defining the SSIM quality index, the paper provides a general context for developing and evaluating perceptual quality measures, including connections to human visual neurobiology and perception, and direct validation of the index against human subject ratings. The basic model was developed in the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin and further developed jointly with the Laboratory for Computational Vision (LCV) at New York University. Further variants of the model have been developed in the Image and Visual Computing Laboratory at University of Waterloo and have been commercially marketed. SSIM subsequently found strong adoption in the image processing community and in the television and social media industries. The 2004 SSIM paper has been cited over 50,000 times according to Google Scholar, making it one of the highest cited papers in the image processing and video engineering fields. It was recognized with the IEEE Signal Processing Society Best Paper Award for 2009. It also received the IEEE Signal Processing Society Sustained Impact Award for 2016, indicative of a paper having an unusually high impact for at least 10 years following its publication. Because of its high adoption by the television industry, the authors of the original SSIM paper were each accorded a Primetime Engineering Emmy Award in 2015 by the Television Academy. == Algorithm == The SSIM index is calculated between two windows of pixel values x {\displaystyle x} and y {\displaystyle y} of common size, from corresponding locations in two images to be compared. These SSIM values can be aggregated across the full images by averaging or other variations. === Special-case formula === In one simple special case, further explained in the next section, the SSIM measure between x {\displaystyle x} and y {\displaystyle y} is: SSIM ( x , y ) = ( 2 μ x μ y + c 1 ) ( 2 σ x y + c 2 ) ( μ x 2 + μ y 2 + c 1 ) ( σ x 2 + σ y 2 + c 2 ) {\displaystyle {\hbox{SSIM}}(x,y)={\frac {(2\mu _{x}\mu _{y}+c_{1})(2\sigma _{xy}+c_{2})}{(\mu _{x}^{2}+\mu _{y}^{2}+c_{1})(\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2})}}} with: μ x {\displaystyle \mu _{x}} the pixel sample mean of x {\displaystyle x} ; μ y {\displaystyle \mu _{y}} the pixel sample mean of y {\displaystyle y} ; σ x 2 {\displaystyle \sigma _{x}^{2}} the sample variance of x {\displaystyle x} ; σ y 2 {\displaystyle \sigma _{y}^{2}} the sample variance of y {\displaystyle y} ; σ x y {\displaystyle \sigma _{xy}} the sample covariance of x {\displaystyle x} and y {\displaystyle y} ; c 1 = ( k 1 L ) 2 {\displaystyle c_{1}=(k_{1}L)^{2}} , c 2 = ( k 2 L ) 2 {\displaystyle c_{2}=(k_{2}L)^{2}} two variables to stabilize the division with weak denominator; L {\displaystyle L} the dynamic range of the pixel-values (typically this is 2 # b i t s p e r p i x e l − 1 {\displaystyle 2^{\#bits\ per\ pixel}-1} ); k 1 = 0.01 {\displaystyle k_{1}=0.01} and k 2 = 0.03 {\displaystyle k_{2}=0.03} by default. === General formula and components === The SSIM formula is based on three comparison measurements between the samples of x {\displaystyle x} and y {\displaystyle y} : luminance ( l {\displaystyle l} ), contrast ( c {\displaystyle c} ), and structure ( s {\displaystyle s} ). The individual comparison functions are: l ( x , y ) = 2 μ x μ y + c 1 μ x 2 + μ y 2 + c 1 {\displaystyle l(x,y)={\frac {2\mu _{x}\mu _{y}+c_{1}}{\mu _{x}^{2}+\mu _{y}^{2}+c_{1}}}} c ( x , y ) = 2 σ x σ y + c 2 σ x 2 + σ y 2 + c 2 {\displaystyle c(x,y)={\frac {2\sigma _{x}\sigma _{y}+c_{2}}{\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2}}}} s ( x , y ) = σ x y + c 3 σ x σ y + c 3 {\displaystyle s(x,y)={\frac {\sigma _{xy}+c_{3}}{\sigma _{x}\sigma _{y}+c_{3}}}} The SSIM for each block is then a weighted combination of those comparative measures: SSIM ( x , y ) = l ( x , y ) α ⋅ c ( x , y ) β ⋅ s ( x , y ) γ {\displaystyle {\text{SSIM}}(x,y)=l(x,y)^{\alpha }\cdot c(x,y)^{\beta }\cdot s(x,y)^{\gamma }} Choosing the third denominator stabilizing constant as: c 3 = c 2 / 2 {\displaystyle c_{3}=c_{2}/2} leads to a simplification when combining the c and s components with equal exponents ( β = γ {\displaystyle \beta =\gamma } ), as the numerator of c is then twice the denominator of s, leading to a cancellation leaving just a 2. Setting the weights (exponents) α , β , γ {\displaystyle \alpha ,\beta ,\gamma } to 1, the formula can then be reduced to the special case shown above. === Mathematical properties === SSIM satisfies the identity of indiscernibles, and symmetry properties, but not the triangle inequality or non-negativity, and thus is not a distance function. However, under certain conditions, SSIM may be converted to a normalized root MSE measure, which is a distance function. The square of such a function is not convex, but is locally convex and quasiconvex, making SSIM a feasible target for optimization. === Application of the formula === In order to evaluate the image quality, this formula is usually applied only on luma, although it may also be applied on color (e.g., RGB) values or chromatic (e.g. YCbCr) values. The resultant SSIM index is a decimal value between -1 and 1, where 1 indicates perfect similarity, 0 indicates no similarity, and -1 indicates perfect anti-correlation. For an image, it is typically calculated using a sliding Gaussian window of size 11×11 or a block window of size 8×8. The window can be displaced pixel-by-pixel on the image to create an SSIM quality map of the image. In the case of video quality assessment, the authors propose to use only a subgroup of the possible windows to reduce the complexity of the calculation. === Variants === ==== Multi-scale SSIM ==== A more advanced form of SSIM, called Multiscale SSIM (MS-SSIM) is conducted over multiple scales through a process of multiple stages of sub-sampling, reminiscent of multiscale processing in the early vision system. It has been shown to perform equally well or better than SSIM on different subjective image and video databases. ==== Multi-component SSIM ==== Three-component SSIM (3-SSIM) is a form of SSIM that takes into account the fact that the human eye can see differences more precisely on textured or edge regions than on smooth regions. The resulting metric is calculated as a weighted average of SSIM for three categories of regions: edges, textures, and smooth regions. The proposed weighting is 0.5 for edges, 0.25 for the textured and smooth regions. The authors mention that a 1/0/0 weighting (ignoring anything but edge distortions) leads to results that are closer to subjective ratings. This suggests that edge regions play a dominant role in image quality perception. The authors of 3-SSIM have also extended the model into four-component SSIM (4-SSIM). The edge types are further subdivided into preserved and changed edges by their distortion status. The proposed weighting is 0.25 for all four components. ==== Structural dissimilarity ==== Structural dissimilarity (DSSIM) may be derived from SSIM, though it does not constitute a distance function as the triangle inequality is not necessarily satisfied. DSSIM ( x , y ) = 1 − SSIM ( x , y ) 2 {\displaystyle {\hbox{DSSIM}}(x,y)={\frac {1-{\hbox{SSIM}}(x,y)}{2}}} ==== Video quality metrics and temporal variants ==== It is worth noting that the original vers

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  Alt TikTok

  Alt TikTok (or 2020 Alt) was an online youth subculture and internet community that emerged on TikTok in 2020. Alt TikTok users (also known as alt girls, alt boys, or alt kids) emerged as primarily LGBTQ+ individuals who were in contrast to "Straight TikTok" which was seen as the mainstream and heteronormative side of the platform. The subculture became closely associated with music surrounding the hyperpop scene, particularly 100 gecs and also led to a short-lived fashion style and Internet aesthetic adopted by Generation Z during the COVID-19 lockdowns. Notable artists associated with the movement included Girl in Red, Freddie Dredd, David Shawty, WHOKILLEDXIX, and 645AR. While "alt kid" might imply a general association with traditional alternative fashion, the subculture was more an offshoot of e-girls and e-boys. In 2023, the hashtag #altfashion on TikTok amassed over 1.8 billion views. == History == Around mid-2020, users on TikTok began to group different content on the site into labels like "elite TikTok", "deep TikTok", and "floptok". These categories acted as different "sides of TikTok", deviating from mainstream lip syncing, online trends, and dance videos. Alt TikTok became one of the many subcultural communities to emerge during this period, initially referred to interchangeably with "elite TikTok". The movement quickly identified itself with alternative and queer users, in contrast to "Straight TikTok", also known as the "straight side of TikTok", which was seen as the mainstream and heteronormative side of the platform. Alt TikTok was accompanied by memes with surrealist or supernatural themes (sometimes being described as cursed), such as videos with heavy saturation and humanoid animals. One of the popular videos from Alt TikTok, gaining 18 million likes, shows a llama dancing to a cover of a song from a Russian commercial by the cereal brand Miel Pops, later becoming a viral audio. Some Alt TikTok users personified brands and products in what was referred to as Retail TikTok. In 2020, Rolling Stone described Alt TikTok as "one of the primary countercultures on the app." In 2020, American journalist Taylor Lorenz stated in an article of The New York Times, "Every pop sensation needs its ironic counterpoints. Alt Tiktok gets it done. [...] alt TikTok stars like Mooptopia are mainstays on the more indie side of the app. They aren't the popular crowd, but their cool, quirky content still attracts millions." === Trump rally trolling === In June 2020, alt TikTok and K-pop twitter users coordinated a strategy to ruin a Trump rally in Tulsa, Oklahoma. American politician and activist Alexandria Ocasio-Cortez later saluted the individuals for their "Trump troll". == Alt subculture == In 2020, Alt TikTok was one of many subcultural communities to emerge on TikTok, alongside Deep TikTok (aka DeepTok) and Flop TikTok (aka Floptok). The alt kid subculture emerged from Alt TikTok primarily among young Gen Z women, influenced by online fashion and aesthetics shaped by e-girls and e-boys. The movement was accelerated by the COVID-19 lockdowns, while the subculture itself stood in opposition to mainstream "Straight TikTok" and the VSCO girl movement, primarily adopting aspects of queer and alternative culture. While the phrase might imply a general association with alternative fashion or alternative culture, it is more accurately understood as a specific internet-driven outgrowth of online aesthetic youth subcultures like e-girls and e-boys. The alt subculture's visual style blended influences from goth, punk, emo, and grunge, often expressed through fashion, music taste, and online presence. === Style and music === The style of alt-girls is reminiscent of a myriad of previous alternative fashion trends, often blending these influences with online aesthetics. In 2020, TikTok alt-girls were teens ranging from ages 13 to 16, who tended to wear friendship bracelets, goth boots, Dr. Martens, bunny and frog hats, piercings, and split-dyed hair, as well as iconography lifted from Monster Energy and Hello Kitty. Some alt-girls displayed a love of cosplay, while drawing from Japanese anime and manga, particularly Danganronpa and Haikyu!!, which originally gained traction on the app through Anime TikTok (aka Anitok). Alt TikTok has been noted for being primarily influenced by queer and alternative culture, positioning itself in contrast to "Straight TikTok", which focused on mainstream dances and music. Alt kids frequently intersected with the e-girls and e-boys subculture, in terms of music, style, visual media, and aesthetics. Several musicians and artists were closely associated with the alt subculture, particularly those in the hyperpop scene, while alt tiktok users became important in the wider popularization of artists like 100 gecs. Notable prominent artists associated with Alt Tiktok included Girl in Red, Freddie Dredd, David Shawty, WHOKILLEDXIX, and 645AR, alongside music by YouTubers turned musicians such as Wilbur Soot's "I'm in Love With an E‐Girl" and Corpse Husband's "E-Girls Are Ruining My Life!". == Legacy == In 2020, Pitchfork claimed Alt TikTok as having an influence on wider music trends, stating: "Alt TikTok's music is now a hot zone for major record labels, pushing it even further into the mainstream". After the COVID-19 lockdowns, Alt TikTok, alongside its subculture, fell out of prominence and was taken over by other Gen Z-related internet aesthetics, developments, and online trends.

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  Höhere Graphische Bundes-Lehr- und Versuchsanstalt

  The Höhere Graphische Bundes-Lehr- und Versuchsanstalt (HGBLuVA) ("Higher Federal Institution for Graphic Education and Research"), now commonly known as "die Graphische", founded in 1888 in Vienna, is a vocational college for professions in visual communication and media technology in Austria. == History == === Opening === Originally set up as a photographic research institute by the President of the Photographic Society, the graphic teaching and research institute (GLV) was created through the incorporation of the photographic school (a department for photographic reproduction processes connected to the Salzburg State Building School) and the Hörwarter general drawing school in Vienna. Since its foundation, it has made an important contribution to the establishment and development of the graphic professions. According to a resolution of March 14, 1887, the City Council of Vienna made three floors of the municipal building in Vienna VII, Westbahnstraße 25, available to the former Schottenfelder Realschule for the establishment of a teaching and research institute for photography and reproduction processes. The k. k. Lehr- und Versuchsanstalt für Photographie und Reproductionsverfahren, founded and directed (1888–1923) by Josef Maria Eder, previously of the Technologische Gewerbemuseum (Museum of Applied Technology), for which he established a Section for Photography and Reproduction Techniques, and the Vienna State Trade School where, recently qualified as a university lecturer, he began teaching chemistry and physics in 1881. It opened on March 1, 1888 with 108 students. In the next school year the number of students rose to 174. In 1890, Eder placed a Wothly solar camera (an early means of enlarging negatives) on the roof. In the context of the history of vocational schools and the applied arts, pioneering educational reforms in Austria from the 1870s created institutions like it outside the format of the classical university, it being a special variation on the “state trade school” (“Staats-Gewerbeschule”). Eder based his institution on earlier foreign models such as the Conservatoire des arts et métiers in Paris (founded 1794), that housed a museum of history and technology and hosted with evening lectures and demonstrations, with lectures in photography commencing in 1891. From 1897 onwards the name Graphische Lehr- und Versuchsanstalt came into being . In 1906, Emperor Franz Joseph granted the school the designation “Imperial and Royal” in the title, and the Republic of Austria confirmed this distinction when the school's Federal Chancellery approved the use of the national coat of arms. === The beginnings === The GLV was instituted on August 27, 1887 "by the highest resolution to approve the activation of this teaching and research institute in Vienna on March 1, 1888". The aim of the institute was the “training of specialist photographers, retouchers, collotype printers, photolithographers, etc., the instruction of artists, scholars and technicians who want to learn photography as an auxiliary science, furthermore the testing of equipment, chemicals and the implementation of independent scientific investigations in the areas of Photochemistry and Related Subjects”. The school consisted of two departments; the Institute for Photography and Reproduction Processes and the Research Institute, and in 1891 the Board of Book Printers and Type Founders pointed out the urgent need to add a department for book printers to the school. In 1897 an additional section for the book and illustration trade was opened, the school called "KK Graphische Lehr- und Versuchsanstalt" was then divided into four sections: Section I: Institute for Photography and Reproduction (corresponds to the former Institute for Photography and Reproduction Processes) Section II: College for the book and illustration trade Section III: Research institute for photochemistry and graphic printing processes (corresponds to the original research institute) Section IV: Collections: graphic collection, library and equipment collection The first original lithographs by famous artists such as Luigi Kasimir and Tina Blau are thanks to the special course for lithography and lithography introduced in 1905 and 'algraphy' - a planographic printing process from an aluminum plate instead of the stone used in lithography - was first taught in Austria in 1896 at the GLV. The specialty course for lithography and lithography existed until 1913/14, after which a specialist course for xylography (wood engraving and woodcuts) was offered. In 1908 the graphic arts department was set up on the top floor of the neighbouring house at Westbahnstraße 27 connected by a spiral staircase still in existence in the courtyard at the current location on Leyserstraße. === Women in the graphic teaching and research institute === From 1908 women were also officially admitted. For the period from 1888 to 1918/19, a total of 718 female students at the Graphische are recorded in the largely preserved class lists. Due to changes and new requirements in the job description, the proportion of women continued to grow, so that in some classes it exceeded two thirds. === The Graphics Department === In 1916, the school statute was changed: all-day lessons with photography internship in the 1st and 2nd years as well as training for disabled people were introduced and a drawing school was added. After the First World War, the school was renamed several times: In 1919 the name was "Deutsch-Österreichische Graphische Lehr- und Versuchsanstalt"; changed in 1920 to "Staatliche Graphische Lehr- und Versuchsanstalt" and in 1923 to "Graphic Education and Research Institute". === The school in the time of National Socialism === The "annexation of Austria by Germany" resulted in organisational restructuring: semesters were introduced and the GLV was made a subordinate level of a university of the graphic arts administered in Leipzig. In 1939 the school became a state graphic teaching and research institute . Up to this point, two thirds of all Austrian postage stamps had been designed and engraved in the Graphische. === Post-war period === In 1945 the period of study at the technical school was extended to four years. In 1948, “manual graphics” became “commercial graphics” followed by an honours year. In 1959, a department A was developed: a three-class specialist department for photography with a master class, and a department B: a specialist department for commercial graphics with four classes and an honours year. Through further school reforms, the university entrance qualification was acquired with the completion of the now five-year course and honours qualification. In 1967, due to a lack of space, the Westbahnstrasse was moved to the new Carl Appel building in Leyserstrasse. === The new building, 1963 === On May 22, 1963, the foundation stone of the new campus was laid in the 14th district in the Breitenseer Strasse, Leyserstrasse and Spallartgasse area (Kommandogebäude Theodor Körner). In 1967 the move to the new building began and in 1968 the official opening coincided with the 80th anniversary of the school. In 1963/64 the first year of the five-year high school for reprography and printing technology began. There was also a four-year technical school. With the advent of personal computers and their use in the graphics industry, change comes first in typesetting and later in image processing, and in 1984 the advent of desktop publishing brought a revolution that permanently challenged the distinction between photographer, typesetter, layout artist and printer. In 1988, the Graphische celebrated its 100th anniversary. The rapid development of technology shaped school events in the 1980s, as did the rapid advance of offset printing - albeit at the expense of Letterpress printing. In reproduction technology, scanner technology for the production of colour separations displaced reprography. === Renovation, 2006 === Due to renovation work on the building in Leyserstraße, the management and the photography, multimedia and graphics departments moved to an alternative location in Vienna's first district at Schellinggasse 13. After the work was completed, the school was relocated in February 2008. == Notable teachers and students ==

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  Friending and following

  Friending is the act of adding someone to a list of "friends" on a social networking service. The notion does not necessarily involve the concept of friendship. It is also distinct from the idea of a "fan"—as employed on the WWW sites of businesses, bands, artists, and others—since it is more than a one-way relationship. A "fan" only receives things. A "friend" can communicate back to the person friending. The act of "friending" someone usually grants that person special privileges (on the service) with respect to oneself. On Facebook, for example, one's "friends" have the privilege of viewing and posting to one's "timeline". Following is a similar concept on other social network services, such as Twitter and Instagram, where a person (follower) chooses to add content from a person or page to their newsfeed. Unlike friending, following is not necessarily mutual, and a person can unfollow (stop following) or block another user at any time without affecting that user's following status. The first scholarly definition and examination of friending and defriending (the act of removing someone from one's friend list, also called unfriending) was David Fono and Kate Raynes-Goldie's "Hyperfriendship and beyond: Friends and Social Norms on LiveJournal" from 2005, which identified the use of the term as both a noun and a verb by users of early social network site and blogging platform LiveJournal, which was originally launched in 1999. == Friend/follower count, friend collecting, and multiple accounts == The addition of people to a friend list without regard to whether one actually is their friend is sometimes known as friend whoring. Matt Jones of Dopplr went so far as to coin the expression "friending considered harmful" to describe the problem of focusing upon the friending of more and more people at the expense of actually making any use of a social network. Friend collecting is the adding of hundreds or thousands of friends/followers, a not uncommon order of magnitude on some social sites. As a result, many teen users feel pressured to heavily curate their posts, posting only carefully posed and edited photographs with well-thought-out captions. Some Instagram users will create a second account, known as a Finsta (short for "Fake Instagram"). A Finsta is typically private, and the owner only allows close friends to follow it. Since the follower count is kept down, the posts can be more candid and silly in nature. Users may also create multiple accounts based on their interests. Someone with a personal social media account might be a photographer and maintain a separate account for that. There is risk associated with following large numbers of people: scholars say that social anxiety could be an effect of managing a large social media network, as users can feel jealous and have a "fear of missing out". == Unfriending and unfollowing == Unfriending is the act of removing someone from a friends list. On Facebook, this means the action is unilateral, meaning, the friendship is terminated on both sides. The act of unfriending is often used when one user was flirting and made the other uncomfortable. Unfollowing is a little different. When a user unfollows someone on Instagram or Twitter, it continues a one-sided relationship. Often, the unfollowed user doesn't realize they were unfollowed, so they continue the following. == Social network friending and friendship == There are distinct groups of "friends" that one can friend on a social networking service. The notion of a social network friend does not necessarily embody the concept of friendship. Although terminology has not yet evolved to distinguish the different types of social networking friends, they can be broken into the following three categories. friends who are actually known These are people that may be one's friends or family in real life, with whom one has regular interaction either on-line or off-line. organizational friends These are companies and other organizations who maintain a "friending" relationship as a contacts list. complete strangers These are social networking "friends" with whom one has no relationship at all. Within these categories "friends" can be made up of strong ties, weak existing ties, weak latent ties, and parasocial ties. Strong ties can be made up of close family members and friends where self-disclosure, intimacy and frequent content occur. Weak existing ties can be made up of acquaintances, co-workers and distance relatives with whom the user has inconsistent contact. Weak latent ties can be made up of people within a similar geographical location or profession that can be used as a potential future bridge to other connections. Parasocial ties can be made up of celebrities, public figures and media personas. Human nature is to reciprocate a friending, marking someone as a friend who has marked oneself as a friend. This is a social norm for social networking services. However, this leads to mixing up who is an actual friend, and who is a contact. Tagging someone as a "contact" who has marked one as a "friend" can be perceived as impolite. Other concerns about this issue are treated in Sherry Turkle's Alone Together which analyses many behavioral dynamics in social media friendships. Turkle defines herself as "cautiously optimistic", but expresses concern that distance communications may undermine genuine face-to-face spoken discourses, lessening people's expectations of one another. One social networking service, FriendFeed, allows one to friend someone as a "fake" friend. The person "fake" friended receives the usual notifications for friending, but that person's updates are not received. Gavin Bell, author of Building Social Web Applications, describes this mechanism as "ludicrous". Results from a 2007 survey the Center for the Digital Future stated that only 23% of internet users have at least one virtual friend whom they have only met online. Ideally the number of virtual friends is directly proportional to the use of the Internet, but the same survey showed 20% of heavy-users (more than 3 hours/day) who claimed an average of 8.7% online friends, reported at least one relationship that started virtually and migrated to in-person contact. This results and other concerning issues are included in the book Networked: The New Social Operating System co-written by Lee Rainie and Barry Wellman in 2012. == Ethical considerations == The act of "friending" someone on a social networking service has particular ethical implications for judges in the United States. Judicial codes of conducts in the various states generally incorporate some form of provision that judges should avoid even the appearance of impropriety. Whether this regulates and even prohibits judges "friending" attorneys that appear before them, and law enforcement personnel, has been the subject of some analysis by the judicial ethics panels of the various states. They haven't all agreed on the guidance that they have given to judges: The New York state Judicial Ethics committee in 2009 simply advised judges to employ caution, noting that the issue of "friending" someone on a social networking service is a publicly observable act that has little difference from other public behavior concerns judges already face. The Florida Judicial Ethics Advisory committee in 2009 noted that, judges being normal human beings, it was unavoidable for judges to form friendships without the responsibilities of their job. It prohibited judges from friending any attorneys that appeared before them, whilst allowing friending of those who do not, on the grounds that it may give the appearance to the general public (even if the substance is otherwise) that those attorneys who are friended hold special sway with the judge. A minority opinion of the committee asserted that there is a substantive difference between "friending" on a social networking service and actual friendship, and that the general public, being aware of the norms of social networking services, was capable of drawing this distinction and would not reasonably conclude either a special degree of influence or a violation of the code of judicial conduct. This minority opinion was outnumbered twice in 2009, both in the Judicial Ethics Advisory and in the Florida Supreme Court Judicial Ethics Advisory committee. The South Carolina judicial conduct committee in 2009 permitted judges to friend attorneys and law enforcement personnel, with the proviso that no judicial business should be conducted upon nor discussed via the social networking service. "... a judge should not become isolated from the community in which the judge lives.", the committee stated. The Kentucky Judicial Ethics committee in 2010 took the same position as the minority opinion in Florida. It urged judges to exercise caution, but recognized that the act of friending "does not, in and of itself, indicate the degree or intensity of a judge's relationship with the person who is the 'friend'

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  Product-family engineering

  Product-family engineering (PFE), also known as product-line engineering (PLE), is based on the ideas of "domain engineering" created by the Software Engineering Institute, a term coined by James Neighbors in his 1980 dissertation at University of California, Irvine. Software product lines are quite common in our daily lives, but before a product family can be successfully established, an extensive process has to be followed. This process is known as product-family engineering. Product-family engineering can be defined as a method that creates an underlying architecture of an organization's product platform. It provides an architecture that is based on commonality as well as planned variabilities. The various product variants can be derived from the basic product family, which creates the opportunity to reuse and differentiate on products in the family. Product-family engineering is conceptually similar to the widespread use of vehicle platforms in the automotive industry. Product-family engineering is a relatively new approach to the creation of new products, recently evolving to Model-Based Product Line Engineering (MBPLE), emphasizing the centrality of a model-centric approach in PLE. It focuses on the process of engineering new products in such a way that it is possible to reuse product components and apply variability with decreased costs and time. Product-family engineering is all about reusing components and structures as much as possible, according to the ISO/IEC 26550/2015 and the latest ISO/IEC 26580/2021 that introduced the concept of feature-based Product Line Engineering. Several studies have proven that using a product-family engineering approach for product development can have several benefits. Here is a list of some of them: Higher productivity Higher quality Faster time-to-market Lower labor needs The Nokia case mentioned below also illustrates these benefits. In 2025 the publishing of the book Model-Based Product Line Engineering (MBPLE): The feature-based path to product lines success by Marco Forlingieri, Tim Weilkiens and Hugo Guillermo Chalé-Gongora formalized the foundation of the discipline, including best practices and new industrial cases. == Overall process == The product family engineering process consists of several phases. The three main phases are: Phase 1: Product management Phase 2: Domain engineering Phase 3: Product engineering The process has been modeled on a higher abstraction level. This has the advantage that it can be applied to all kinds of product lines and families, not only software. The model can be applied to any product family. Figure 1 (below) shows a model of the entire process. Below, the process is described in detail. The process description contains elaborations of the activities and the important concepts being used. All concepts printed in italic are explained in Table 1. === Phase 1: product management === The first phase is the starting up of the whole process. In this phase some important aspects are defined especially with regard to economic aspects. This phase is responsible for outlining market strategies and defining a scope, which tells what should and should not be inside the product family. ==== Evaluate business visioning ==== During this first activity all context information relevant for defining the scope of the product line is collected and evaluated. It is important to define a clear market strategy and take external market information into account, such as consumer demands. The activity should deliver a context document that contains guidelines, constraints and the product strategy. ==== Define product line scope ==== Scoping techniques are applied to define which aspects are within the scope. This is based upon the previous step in the process, where external factors have been taken into account. The output is a product portfolio description, which includes a list of current and future products and also a product roadmap. It can be argued whether phase 1, product management, is part of the product-family-engineering process, because it could be seen as an individual business process that is more focused on the management aspects instead of the product aspect. However phase 2 needs some important input from this phase, as a large piece of the scope is defined in this phase. So from this point of view it is important to include the product-management phase (phase 1) into the entire process as a base for the domain-engineering process. === Phase 2: domain engineering === During the domain-engineering phases, the variable and common requirements are gathered for the whole product line. The goal is to establish a reusable platform. The output of this phase is a set of common and variable requirements for all products in the product line. ==== Analyze domain requirements ==== This activity includes all activities for analyzing the domain with regard to concept requirements. The requirements are categorized and split up into two new activities. The output is a document with the domain analysis. As can be seen in Figure 1 the process of defining common requirements is a parallel process with defining variable requirements. Both activities take place at the same time. ==== Define common requirements ==== Includes all activities for eliciting and documenting the common requirements of the product line, resulting in a document with reusable common requirements. ==== Define variable requirements ==== Includes all activities for eliciting and documenting the variable requirements of the product line, resulting in a document with variable requirements. ==== Design domain ==== This process step consists of activities for defining the reference architecture of the product line. This generates an abstract structure for all products in the product line. ==== Implement domain ==== During this step a detailed design of the reusable components and the implementation of these components are created. ==== Test domain ==== Validates and verifies the reusability of components. Components are tested against their specifications. After successful testing of all components in different use cases and scenarios, the domain engineering phase has been completed. === Phase 3: product engineering === In the final phase a product X is being engineered. This product X uses the commonalities and variability from the domain engineering phase, so product X is being derived from the platform established in the domain engineering phase. It basically takes all common requirements and similarities from the preceding phase plus its own variable requirements. Using the base from the domain engineering phase and the individual requirements of the product engineering phase a complete and new product can be built. After the product has been fully tested and approved, the product X can be delivered. ==== Define product requirements ==== Developing the product requirements specification for the individual product and reuse the requirements from the preceding phase. ==== Design product ==== All activities for producing the product architecture. Makes use of the reference architecture from the step "design domain", it selects and configures the required parts of the reference architecture and incorporates product specific adaptations. ==== Build product ==== During this process the product is built, using selections and configurations of the reusable components. ==== Test product ==== During this step the product is verified and validated against its specifications. A test report gives information about all tests that were carried out, this gives an overview of possible errors in the product. If the product in the next step is not accepted, the process will loop back to "build product", in Figure 1 this is indicated as "[unsatisfied]". ==== Deliver and support product ==== The final step is the acceptance of the final product. If it has been successfully tested and approved to be complete, it can be delivered. If the product does not satisfy to the specifications, it has to be rebuilt and tested again. The next figure shows the overall process of product-family engineering as described above. It is a full process overview with all concepts attached to the different steps. == Process data diagram == On the left side the entire process from the top to bottom has been drawn. All activities on the left side are linked to the concepts on the right side through dotted lines. Every concept has a number, which reflects the association with other concepts. == List of concepts == Below the list with concepts will be explained. Most concept definitions are extracted from Pohl, Bockle, & Linden (2005) and also some new definitions have been added. Table 1: List of concepts == Example == There are some good examples of the use of product family engineering, which were quite successful. The abstract model of product family engineering allows different kinds of uses, most of them are related to the consumer electronics m

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  Hype (marketing)

  Hype in marketing is a strategy of using extreme publicity. Hype as a modern marketing strategy is closely associated with social media. Marketing through hype often uses artificial scarcity to induce demand. Consumers of hyped products often participate as a form of conspicuous consumption to signify characteristics about themselves. Hype allows brands to promote their image above the actual quality of the product. Streetwear brands have collaborated with luxury fashion to justify charging premium prices for their goods. As an example, fashion label Vetements used social media channels to promote a limited-edition hoodie which sold 500 units in hours, recording sales of €445,000. When hype marketing is used to drive demand for limited-edition goods, consumers sometimes attempt resell those good on secondary markets for a profit (comparable to ticket scalping). The resale market is a $24 billion industry. == Method == Luxury brands may release products as a collaborate with ready-made garment brands as a way to build hype. Collaborations have been used by some luxury brands to circumvent fast fashion brands copying their designs. NYU Professor Adam Alter says that for an established brand to create a scarcity frenzy, they need to release a limited number of different products, frequently. Hype is often built via Pop-up retail. Comme des Garçons was one of the first to use this strategy, leasing a short-term vacant shop solved the storage problems of releasing product for quick sale. Hype campaigns also rely on influencer marketing, where brands enlist creators whose parasocial relationships with their followers help convert audience attention into demand for limited releases. == In popular culture == The term 'hypebeast' has been coined to define consumers vulnerable to hype marketing. The origins of the term come from the Hong Kong-based company Hypebeast. The behaviours of the hypebeast define hype marketing; the purchase of popular goods they can't afford to impress others. Hype also manifests itself in queues with brands often retailing hyped products through pop-up stores. Many luxury brands release hyped products via their online shop. This has led to the creation of companies that allow consumers to use bots to guarantee or improve their chances of purchasing a limited-edition product.

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  Blue check

  A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official

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