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Hierarchical control system

A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system. == Overview == A human-built system with complex behavior is often organized as a hierarchy. For example, a command hierarchy has among its notable features the organizational chart of superiors, subordinates, and lines of organizational communication. Hierarchical control systems are organized similarly to divide the decision making responsibility. Each element of the hierarchy is a linked node in the tree. Commands, tasks and goals to be achieved flow down the tree from superior nodes to subordinate nodes, whereas sensations and command results flow up the tree from subordinate to superior nodes. Nodes may also exchange messages with their siblings. The two distinguishing features of a hierarchical control system are related to its layers. Each higher layer of the tree operates with a longer interval of planning and execution time than its immediately lower layer. The lower layers have local tasks, goals, and sensations, and their activities are planned and coordinated by higher layers which do not generally override their decisions. The layers form a hybrid intelligent system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning. A hierarchical task network is a good fit for planning in a hierarchical control system. Besides artificial systems, an animal's control systems are proposed to be organized as a hierarchy. In perceptual control theory, which postulates that an organism's behavior is a means of controlling its perceptions, the organism's control systems are suggested to be organized in a hierarchical pattern as their perceptions are constructed so. == Control system structure == The accompanying diagram is a general hierarchical model which shows functional manufacturing levels using computerised control of an industrial control system. Referring to the diagram; Level 0 contains the field devices such as flow and temperature sensors, and final control elements, such as control valves Level 1 contains the industrialised Input/Output (I/O) modules, and their associated distributed electronic processors. Level 2 contains the supervisory computers, which collate information from processor nodes on the system, and provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and monitoring targets Level 4 is the production scheduling level. == Applications == === Manufacturing, robotics and vehicles === Among the robotic paradigms is the hierarchical paradigm in which a robot operates in a top-down fashion, heavy on planning, especially motion planning. Computer-aided production engineering has been a research focus at NIST since the 1980s. Its Automated Manufacturing Research Facility was used to develop a five layer production control model. In the early 1990s DARPA sponsored research to develop distributed (i.e. networked) intelligent control systems for applications such as military command and control systems. NIST built on earlier research to develop its Real-Time Control System (RCS) and Real-time Control System Software which is a generic hierarchical control system that has been used to operate a manufacturing cell, a robot crane, and an automated vehicle. In November 2007, DARPA held the Urban Challenge. The winning entry, Tartan Racing employed a hierarchical control system, with layered mission planning, motion planning, behavior generation, perception, world modelling, and mechatronics. === Artificial intelligence === Subsumption architecture is a methodology for developing artificial intelligence that is heavily associated with behavior based robotics. This architecture is a way of decomposing complicated intelligent behavior into many "simple" behavior modules, which are in turn organized into layers. Each layer implements a particular goal of the software agent (i.e. system as a whole), and higher layers are increasingly more abstract. Each layer's goal subsumes that of the underlying layers, e.g. the decision to move forward by the eat-food layer takes into account the decision of the lowest obstacle-avoidance layer. Behavior need not be planned by a superior layer, rather behaviors may be triggered by sensory inputs and so are only active under circumstances where they might be appropriate. Reinforcement learning has been used to acquire behavior in a hierarchical control system in which each node can learn to improve its behavior with experience. James Albus, while at NIST, developed a theory for intelligent system design named the Reference Model Architecture (RMA), which is a hierarchical control system inspired by RCS. Albus defines each node to contain these components. Behavior generation is responsible for executing tasks received from the superior, parent node. It also plans for, and issues tasks to, the subordinate nodes. Sensory perception is responsible for receiving sensations from the subordinate nodes, then grouping, filtering, and otherwise processing them into higher level abstractions that update the local state and which form sensations that are sent to the superior node. Value judgment is responsible for evaluating the updated situation and evaluating alternative plans. World Model is the local state that provides a model for the controlled system, controlled process, or environment at the abstraction level of the subordinate nodes. At its lowest levels, the RMA can be implemented as a subsumption architecture, in which the world model is mapped directly to the controlled process or real world, avoiding the need for a mathematical abstraction, and in which time-constrained reactive planning can be implemented as a finite-state machine. Higher levels of the RMA however, may have sophisticated mathematical world models and behavior implemented by automated planning and scheduling. Planning is required when certain behaviors cannot be triggered by current sensations, but rather by predicted or anticipated sensations, especially those that come about as result of the node's actions.
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Digital citizen

The term digital citizen is used with different meanings. According to the definition provided by Karen Mossberger, one of the authors of Digital Citizenship: The Internet, Society, and Participation, digital citizens are "those who use the internet regularly and effectively". In this sense, a digital citizen is a person who uses information technology (IT) to engage in society, politics, and government. More recent elaborations of the concept define digital citizenship as the self-enactment of people’s role in society through the use of digital technologies, stressing the empowering and democratizing characteristics of the citizenship idea. These theories aim at taking into account the ever-increasing datafication of contemporary societies (symbolically linked to the Snowden leaks), which has called into question the meaning of “being (digital) citizens in a datafied society”. This condition is also referred to as the “algorithmic society”, characterised by the increasing datafication of social life and the pervasive presence of surveillance practices – see surveillance and surveillance capitalism, the use of artificial intelligence, and Big Data. Datafication presents crucial challenges for the very notion of citizenship, so that data collection can no longer be seen as an issue of privacy alone so that:We cannot simply assume that being a citizen online already means something (whether it is the ability to participate or the ability to stay safe) and then look for those whose conduct conforms to this meaning Instead, the idea of digital citizenship shall reflect the idea that we are no longer mere “users” of technologies since they shape our agency both as individuals and as citizens. Digital citizenship refers to the responsible and respectful use of technology to engage online, evaluate information, and protect human rights. It encompasses skills for communication, collaboration, empathy, privacy protection, and security to prevent data breaches and identity theft. == Digital citizenship in the "algorithmic society" == In the context of the algorithmic society, the question of digital citizenship "becomes one of the extents to which subjects are able to challenge, avoid or mediate their data double in this datafied society”. These reflections put the emphasis on the idea of the digital space (or cyberspace) as a political space where the respect of fundamental rights of the individual shall be granted (with reference both to the traditional ones as well as to new specific rights of the internet [see “digital constitutionalism”]) and where the agency and the identity of the individuals as citizens is at stake. This idea of digital citizenship is thought to be not only active but also performative, in the sense that “in societies that are increasingly mediated through digital technologies, digital acts become important means through which citizens create, enact and perform their role in society.” In particular, for Isin and Ruppert this points towards an active meaning of (digital) citizenship based on the idea that we constitute ourselves as digital citizen by claiming rights on the internet, either by saying or by doing something. == Types of digital participation == People who characterize themselves as digital citizens often use IT extensively—creating blogs, using social networks, and participating in online journalism. Although digital citizenship begins when any child, teen, or adult signs up for an email address, posts pictures online, uses e-commerce to buy merchandise online, and/or participates in any electronic function that is B2B or B2C, the process of becoming a digital citizen goes beyond simple internet activity. According to Thomas Humphrey Marshall, a British sociologist known for his work on social citizenship, a primary framework of citizenship comprises three different traditions: liberalism, republicanism, and ascriptive hierarchy. Within this framework, the digital citizen needs to exist in order to promote equal economic opportunities and increase political participation. In this way, digital technology helps to lower the barriers to entry for participation as a citizen within a society. They also have a comprehensive understanding of digital citizenship, which is the appropriate and responsible behavior when using technology. Since digital citizenship evaluates the quality of an individual's response to membership in a digital community, it often requires the participation of all community members, both visible and those who are less visible. A large part in being a responsible digital citizen encompasses digital literacy, etiquette, online safety, and an acknowledgement of private versus public information. The development of digital citizen participation can be divided into two main stages. The first stage is through information dissemination, which includes subcategories of its own: static information dissemination, characterized largely by citizens who use read-only websites where they take control of data from credible sources in order to formulate judgments or facts. Many of these websites where credible information may be found are provided by the government. dynamic information dissemination, which is more interactive and involves citizens as well as public servants. Both questions and answers can be communicated, and citizens have the opportunity to engage in question-and-answer dialogues through two-way communication platforms The second stage of digital citizen participation is citizen deliberation, which evaluates what type of participation and role that they play when attempting to ignite some sort of policy change. static citizen participants can play a role by engaging in online polls as well as through complaints and recommendations sent up, mainly toward the government who can create changes in policy decisions. dynamic citizen participants can deliberate amongst others on their thoughts and recommendations in town hall meetings or various media sites. One potential advantage of online participation through digital citizenship is increased social inclusion. In a report on civic engagement, citizen-powered democracy can be initiated either through information shared through the web, direct communication signals made by the state toward the public, and social media tactics from both private and public companies. In fact, it was found that the community-based nature of social media platforms allow individuals to feel more socially included and informed about political issues that peers have also been found to engage with, otherwise known as a "second-order effect." Understanding strategic marketing on social media would further explain social media customers’ participation. Two types of opportunities rise as a result, the first being the ability to lower barriers that can make exchanges much easier. In addition, they have the chance to participate in transformative disruption, giving people who have a historically lower political engagement to mobilize in a much easier and convenient fashion. Nonetheless, there are several challenges that face the presence of digital technologies in political participation. Both current as well as potential challenges can create significant risks for democratic processes. Not only is digital technology still seen as relatively ambiguous, it was also seen to have "less inclusivity in democratic life." Demographic groups differ considerably in the use of technology, and thus, one group could potentially be more represented than another as a result of digital participation. Another primary challenge consists in the ideology of a "filter bubble" effect. Alongside a tremendous spread of false information, internet users could reinforce existing prejudices and assist in polarizing disagreements in the public sphere. This can lead to misinformed voting and decisions based on exposure rather than on pure knowledge. A communication technology director, Van Dijk, stated, "Computerized information campaigns and mass public information systems have to be designed and supported in such a way that they help to narrow the gap between the 'information rich' and 'information poor' otherwise the spontaneous development of ICT will widen it." Access and equivalent amounts of knowledge behind digital technology must be equivalent in order for a fair system to put into place. Alongside a lack of evidenced support for technology that can be proven to be safe for citizens, the OECD has identified five struggles for the online engagement of citizens: Scale: To what extent can a society allow every individual's voice to be heard, but also not be lost in the mass debate? This can be extremely challenging for the government, which may not effectively know how to listen and respond to each individual contribution. Capacity: How can digital technology offer citizens more information on public policy-making? The opportunity for citizens to debate with one another is lacking for acti
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Amplified conference

An amplified conference is a conference or similar event in which the talks and discussions at the conference are 'amplified' through use of networked technologies in order to extend the reach of the conference deliberations. The term was originally coined by Lorcan Dempsey in a blog post. The term is now widely used within the academic and research community with Wankel proposing the following definition: The extension of a physical event (or a series of events) through the use of social media tools for expanding access to (aspects of) the event beyond physical and temporal bounds. Such amplification takes place in the context of intent to make the most of the intellectual content, discussion, networking, and discovery initiated by the event through the process of sharing with co-attendees, colleagues, friends and wider informed publics. A paper by Haider and others illustrates how amplified conferences are becoming mainstream in a discussion on "how social media have been employed as part of the project, particularly around event amplification". As described by Guy in the Ariadne ejournal the term is not a prescriptive one, but rather describes a pattern of behaviors which initially took place at IT and Web-oriented conferences once WiFi networks started to become available at conference venues and delegates started to bring with them networked devices such as laptops and, more recently, PDAs and mobile phones. == Different Approaches to 'Amplification' of Conferences == There are a number of ways in which conferences can be amplified through use of networked technologies: Amplification of the audiences' voice: Prior to the availability of real time chat technologies at events (whether use of IRC, Twitter, instant messaging clients, etc.) it was only feasible to discuss talks with immediate neighbours, and even then this may be considered rude. Amplification of the speaker's talk: The availability of video and audio-conferencing technologies make it possible for a speaker to be heard by an audience which isn't physically present at the conference. Although use of video technologies has been available to support conferences for some time, this has normally been expensive and require use of dedicated video-conferencing technologies. However the availability of lightweight desktop tools make it much easier to deploy such technologies, without even, requiring the involvement of conference organisers. Amplification across time: Video and audio technologies can also be used to allow a speaker's talk to be made available after the event, with use of podcasting or videocasting technologies allowing the talks to be easily syndicated to mobile devices as well as accessed on desktop computers. Amplification of the speaker's slides: The popularity of global repository services for slides, such as SlideShare, enable the slides used by a speaker to be more easily found, embedded on other Web sites and commented upon, in ways that were not possible when the slides, if made available at all, were only available on a conference Web site. Amplification of feedback to the speaker: Micro-blogging technologies, such as Twitter, are being used not only as a discussion channel for conference participants but also as a way of providing real-time feedback to a speaker during a talk. We are also now seeing dedicated microblogging technologies, such as Coveritlive and Scribblelive, being developed which aim to provide more sophisticated 'back channels' for use at conferences. Amplification of a conference's collective memory: The popularity of digital cameras and the photographic capabilities of many mobile phones is leading to many photographs being taken at conferences. With such photographs often being uploaded to popular photographic sharing services, such as Flickr, and such collections being made more easy to discover through agreed use of tags, we are seeing amplification of the memories of an event though the sharing of such resources. The ability of such photographic resources to be 'mashed up' with, say, accompanying music, can similarly help to enrich such collective experiences. Amplification of the learning: The ability to be able to follow links to resources and discuss the points made by a speaker during a talk can enrich the learning which takes place at an event, as described by Shabajee's article on "'Hot' or Not? Welcome to real-time peer review" published in the Times Higher Education Supplement in May 2003. Long term amplification of conference outputs: The availability in a digital format of conference resources, including 'official' resources such as slides, video and audio recordings, etc. which have been made by the conference organisers with the approval of speakers, together with more nebulous resources such as archives of conference back channels, and photographs and unofficial recordings taken at the event may help to provide a more authentic record of an event, which could potentially provide a valuable historical record. The amplification of conferences can be viewed as an example of how new technologies are altering standard practice. By using these techniques a different type of interaction is created at the conference itself, but also the boundaries around the conference can be seen as permeable, with remote participants engaging in discussion. An amplified conference also provides a considerably altered archive compared with a 'traditional' one. For the latter, the printed proceedings will be the main record, but for an amplified event this record is distributed across many media and takes in a wider range of content types, including the papers, videos of the presentations (for example on YouTube), the slides (e.g. on Slideshare), photos of the event (Flickr), interaction between participants (Twitter), reflections and comments (blogs), etc. The amplified conference represents an example of changing practice in digital scholarship.
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DiscoVision

DiscoVision is the name of several things related to the video LaserDisc format. It was the original name of the "Reflective Optical Videodisc System" format later known as "LaserVision" or LaserDisc. == Description == MCA DiscoVision, Inc. was a division of entertainment giant MCA (Music Corporation of America), established in 1969 to develop and sell an optical videodisc system. MCA released discs pressed in Carson and Costa Mesa, California on the DiscoVision label from the format's Atlanta, Georgia launch in 1978 to 1982 and the release of the film The Four Seasons. DiscoVision titles included films from Universal Pictures, Paramount Pictures, Warner Bros. Pictures, and Disney content. Agreements were made with Columbia Pictures and United Artists, though no discs were released on the DiscoVision label from either studio. Most of these companies later established their own labels for the format, the first being Paramount with a dozen movies released on the Paramount Home Video label in the summer of 1981. The successor to MCA DiscoVision, DiscoVision Associates (DVA), was the result of a partnership between IBM and MCA. It was hoped that the merger would provide the basis for improvement of the quality of DiscoVision pressings, but no appreciable improvement ever took hold. In 1981, responsibility for the laser videodisc was sold to Pioneer Electronic Corporation, after MCA Discovision had previously started a partnership in 1977 with Pioneer, Universal Pioneer, to produce the Pioneer PR-7820 player (the first industrial model of DiscoVision player from 1978), as well as establishing disc pressing plants in Japan. As part of the partnership, Pioneer, in association with MCA, had a disc replication facility in Kofu, Japan that produced discs. Some of the last DiscoVision label discs were manufactured by Pioneer in Japan. In the same year, MCA discontinued their DiscoVision branding, due to the sale of the technology to Pioneer (who then rebranded the format as LaserDisc) and in turn rebranded their laserdisc releases, now fabricated by Pioneer, under the MCA Videodisc banner; this was changed to the "MCA Home Video" name for both its VHS and videodisc releases. Some of DiscoVision's technical staff went on to form MCA Video Games, in an effort to produce video game cartridges. DiscoVision Associates later evolved into a patent holding company which manages and licenses intellectual property related to LaserDisc, Compact Disc, and optical disc technologies, as well as other non-disc related fields. In 1989, Pioneer acquired DiscoVision Associates where it continues to license its technologies independently. As the portfolio of patent expired, the presence of DiscoVision became less visible. However, it established the success of a patent holding company, which other companies are stimulated to generate royalty income from their own patent portfolio.
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Text-to-video model

A text-to-video model is a form of generative artificial intelligence that uses a natural language description as input to produce a video relevant to the input text. Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models. == Models == There are different models, including open source models. Chinese-language input CogVideo is the earliest text-to-video model "of 9.4 billion parameters" to be developed, with its demo version of open source codes first presented on GitHub in 2022. That year, Meta Platforms released a partial text-to-video model called "Make-A-Video", and Google's Brain (later Google DeepMind) introduced Imagen Video, a text-to-video model with 3D U-Net. === 2023 === In February 2023, Runway released Gen-1 and Gen-2, among the first commercially available text-to-video and video-to-video models accessible to the public through a web interface. Gen-1, initially released as a video-to-video model, allowed users to transform existing video footage using text or image prompts. Gen-2, introduced in March 2023 and made publicly available in June 2023, added text-to-video capabilities, enabling users to generate videos from text prompts alone. In March 2023, a research paper titled "VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation" was published, presenting a novel approach to video generation. The VideoFusion model decomposes the diffusion process into two components: base noise and residual noise, which are shared across frames to ensure temporal coherence. By utilizing a pre-trained image diffusion model as a base generator, the model efficiently generated high-quality and coherent videos. Fine-tuning the pre-trained model on video data addressed the domain gap between image and video data, enhancing the model's ability to produce realistic and consistent video sequences. In the same month, Adobe introduced Firefly AI as part of its features. === 2024 === In January 2024, Google announced development of a text-to-video model named Lumiere which is anticipated to integrate advanced video editing capabilities. Matthias Niessner and Lourdes Agapito at AI company Synthesia work on developing 3D neural rendering techniques that can synthesise realistic video by using 2D and 3D neural representations of shape, appearances, and motion for controllable video synthesis of avatars. In June 2024, Luma Labs launched its Dream Machine video tool. That same month, Kuaishou extended its Kling AI text-to-video model to international users. In July 2024, TikTok owner ByteDance released Jimeng AI in China, through its subsidiary, Faceu Technology. By September 2024, the Chinese AI company MiniMax debuted its video-01 model, joining other established AI model companies like Zhipu AI, Baichuan, and Moonshot AI, which contribute to China's involvement in AI technology. In December 2024 Lightricks launched LTX Video as an open source model. === 2025 === Alternative approaches to text-to-video models include Google's Phenaki, Hour One, Colossyan, Runway's Gen-3 Alpha, and OpenAI's Sora, Several additional text-to-video models, such as Plug-and-Play, Text2LIVE, and TuneAVideo, have emerged. FLUX.1 developer Black Forest Labs has announced its text-to-video model SOTA. Google was preparing to launch a video generation tool named Veo for YouTube Shorts in 2025. In May 2025, Google launched the Veo 3 iteration of the model. It was noted for its impressive audio generation capabilities, which were a previous limitation for text-to-video models. In July 2025 Lightricks released an update to LTX Video capable of generating clips reaching 60 seconds, and in October 2025 it released LTX-2, with audio capabilities built in. === 2026 === In February 2026, ByteDance released Seedance 2.0, it was noted for its impressive realistic generation, motion and camera control and 15 second generation, however the model faced huge critiscism from Motion Picture Association for copyright infringement. After viewing a viral clip of a fight between actors Brad Pitt and Tom Cruise, Rhett Reese, who is the co-writer of Deadpool & Wolverine and Zombieland announced that on social media "I hate to say it. It’s likely over for us," further stating that "In next to no time, one person is going to be able to sit at a computer and create a movie indistinguishable from what Hollywood now releases." == Architecture and training == There are several architectures that have been used to create text-to-video models. Similar to text-to-image models, these models can be trained using Recurrent Neural Networks (RNNs) such as long short-term memory (LSTM) networks, which has been used for Pixel Transformation Models and Stochastic Video Generation Models, which aid in consistency and realism respectively. An alternative for these include transformer models. Generative adversarial networks (GANs), Variational autoencoders (VAEs), — which can aid in the prediction of human motion — and diffusion models have also been used to develop the image generation aspects of the model. Text-video datasets used to train models include, but are not limited to, WebVid-10M, HDVILA-100M, CCV, ActivityNet, and Panda-70M. These datasets contain millions of original videos of interest, generated videos, captioned-videos, and textual information that help train models for accuracy. Text-video datasets used to train models include, but are not limited to PromptSource, DiffusionDB, and VidProM. These datasets provide the range of text inputs needed to teach models how to interpret a variety of textual prompts. The video generation process involves synchronizing the text inputs with video frames, ensuring alignment and consistency throughout the sequence. This predictive process is subject to decline in quality as the length of the video increases due to resource limitations. The Will Smith Eating Spaghetti test is a benchmark for models. == Limitations == Despite the rapid evolution of text-to-video models in their performance, a primary limitation is that they are very computationally heavy which limits its capacity to provide high quality and lengthy outputs. Additionally, these models require a large amount of specific training data to be able to generate high quality and coherent outputs, which brings about the issue of accessibility. Moreover, models may misinterpret textual prompts, resulting in video outputs that deviate from the intended meaning. This can occur due to limitations in capturing semantic context embedded in text, which affects the model's ability to align generated video with the user's intended message. Various models, including Make-A-Video, Imagen Video, Phenaki, CogVideo, GODIVA, and NUWA, are currently being tested and refined to enhance their alignment capabilities and overall performance in text-to-video generation. Another issue with the outputs is that text or fine details in AI-generated videos often appear garbled, a problem that stable diffusion models also struggle with. Examples include distorted hands and unreadable text. == Ethics == The deployment of text-to-video models raises ethical considerations related to content generation. These models have the potential to create inappropriate or unauthorized content, including explicit material, graphic violence, misinformation, and likenesses of real individuals without consent. Ensuring that AI-generated content complies with established standards for safe and ethical usage is essential, as content generated by these models may not always be easily identified as harmful or misleading. The ability of AI to recognize and filter out NSFW or copyrighted content remains an ongoing challenge, with implications for both creators and audiences. == Impacts and applications == Text-to-video models offer a broad range of applications that may benefit various fields, from educational and promotional to creative industries. These models can streamline content creation for training videos, movie previews, gaming assets, and visualizations, making it easier to generate content. During the Russo-Ukrainian war, fake videos made with artificial intelligence were created as part of a propaganda war against Ukraine and shared in social media. These included depictions of children in the Ukrainian Armed Forces, fake ads targeting children encouraging them to denounce critics of the Ukrainian government, or fictitious statements by Ukrainian President Volodymyr Zelenskyy about the country's surrender, among others. === Movies === Kaur vs Kore is the first Indian feature film made using generative AI which features dual role for the AI character of Sunny Leone, set to release in 2026. Chiranjeevi Hanuman – The Eternal is an Indian movie made entirely using Generative AI created by Vijay Subramaniam which is set for theatrical release in 2026. The movie was widely criticised by the Film makers in the Bollywood industr
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Artificial Intelligence for Digital Response

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

Digital goods or e-goods are intangible goods that exist in digital form. Examples are Wikipedia articles; digital media, such as e-books, downloadable music, internet radio, internet television and streaming media; fonts, logos, photos and graphics; digital subscriptions; online ads (as purchased by the advertiser); internet coupons; electronic tickets; electronically treated documentation in many different fields; downloadable software (Digital Distribution) and mobile apps; cloud-based applications and online games; virtual goods used within the virtual economies of online games and communities; community access; workbooks; worksheets; planners; e-learning (online courses); webinars, video tutorials, blog posts; cards; patterns; website themes and templates. == Legal concerns about digital goods == Special legal concerns regarding digital goods include copyright infringement and taxation. Also the question of the ownership (versus licensed use or service only) of purely digital goods is not finally resolved. For instance, the software installers of the digital software distributor gog.com are technically independent to the account but are still subject to the EULA, where a "licensed, not sold" formulation is used. Therefore, it is not clear if the software can be legally used after a hypothetical loss of the account; a question which was also raised before in practice for the similar service Steam. In July 2012, the European Court of Justice ruled in the case UsedSoft GMbH v. Oracle International Corp. that the sale of a software product, either through a physical support or download, constituted a transfer of ownership in EU law, thus the first sale doctrine applies; the ruling thereby breaks the "licensed, not sold" legal theory, but leaves open numerous questions. Therefore, it is also permissible to resell software licenses even if the digital good has been downloaded directly from the Internet, as the first-sale doctrine applied whenever software was originally sold to a customer for an unlimited amount of time, thus prohibiting any software maker from preventing the resale of their software by any of their legitimate owners. The court requires that the previous owner must no longer be able to use the licensed software after the resale, but finds that the practical difficulties in enforcing this clause should not be an obstacle to authorizing resale, as they are also present for software which can be installed from physical supports, where the first-sale doctrine is in force. In several cases, content providers have faced criticism for revoking access to digital goods due to expired licenses or the discontinuation of a product, such as ebooks (which resulted in a lawsuit against Amazon.com, Inc.), digital video (with Sony Interactive Entertainment revoking access to purchased StudioCanal content from its now-defunct PlayStation video store; a similar move involving Warner Bros. Discovery content was averted by an updated license agreement), and video games (such as Ubisoft discontinuing and revoking access to its game The Crew without providing refunds or the ability to redownload the game) In September 2024, the U.S. state of California implemented a consumer protection law that prohibits the use of terms such as "buy" or "purchase" during transactions involving digital goods if there is no way to obtain the purchases in a manner that cannot be revoked by the seller (such as allowing it to be downloaded for permanent, offline access), and requires a disclaimer to be displayed to the customer at the time of purchase.
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Group (online social networking)

A group (often termed as a community, e-group or club) is a feature in many social networking services which allows users to create, post, comment to and read from their own interest- and niche-specific forums, often within the realm of virtual communities. Groups, which may allow for open or closed access, invitation and/or joining by other users outside the group, are formed to provide mini-networks within the larger, more diverse social network service. Much like electronic mailing lists, they are also owned and maintained by owners, moderators, or managers, who can edit posts to discussion threads and regulate member behavior within the group. However, unlike traditional Internet forums and mailing lists, groups in social networking services allow owners and moderators alike to share account credentials between groups without having to log in to every group. == History == The rise of the World Wide Web resulted in an expansion of the varieties of methods for communication on the Internet, much of which was limited in the 1980s to discussion in newsgroups, BBS and chat rooms. While the initial rise of web-based mass communication took place in the form of early Internet forums in the mid-1990s, a few services such as MSN Groups, Yahoo! Groups and eGroups pioneered the combination of web-based mailing list archives with user profiles; by 2000, such services doubled as full-fledged mailing lists and Internet forums, allowing users to create an extremely large variety of discussion and networking mediums with comparatively sparse thresholds of complexity. Further features included chat rooms (often Java-based), image and video galleries, and group calendars. The second spurt of bullecalbel networking, one which was less dependent upon mailing list-related features and more upon Internet forum features, began in the early- to mid-2000s in the form of such services as LiveJournal, Friendster, MySpace and Facebook. These services continued the evolution of the web-based e-group as a discussion and organization medium. In the late 2000s, services such as Yammer and Micromobs further advanced e-group communication by taking advantage of microblog-style activity streams. == In virtual worlds == In Second Life, groups are centered less around discussion forums (as such, an asynchronous conferencing feature is not built into the Second Life network as of 2009) and common interest, and are more centered on maintenance of a particular geographic location inside the network. Such groups are often created by the owners of areas such as buildings, plots of land or whole islands in order to cater to the most frequent visitors and patrons of the regions. With the limited asynchronous messaging capability of Second Life, groups are also a means of mass-emailing announcements pertinent to the group, but are not completely capable of hosting discussion or deliberation of such announcement messages. == The importance of online social networking groups == Before people expanded their social life to the internet, they had small circles. These included the networks gained from rural areas or villages, such as family, friends and neighbors, and community groups such as churches. These networks represented a social safety net to support individuals. Since we have moved a huge part of our social life to the internet, online social networking groups have become a way to maintain a structure in social life. Online networking is made up by clusters of people, bounding themselves together on the World Wide Web. To be able to sort out the many different clusters we belong to we use online groups to helps us arrange and make sense of all our contacts. This sense-making is rooted within us, we sort and put people into compartments or sort by categories to make sense and try to understand our relationships to the people around us. Online social networking groups therefore enables us to do the same thing online. Online social networks have a huge impact on people’s lives. Since the social network revolution has offered people with more loose ties and diversity in their relationships, it creates both stress and opportunities. Furthermore, the Internet revolution has transformed the contact point from a household to the individual. In addition, people are in constant communication with each other due to the mobile revolution. All in all, the mentioned revolutions created a new social operating system: "networked individualism". The way that people currently connect, communicate and exchange information can be described as a form of operating system because of the similarities between the structure of computer systems and the networked individualism that has taken form in society. These structures consist of unwritten rules, norms, constraints and opportunities which are apparent for those who are part of a specific network. == Concerns == There is some research claiming that fake news is infiltrating online social networking. A recent study claimed that people exposed to fake news generally revert to their original opinion even after finding out the information they were given was false.
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Image scaling

In computer graphics and digital imaging, image scaling is the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement. When scaling a vector graphic image, the graphic primitives that make up the image can be rendered using geometric transformations at any resolution with no loss of image quality. When scaling a raster graphics image, a new image with a higher or lower number of pixels must be generated. In the case of decreasing the pixel number (scaling down), this usually results in a visible quality loss. From the standpoint of digital signal processing, the scaling of raster graphics is a two-dimensional example of sample-rate conversion, the conversion of a discrete signal from a sampling rate (in this case, the local sampling rate) to another. == Mathematical == Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem. According to the theorem, downsampling to a smaller image from a higher-resolution original can only be carried out after applying a suitable 2D anti-aliasing filter to prevent aliasing artifacts. The image is reduced to the information that can be carried by the smaller image. In the case of up sampling, a reconstruction filter takes the place of the anti-aliasing filter. A more sophisticated approach to upscaling treats the problem as an inverse problem, solving the question of generating a plausible image that, when scaled down, would look like the input image. A variety of techniques have been applied for this, including optimization techniques with regularization terms and the use of machine learning from examples. == Algorithms == An image size can be changed in several ways. === Nearest-neighbor interpolation === One of the simpler ways of increasing image size is nearest-neighbor interpolation, replacing every pixel with the nearest pixel in the output; for upscaling, this means multiple pixels of the same color will be present. This can preserve sharp details but also introduce jaggedness in previously smooth images. 'Nearest' in nearest-neighbor does not have to be the mathematical nearest. One common implementation is to always round toward zero. Rounding this way produces fewer artifacts and is faster to calculate. This algorithm is often preferred for images which have little to no smooth edges. A common application of this can be found in pixel art. === Bilinear and bicubic interpolation === Bilinear interpolation works by interpolating pixel color values, introducing a continuous transition into the output even where the original material has discrete transitions. Although this is desirable for continuous-tone images, this algorithm reduces contrast (sharp edges) in a way that may be undesirable for line art. Bicubic interpolation yields substantially better results, with an increase in computational cost. === Sinc and Lanczos resampling === Sinc resampling, in theory, provides the best possible reconstruction for a perfectly bandlimited signal. In practice, the assumptions behind sinc resampling are not completely met by real-world digital images. Lanczos resampling, an approximation to the sinc method, yields better results. Bicubic interpolation can be regarded as a computationally efficient approximation to Lanczos resampling. === Box sampling === One weakness of bilinear, bicubic, and related algorithms is that they sample a specific number of pixels. When downscaling below a certain threshold, such as more than twice for all bi-sampling algorithms, the algorithms will sample non-adjacent pixels, which results in both losing data and rough results. The trivial solution to this issue is box sampling, which is to consider the target pixel a box on the original image and sample all pixels inside the box. This ensures that all input pixels contribute to the output. The major weakness of this algorithm is that it is hard to optimize. === Mipmap === Another solution to the downscale problem of bi-sampling scaling is mipmaps. A mipmap is a prescaled set of downscaled copies. When downscaling, the nearest larger mipmap is used as the origin to ensure no scaling below the useful threshold of bilinear scaling. This algorithm is fast and easy to optimize. It is standard in many frameworks, such as OpenGL. The cost is using more image memory, exactly one-third more in the standard implementation. === Fourier-transform methods === Simple interpolation based on the Fourier transform pads the frequency domain with zero components (a smooth window-based approach would reduce the ringing). Besides the good conservation (or recovery) of details, notable are the ringing and the circular bleeding of content from the left border to the right border (and the other way around). === Edge-directed interpolation === Edge-directed interpolation algorithms aim to preserve edges in the image after scaling, unlike other algorithms, which can introduce staircase artifacts. Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), Edge-Guided Image Interpolation (EGGI), Iterative Curvature-Based Interpolation (ICBI), and Directional Cubic Convolution Interpolation (DCCI). A 2013 analysis found that DCCI had the best scores in peak signal-to-noise ratio and structural similarity on a series of test images. === hqx === For magnifying computer graphics with low resolution and/or few colors (usually from 2 to 256 colors), better results can be achieved by hqx or other pixel-art scaling algorithms. These produce sharp edges and maintain a high level of detail. === Vectorization === Vector extraction, or vectorization, offers another approach. Vectorization first creates a resolution-independent vector representation of the graphic to be scaled. The resulting SVG vector file can then be exported and rendered at any required resolution without quality loss, serving directly as production-ready artwork for scalable display & printing. This technique is used by Adobe Illustrator, Live Trace, and Inkscape. Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity. === Deep convolutional neural networks === This method uses machine learning for more detailed images, such as photographs and complex artwork. Programs that use this method include waifu2x, Imglarger and Neural Enhance. Demonstration of conventional vs. waifu2x upscaling with noise reduction, using a detail of Phosphorus and Hesperus by Evelyn De Morgan. [Click image for full size] AI-driven upscaling software allows detail and sharpness to be added to historical photographs, where it is not present in the original. The availability of AI upscaling tools has led to confusion where a person believes that the upscaled version of a blurry image is genuinely showing them the subject of the original photograph. In 2025 a user of the social media site X posted an AI-upscaled version of a low resolution photo of Donald Trump that they had zoomed in on, and asked if anyone could "explain what the hell is happening to his forehead". Experts noted that the image had been distorted by the upscaling process, and that such tools "inevitably have to invent, or at least recreate, details that were or were not there". == Applications == === General === Image scaling is used in, among other applications, web browsers, image editors, image and file viewers, software magnifiers, digital zoom, the process of generating thumbnail images, and when outputting images through screens or printers. === Video === This application is the magnification of images for home theaters for HDTV-ready output devices from PAL-Resolution content, for example, from a DVD player. Upscaling is performed in real time, and the output signal is not saved. === Pixel-art scaling === As pixel-art graphics are usually low-resolution, they rely on careful placement of individual pixels, often with a limited palette of colors. This results in graphics that rely on stylized visual cues to define complex shapes with little resolution, down to individual pixels. This makes scaling pixel art a particularly difficult problem. Specialized algorithms were developed to handle pixel-art graphics, as the traditional scaling algorithms do not take perceptual cues into account. Since a typical application is to improve the appearance of fourth-generation and earlier video games on arcade and console emulators, many are designed to run in real time for small input images at 60 frames per second. On fast hardware, these algorithms are suitable for gaming and other real-time image processing. These algorithms provide sharp, crisp graphics, while minimizing blur. Scaling art algorithms have been implemented in a wide range of emulators such as HqMAME and DOSBox, as well as 2D game engines and game engine recreations such as ScummVM. They gained recognition with game
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Influence-for-hire

Influence-for-hire or collective influence, refers to the economy that has emerged around buying and selling influence on social media platforms. == Overview == Companies that engage in the influence-for-hire industry range from content farms to high-end public relations agencies. Traditionally influence operations have largely been confined to public sector actors like intelligence agencies, in the influence-for-hire industry the groups conduction the operations are private with commerce being their primary consideration. However many of the clients in the influence-for-hire industry are countries or countries acting through proxies. They are often located in countries with less expensive digital labor. == History == In May 2021, Facebook took a Ukrainian influence-for-hire network offline. Facebook attributed the network to organizations and consultants linked to Ukrainian politicians including Andriy Derkach. During the COVID-19 pandemic state sponsored misinformation was spread through influence-for-hire networks. In August 2021, a report published by the Australian Strategic Policy Institute implicated the Chinese government and the ruling Chinese Communist Party in campaigns of online manipulation conducted against Australia and Taiwan using influence-for-hire.
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Virtual advertising

Virtual advertising is the use of digital technology to insert virtual advertisements into a live or pre-recorded television show, often in sports events. This technique is often used to allow broadcasters to overlay existing physical advertising panels inside the sports venue with virtual content on the screen when broadcasting the same event in multiple regions; a Spanish football game can be broadcast in Mexico with Mexican advertisements. Similarly, virtual content can be inserted onto empty space within the sports venue such as the pitch, where physical advertising cannot be placed due to regulatory or safety reasons. Virtual advertising content is intended to be photorealistic, so that the viewer has the impression they are seeing the real in-stadium advertising. == History == Throughout the 1980s, 1990s, and 2000s, advertising on television and in newspapers was a popular method of spreading information. The marketer Jeremiah Lynwood stated that "Thirty years ago, [U.S.] consumers viewed an average of 560 ads per day", mostly from newspapers, television shows, gasoline pumps, and so on. Lynwood also stated that, at the time, "American consumers may be exposed to 3,000 commercial messages every day". Within that time frame, the exposure of daily ads have supported many local and big businesses. With the arrival of the 2000s and 2010s, technological advances have created new opportunities for many businesses to grow. In the 21st century, virtual advertising has been used to create virtual product placements in television shows hours, days, or years after they have been produced. Advertisements can be targeted to regional markets and updated over time to ensure maximum efficiency of advertising money. A good example of how virtual advertising is used in everyday life is in sports. Virtual advertising uses the latest technology to place an ad in position to the field of play, regardless of camera motion, and the players' movement over the logos. Recently, the NHL have virtually inserted sponsors on the glass above the physical boards in NHL stadiums. Big brands will not spend their time or money on hitting a certain region when their main goal is to build global brand awareness. Digital signage opportunities allow these larger brands to purchase signage in a stadium during games that are instead nationally televised. This gets even more expansive thanks to social media outlets like Twitter, Facebook, and Amazon. On the other hand, local businesses sign when there are smaller games going on. The signage is much more affordable and still reaches a vast number of people. Virtual advertising may even make live attendance more attractive to sport fans because the technology allows the playing field and surrounding areas to be cleared of advertisements while television viewers at home are exposed to commercials. For the most part, virtual advertising makes a live attendance more attractive to sports fans, because instead of being at home watching commercials, live fans are able to be clear of advertisements and enjoy the game without pop-up ads. == Technology == The technology used in virtual insertions often uses automated processes such as: automatic detection of playfield limits, automatic detection of cuts, recognition of playfield surface, recognition of existing logos for logo replacements, etc. An operator is usually dedicated to the visual control of the effect but new systems allow to use the instant replay operator. == Examples == === Live events === Virtual advertisements can be effectively integrated into live television in real-time. For example, Fox Sports Net places a virtual advertisement on the glass behind the goaltender that can only be seen on television. The advertising in the playfields is property of the club, except in some professional sports where the league or federation owns the advertising rights. However, the advertising rights broadcast on the screen are property of the broadcasters or the TV channel. This means that second right holders can benefit from selling this virtual advertising. The number of TV viewers is also higher than the people in the stadium, generating more visibility to the advertised marks and more income to the broadcasters. Virtual advertising was first introduced in football during the 2015 Audi Cup at the Allianz Arena in Munich. AIM Sport implemented the technology to digitally overlay advertisements on the stadium's perimeter boards, allowing different sponsors to be displayed to viewers in different broadcast regions. In Formula One, virtual ads are placed on the grass or as virtual billboards. In baseball, Major League Baseball places virtual advertisements on a back-board behind the batter which can be targeted differently in local markets or countries. During the World Series, MLB international broadcasts of the World Series feature different advertisements on a per market basis, showing a different ad in the US, Canadian, Latin American and Japanese markets. In tennis, e.g. during the 2019 ATP Finals in London's O2 Arena certain logos in the background were replaced for various country feeds. In table tennis e.g. during the ITTF World Tour Australian Open 2019 virtual advertising overlays were used by uniqFEED AG in Switzerland. Since the 2022–23 season, the National Hockey League (NHL) has used digitally enhanced dasherboards (DED) to erase and replace ads on each arena's boards with up to 120 thirty-second segments on all or part of the rink. Each broadcaster can use a different set of ads. DED were first used at the 2016 World Cup of Hockey, which was organized by the NHL. At UEFA Euro 2024, AIM Sport provided virtual advertising for all matches, marking one of the largest implementations of the technology in an international tournament. In addition to the tournament itself, virtual advertising was also used in the participating teams' domestic matches, extending region-specific advertising beyond the competition itself.
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Outline of telecommunication

The following outline is provided as an overview of and topical guide to telecommunication: Telecommunication – the transmission of signals over a distance for the purpose of communication. In modern times, this process almost always involves the use of electromagnetic waves by transmitters and receivers, but in earlier years it also involved the use of drums and visual signals such as smoke, fire, beacons, semaphore lines and other optical communications. == Modes of telecommunication == E-mail Fax Instant messaging Radio Satellite SMS Telegraphy Telephony Television Television broadcasting mobile telephony Videoconferencing VoIP Voicemail == Types of telecommunication networks == Telecommunications network Computer networks ARPANET Ethernet Internet Wireless networks Public switched telephone networks (PSTN) Packet switched networks Radio network Broadband Wireless Broadband == Aspects of telecommunication transmission == Telecommunication Analog Digital Functional profile Optics === Telecommunication technology === Modulation Amplitude modulation Frequency modulation Quadrature amplitude modulation Nyquist rate Nyquist ISI criterion Pulse shaping Intersymbol interference === Communications media types === Physical media for Telecommunication Twisted pair Coaxial cable Optical fiber Telecommunication through Free Space Broadcast radio frequency including television and radio Line-of-sight Communications satellite Terrestrial Microwave Wireless LAN === Relationship between media and transmitters === Physical access to media Simplex Duplex (telecommunications) Logical relationships Return channel Two-way alternating Two-way simultaneous === Multiple access to media === Multiplexing Analog Frequency division multiplexing Space division multiplexing Digital Time-division multiplexing Statistical multiplexing and Packet switching Media Access Control Contention Token-based Centralized token control Distributed token control == History of telecommunication == History of telecommunication History of telegraphy History of the telephone Invention of the telephone Timeline of the telephone History of radio History of television History of videophones History of mobile phones History of computing hardware History of the Internet == Major telecommunications equipment manufacturers == Alcatel-Lucent – French global telecommunications equipment company Aricent – Former company AT&T – American telecommunications company Avaya – American technology company Ciena – American telecommunications company Cisco Systems – American multinational technology companyPages displaying short descriptions of redirect targets Ericsson – Swedish multinational networking and telecommunications company Fujitsu – Japanese multinational technology company HCL Technologies – Indian multinational technology companyPages displaying short descriptions of redirect targets Huawei – Chinese multinational technology company NEC – Japanese technology corporation Nokia – Multinational data networking and telecommunications equipment company ShoreTel – US telecommunications company Verizon – American telecommunications company ZTE – Chinese telecommunications company == Major telecommunications service providers == List of mobile network operators List of telephone operating companies == Telecommunication organizations == Alliance for Telecommunications Industry Solutions Telecommunications Industry Association == Telecommunication publications == Magazines Billing and OSS World Cabling Installation & Maintenance Call Center Communications News Communications System Design Lightwave Mobile Radio Technology (MRT) New Telephony Phone+ RCR Wireless News Telecom Asia Telecommunications Magazine Telephony WhatSatphone Magazine Wireless Systems Design Wireless Week Xchange == Persons influential in telecommunication == Edwin Howard Armstrong – American radio-frequency engineer and inventor (1890–1954) John Logie Baird – Scottish inventor (1888–1946) Paul Baran – American-Jewish engineer (1926–2011) Alexander Graham Bell – Inventor of the telephone (1847–1922) Tim Berners-Lee – English computer scientist (born 1955) Jagadish Chandra Bose – Physicist, biologist and botanist (1857–1937) Vint Cerf – American computer scientist and Internet pioneer (born 1943) Claude Chappe – Late 18th-century French inventor Donald Davies – British computer scientist (1924–2000) Louis Pouzin – French computer scientist and Internet pioneer (born 1931) Lee de Forest – American inventor (1873–1961) Philo Farnsworth – American inventor (1906–1971) Reginald Fessenden – Canadian-American electrical engineer and inventor (1866–1932) Elisha Gray – American electrical engineer (1835–1901) Innocenzo Manzetti – Italian inventor (1826–1877) Guglielmo Marconi – Italian radio-frequency engineer and inventor (1874–1937) Antonio Meucci – Italian inventor (1808–1889) Alexander Stepanovich Popov – Russian physicist (1859–1906)Pages displaying short descriptions of redirect targets Johann Philipp Reis – German scientist and inventor Almon Brown Strowger – American inventor of the telephone exchange (1839–1902) Nikola Tesla – Serbian-American engineer and inventor (1856–1943) Camille Tissot – French physicist (1868–1917) Alfred Vail – 19th-century American machinist and inventor Charles Wheatstone – English physicist and inventor (1802–1875) Vladimir K. Zworykin – Russian-American engineer (1888–1982)
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Structured sparsity regularization

Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the output variable Y {\displaystyle Y} (i.e., response, or dependent variable) to be learned can be described by a reduced number of variables in the input space X {\displaystyle X} (i.e., the domain, space of features or explanatory variables). Sparsity regularization methods focus on selecting the input variables that best describe the output. Structured sparsity regularization methods generalize and extend sparsity regularization methods, by allowing for optimal selection over structures like groups or networks of input variables in X {\displaystyle X} . Common motivation for the use of structured sparsity methods are model interpretability, high-dimensional learning (where dimensionality of X {\displaystyle X} may be higher than the number of observations n {\displaystyle n} ), and reduction of computational complexity. Moreover, structured sparsity methods allow to incorporate prior assumptions on the structure of the input variables, such as overlapping groups, non-overlapping groups, and acyclic graphs. Examples of uses of structured sparsity methods include face recognition, magnetic resonance image (MRI) processing, socio-linguistic analysis in natural language processing, and analysis of genetic expression in breast cancer. == Definition and related concepts == === Sparsity regularization === Consider the linear kernel regularized empirical risk minimization problem with a loss function V ( y i , f ( x ) ) {\displaystyle V(y_{i},f(x))} and the ℓ 0 {\displaystyle \ell _{0}} "norm" as the regularization penalty: min w ∈ R d 1 n ∑ i = 1 n V ( y i , ⟨ w , x i ⟩ ) + λ ‖ w ‖ 0 , {\displaystyle \min _{w\in \mathbb {R} ^{d}}{\frac {1}{n}}\sum _{i=1}^{n}V(y_{i},\langle w,x_{i}\rangle )+\lambda \|w\|_{0},} where x , w ∈ R d {\displaystyle x,w\in \mathbb {R^{d}} } , and ‖ w ‖ 0 {\displaystyle \|w\|_{0}} denotes the ℓ 0 {\displaystyle \ell _{0}} "norm", defined as the number of nonzero entries of the vector w {\displaystyle w} . f ( x ) = ⟨ w , x i ⟩ {\displaystyle f(x)=\langle w,x_{i}\rangle } is said to be sparse if ‖ w ‖ 0 = s < d {\displaystyle \|w\|_{0}=s 0 {\displaystyle w_{j}>0} . However, as in this case groups may overlap, we take the intersection of the complements of those groups that are not set to zero. This intersection of complements selection criteria implies the modeling choice that we allow some coefficients within a particular group g {\displaystyle g} to be set to zero, while others within the same group g {\displaystyle g} may remain positive. In other words, coefficients within a group may differ depending on the several group memberships that each variable within the group may have. ==== Union of groups: latent group Lasso ==== A different approach is to consider union of groups for variable selection. This approach captures the modeling situation where variables can be selected as long as they belong at least to one group with positive coefficients. This modeling perspective implies that we want to preserve group structure. The formulation of the union of groups approach is also referred to as latent group Lasso, and requires to modify the group ℓ 2 {\displaystyle \ell _{2}} norm considered above and introduce the following regularizer R ( w ) = i n f { ∑ g ‖ w g ‖ g : w = ∑ g = 1 G w ¯ g } {\displaystyle R(w)=inf\left\{\sum _{g}\|w_{g}\|_{g}:w=\sum _{g=1}^{G}{\bar {w}}_{g}\right\}} where w ∈ R d {\displaystyle w\in {\mathbb {R^{d}} }} , w g ∈ G g {\displaystyle w_{g}\in G_{g}} is the vector of coefficients of group g, and w ¯ g ∈ R d {\displaystyle {\bar {w}}_{g}\in {\mathbb {R^{d}} }} is a vector with coefficients w g j {\displaystyle w_{g}^{j}} for all variables j {
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SitePal

SitePal is a speaking avatar platform for small and medium-sized businesses developed by Oddcast. SitePal allows users to deploy "virtual employees" on websites that can welcome visitors, guide them around the site and answer questions. The use of SitePal on commercial websites has been controversial because many visitors report finding them annoying. Some research has shown that they can increase sales in comparison to using static photographs. == Development == The technology used was the result of more than 4 years of research at Stanford University. The research was based on a literature review and other previous work in the field of artificial intelligence research. The SitePal AI option uses the AIML programming language, which is partially editable by users. This allows web designers to simulate normal human conversation by using keywords or key phrases that the bot can respond to. == Features == The company provides web designers with options to customize the chosen avatar. A large selection of faces, clothing, hair, backgrounds, voices and other details are available. If a web designer wants to use a particular face, Sitepal can create one from a photo. Thus, a mascot or a known face can be simulated. == Speech == Sitepal avatars talk through text-to-speech (tts) software. A short paragraph can be written (up to 900 characters) and the text-to-speech engine will compile the actual speech, which can be reproduced and edited. The tts engine is not perfect, but it comes close to actual speech and is easy to understand. Tts can be further enhanced by some commands, like /laugh and /loud which make the avatar laugh or talk loud. Even pronunciation is possible. The web designer can record and upload his or her own audio messages. Alternatively Sitepal offers professional voice acting service at extra cost. == User interaction == The company provides 5 options for visitor interaction: No interaction. The avatar simply says a pre-fixed message. FAQ mode. Questions can be configured, which are clickable and the user can hear the answer. Lead mode. The avatar prompts the user to type his email and short message, so it can be sent to the webmaster (usually used on a "contact us" page) Chatbot mode. The avatar greets the user, and he can type his questions and have a conversation with the bot. With predetermined replies, this can work as an FAQ as well. API customization. Experienced programmers can make their avatar interact with their website, making it talk when the user clicks on a link or when other triggers occur. Even dual avatar conversations can be created, like a talk show. == Posting options == The company provides five options for posting the avatar: Embed in webpage (via javascript) Embed in HTML Send by email Publish to eBay Embed in Flash == Criticism == Early reviews, such as one by Troy Dreier published in PC World in 2002 were positive and described SitePal as: "an engagingly simple and personal tool, and the price is reasonable for what it adds to a site". Although Dreier did note that the program had "bugs that suggested it hadn't been tested thoroughly". In more recent years, reaction to SitePal has been much more negative with reviews such as Tom Spring writing in a PC World review citing SitePal ads and described his reaction as "Not so nice". Paul Bissex, writing in E-Scribe News described SitePal as "heinous... and embarrassing if anyone is within earshot...they creep me out" == Research on effectiveness == In one single-website research project Anita Campbell had half the visitors to Small Business Trends see a SitePal and the other half see just a static photograph. Over 11,000 visitors the SitePal avatar improved sign-up for a newsletter 144% over the control condition.
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Extremely online

An extremely online (often capitalized), terminally online, or chronically online person is someone who is closely engaged with Internet culture. People said to be extremely online often believe that online posts are very important. Events and phenomena can themselves be extremely online; while often used as a descriptive term, the phenomenon of extreme online usage has been described as "both a reformation of the delivery of ideas – shared through words and videos and memes and GIFs and copypasta – and the ideas themselves". Here, "online" is used to describe "a way of doing things, not [simply] the place they are done". == Criteria == While the term was in use as early as 2014, it gained popularity over the latter half of the 2010s in conjunction with the increasing prevalence and notability of Internet phenomena in all areas of life. Extremely online people, according to The Daily Dot, are interested in topics "no normal, healthy person could possibly care about", and have been analogized to "pop culture fandoms, just without the pop". Extremely online phenomena such as fan culture and reaction GIFs have been described as "swallowing democracy" by journalists such as Amanda Hess in The New York Times, who claimed that a "great convergence between politics and culture, values and aesthetics, citizenship and commercialism" had become "a dominant mode of experiencing politics". Vulture – formerly the pop culture section of New York magazine, now a stand-alone website – has a section for articles tagged "extremely online". == Historical background == In the 2010s, many categories and labels came into wide use from media outlets to describe Internet-mediated cultural trends, such as the alt-right, the dirtbag left, and doomerism. These ideological categories are often defined by their close association with online discourse. For example, the term "alt-right" was added to the Associated Press' stylebook in 2016 to describe the "digital presence" of far-right ideologies, the dirtbag left refers to a group of "underemployed and overly online millennials" who "have no time for the pieties of traditional political discourse", and the doomer's "blackpilled despair" is combined with spending "too much time on message boards in high school" to produce an eclectic "anti-socialism". Extreme onlineness transcends ideological boundaries. For example, right-wing figures like Alex Jones and Laura Loomer have been described as "extremely online", but so have those on the left like Alexandria Ocasio-Cortez and fans of the Chapo Trap House podcast. Extremely online phenomena can range from acts of offline violence (such as the 2019 Christchurch shootings) to "[going] on NPR to explain the anti-capitalist irony inherent in kids eating Tide Pods". United States President Donald Trump's posts on social media have been frequently cited as extremely online, during both his presidency and his 2020 presidential campaign; Vox claimed his approach to re-election veered into being "Too Online", and Reason questioned whether the final presidential debate was "incomprehensible to normies". While individual people are often given the description, being extremely online has also been posited as an overall cultural phenomenon, applying to trends like lifestyle movements suffixed with "-wave" and "-core" based heavily on Internet media, as well as an increasing expectation for digital social researchers to have an "online presence" to advance in their careers. == Participants and media coverage == One example of a phenomenon considered to be extremely online is the "wife guy" (a guy who posts about his wife); despite being a "stupid online thing" which spent several years as a piece of Internet slang, in 2019 it became the subject of five articles in leading U.S. media outlets. Like many extremely online phrases and phenomena, the "wife guy" has been attributed in part to the in-character Twitter account dril. The account frequently parodies how people behave on the Internet, and has been widely cited as influential on online culture. In one tweet, his character refuses to stop using the Internet, even when someone shouts outside his house that he should log off. Many of dril's other coinages have become ubiquitous parts of Internet slang. Throughout the 2010s, posters such as dril inspired commonly used terms like "corncobbing" (referring to someone losing an argument and failing to admit it); while originally a piece of obscure Internet slang used on sites like Twitter, use of the term (and controversy over its misinterpretation) became a subject of reporting from traditional publications, with some noting that keeping up with the rapid turnover of inside jokes, memes, and quotes online required daily attention to avoid embarrassment. Twitch has been described as "talk radio for the extremely online". Another example of an event cited as extremely online is No Nut November. Increasingly, researchers are expected to have more of an online presence, to advance in their careers, as networking and portfolios continue to transition to the digital world. In November 2020, an article in The Washington Post criticized the filter bubble theory of online discourse on the basis that it "overgeneralized" based on a "small subset of extremely online people". The 2021 storming of the United States Capitol was described as extremely online, with "pro-Trump internet personalities", such as Baked Alaska, and fans livestreaming and taking selfies. People who have been described as extremely online include Chrissy Teigen, Jon Ossoff, and Andrew Yang. In contrast, Joe Biden has been cited as the antithesis of extremely online—The New York Times wrote in 2019 that he had "zero meme energy".
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