AI Content Blocker Extension

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

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  Shader lamps

  Shader lamps is a computer graphic technique used to change the appearance of physical objects. The still or moving objects are illuminated, using one or more video projectors, by static or animated texture or video stream. The method was invented at University of North Carolina at Chapel Hill by Ramesh Raskar, Greg Welch, Kok-lim Low and Deepak Bandyopadhyay in 1999 [1] as a follow on to Spatial Augmented Reality [2] also invented at University of North Carolina at Chapel Hill in 1998 by Ramesh Raskar, Greg Welch and Henry Fuchs. A 3D graphic rendering software is typically used to compute the deformation caused by the non perpendicular, non-planar or even complex projection surface. Complex objects (or aggregation of multiple simple objects) create self shadows that must be compensated by using several projectors. The objects are typically replaced by neutral color ones, the projection giving all its visual properties, thus the name shader lamps. The technique can be used to create a sense of invisibility, by rendering transparency. The object is illuminated not by a replacement of its own visual properties, but by the corresponding visual surface placed behind the object as seen from an arbitrary viewing point.

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  Group of Governmental Experts on Lethal Autonomous Weapons Systems

  The Group of Governmental Experts on Lethal Autonomous Weapons Systems, commonly known as the GGE on LAWS, refers to a group of governmental experts established under the framework of the Convention on Certain Conventional Weapons (CCW), a United Nations arms control framework. The group examines legal, ethical, societal and moral questions that arise from the increased use of autonomous robots to carry weapons and to be programmed to engage in combat in various situations that might arise, including battles between countries, or in patrolling border areas or sensitive areas, or other similar roles. As of 18 March 2025, the Convention on Certain Conventional Weapons had 128 High Contracting Parties. In the Geneva Conventions, the term "High Contracting Parties" refers to the states that have joined the conventions and are therefore bound to uphold them. Among the countries that have joined are states with tense relations or ongoing armed conflict with one another, including Russia and Ukraine, Israel and the State of Palestine, and Pakistan and Afghanistan. == Background == In 2013, the Meeting of State Parties to the Convention on Certain Conventional Weapons agreed on a mandate on lethal autonomous weapon systems and tasked its chairperson with convening an informal Meeting of Experts to discuss issues related to emerging technologies in the area of LAWS. Those informal Meetings of Experts were then held in 2014, 2015 and 2016, and their reports fed into subsequent meetings of the High Contracting Parties. At the Fifth CCW Review Conference in 2016, the High Contracting Parties decided to establish an open-ended Group of Governmental Experts on emerging technologies in the area of LAWS, building on the earlier expert meetings. Since then, the group has been reconvened annually. In 2023, the Meeting of the High Contracting Parties to the CCW decided that the GGE on LAWS would continue its work in 2024 and 2025. The group was tasked with developing, by consensus, elements of a possible instrument, without predetermining its form, as well as other measures addressing lethal autonomous weapon systems, drawing on existing CCW protocols, earlier recommendations, state proposals, and legal, military, and technological expertise. == 2024 == In 2024, the GGE met twice, and the group was chaired by Robert in den Bosch, the Netherlands' disarmament ambassador. The 2024 Meeting of the High Contracting Parties decided that the group would meet for 10 days in 2025, in two five-day sessions, and reaffirmed its mandate to continue work by consensus on possible elements of an instrument and other measures addressing lethal autonomous weapon systems. == 2025 == At its first 2025 session, held in Geneva from 3 to 7 March 2025, the Group of Governmental Experts on Lethal Autonomous Weapon Systems discussed revisions to the chair's rolling text. The text was structured into five sections, or "boxes", though delegates held differing views on whether headings were useful or appropriate. Broadly, the discussions covered the characterization of lethal autonomous weapon systems, the application of international humanitarian law, possible prohibitions and regulations, legal review, and questions of accountability and responsibility. At its second session, held from 1 to 5 September 2025, delegations continued work on the chair's rolling text, which set out elements of a possible instrument and was organized into five thematic "boxes". == 2026 == === Developments before the 2026 session === A few weeks before the meeting, autonomous weapons drew renewed attention when the United States pressured Anthropic to revise the terms of use for its AI model Claude. Anthropic prohibited the model's use for mass domestic surveillance and for fully autonomous weapons operating without human oversight, while reports also emerged that OpenAI had reached an agreement with the U.S. Department of War for the use of its AI models, reportedly stipulating that they would not independently direct autonomous weapons where human control was required. The U.S. military nevertheless continued to use Claude during its war on Iran, and there was increasing alarm about the use of AI-assisted semi-autonomous weapons in conflicts including those in Ukraine, Sudan, Gaza, and Iran. Before the start of the sessions, Robert in den Bosch, as chair, warned that progress was urgent because technological developments were moving quickly. At the same time, although states agreed that international humanitarian law applied to LAWS, specific internationally binding standards governing such systems remained largely absent. A key divide before the session was that Russia and the United States opposed new legally binding instruments, while other states argued that new rules were necessary. According to Robert in den Bosch, the talks could lead to new rules, amendments to an existing convention, or a new treaty. === First session === From 2 to 6 March 2026, the group held its penultimate session under the group's three-year mandate. Delegations discussed the chair's rolling draft text, circulated in December 2025, on elements of a possible instrument or other measures concerning lethal autonomous weapon systems. In revised text circulated by the chair on 5 March 2026, a lethal autonomous weapon system was characterized as "a functionally integrated combination of one or more weapons and technological components, that can identify, select, and engage a target, without intervention by a human operator in the execution of these tasks". The text was divided into five boxes to structure discussion. During the session, delegates conducted a first reading of the draft text, and the chair later circulated revised language for several sections. Informal consultations were also held. According to campaign groups and participating observers, support grew during the week for moving to negotiations on the basis of the rolling text, with more than 70 states said to support that step by the end of the session, though some participants warned that attempts to bridge differences risked blurring the group's core purpose. The International Committee of the Red Cross argued that the text should not only restate existing international humanitarian law, but also clarify how those rules apply to autonomous weapons and set out additional measures tailored to the specific challenges such systems raise. Stop Killer Robots likewise emphasized the need to preserve meaningful human judgment and control over increasingly autonomous systems. During the discussions, the U.S. delegation opposed the term "human control" and reportedly proposed the alternative phrase "good faith human judgment and care". Other delegations rejected that wording as too weak, while many states continued to insist that meaningful human control over weapon systems remained essential.

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  Depop

  Depop Limited is a social e-commerce company based in London, with additional offices in Milan and New York City. The company allows users to buy and sell items, which are mostly used and vintage pieces of clothing. == History == Depop was founded in 2011 by entrepreneur Simon Beckerman at an Italian technological incubator and business start-up centre, H-Farm. Beckerman came up with the original outline of the application during his time working on PIG, a fashion magazine based in Italy that he co-founded. The idea was to create a platform where products shown in the magazine could be purchased by users online. This idea turned into a concept similar to a flea market but on the internet, where people could sell their items while also being in control of advertising, public relations, and the creative process behind their accounts. While being financially supported by H-Farm, Beckerman worked within a team to create and lay out the Depop application while exposing it to numerous investors. In 2013, Beckerman became a member of the company's board to help improve the application and business while concurrently ceding his role of CEO. Maria Raga, Depop's co-founder and former CEO, took on the role of vice president of operations in 2014, and in 2016, she became chief executive. According to Raga, the main goal while developing Depop was to become the next Airbnb or Spotify, but to make an impact on fashion. Paolo Barberis and Nana Bianca were two of the first investors in the platform in 2012 with a seed investment. Its headquarters were moved to London in 2012. Depop expanded and opened additional offices in Milan and New York City. Beckerman raised €1 million in funding in October 2013 from Red Circle Investment and brought on Faroese Runar Reistrup as new CEO. In 2015, Depop secured another investment of $8 million from Balderton Capital and HV Capital. In March 2016, former CEO, Runar Reistrup, stated that Depop's growth was achieved through word of mouth. During his time as CEO, this growth involved taking Depop as a startup and working to raise funds to eventually amass a significant user base within the United States. In June 2019, Depop raised $62 million in Series C from General Atlantic to fund its expansion. Previous investors HV Capital, Balderton Capital, Creandum, Octopus Ventures, TempoCap and Sebastian Siemiatkowski also participated. During this time, Depop held workshops and conversations as part of their Depop Live NY events, and the company also opened a London store through their partnership with Selfridges. In 2020, Depop's gross merchandise sales and revenue both more than doubled to $650 million and $70 million respectively. This may be attributed to Depop's responsiveness to user trends, its lack of issues regarding inventory management, and the increase in users looking to resell. As of 2024, Depop has over 35 million users, according to their website. Depop is popular for Gen Z and young millennials, it is the 10th most-visited shopping platform for Gen Z consumers in the US, and, in a poll conducted by The Strategist in 2019, Depop was voted by teenagers as their favorite resale website. === Acquisition by Etsy === In June 2021, Depop was acquired by Etsy for $1.6 billion in cash, making it Etsy's most expensive acquisition; however, Depop continues to operate as a standalone brand independent from Etsy. This means that in addition to Depop keeping its existing team, the company retained its London location. At the time of acquisition, Etsy CEO Josh Silverman’s goal was to counteract the influx of buyers starting to go back to physical shops for their purchases. He saw Depop for its potential as a platform supporting a variety of products and creating a greater community of users. According to Silverman, Depop may expand and improve its services for its significant Gen Z user base. For Etsy, this acquisition maintains the company's foothold in the clothing industry and allows the company to expand its customer base to a younger demographic; at the same time, Depop is now able to make use of Etsy's company operations. When Maria Raga relinquished her position as Depop's CEO in 2022, Etsy assigned the role to Kruti Patel Goyal, who was Etsy's former chief product officer and a leader there for eleven years. When Goyal was appointed president and chief growth officer for Etsy in May, Peter Semple, former chief marketing officer, was assigned CEO of Depop officially on August 1st. === Acquisition by eBay === In February 2026, Etsy announced a proposed sale of Depop to eBay for $1.2 billion that was estimated to close within the year. == Business model == === Selling === Depop operates as a marketplace and social platform, where users can follow friends and other influencers to view their buying and selling activities. Through the platform, users are able to sell branded and designer items, as well as vintage pieces. Depop users are also encouraged by the platform to use other social networking services such as Instagram to promote their shop profiles. Celebrities have resold their own items on Depop, with some donating proceeds to charitable causes. Depop's user interface is modeled after that of Instagram. According to Depop, users who list and sell items provide their own photos with item descriptions. Users also note their designer items' authenticity and if they include any labels, tags, and receipts. These listings will appear in users' feeds. The platform's "Explore" page features items picked out by Depop staff. According to Depop, purchases are made via Apple Pay, Google Pay, credit and debit cards, and Klarna. Depop payments stay in-app, allowing for the company to mediate disputes and process refunds. Depop payments allow sellers to directly receive their payments in their bank account. To get paid by Depop, a seller has to add a bank account and verify their identification by uploading an ID. On July 18, 2024, Depop CEO Kruti Patel Goyal announced the removal of selling fees for US sellers, while maintaining a payment processing fee. This policy adjustment aimed to enhance seller revenue and support the growth of the second-hand market. === Buying === A Depop transaction includes the agreed sale price of the item, shipping fees, VAT or other applicable taxes and duties, and the marketplace fee for buyers in the U.S. or U.K. For international deliveries, packages may be subject to import taxes, customs duties, or fees, payable upon arrival or at checkout if Depop collects the tax on behalf of the buyer. For domestic purchases, relevant taxes may be collected by the seller or charged by the platform at checkout, ensuring no additional taxes are due upon delivery. For users in Australia, the United Kingdom, and the United States, Depop allows users to receive a full refund if their item does not arrive, arrives damaged, or is considerably different from the original when the issue is reported within 30 days. === Competitors === As of June 2021, Depop's competitors include Vinted, a platform founded by Milda Mitkute and Justas Janauskas in 2008 and valued at €3.5 billion, as well as the U.S. resale site Poshmark, valued at $3.5 billion. Additional competitors include Grailed, a peer-to-peer e-commerce site founded in 2014 that is recognized for its high-end second-hand menswear and streetwear, and Vestiaire Collection, a European resale app established in 2009 which specializes in authenticated pre-owned luxury items. The popularity of Depop has negatively impacted traditional second-hand stores, which can struggle to compete due to high labor costs and quality demands. There is an oversupply of clothes with the rise of fast fashion; this has taken a toll on the revenue aspect of the second-hand clothing industry. == Criticism == In November 2019, Business of Fashion reported that users within the Depop app were receiving sexually suggestive messages. In February 2020, Jessica Hamilton, a Depop buyer, reported that she found many scammers on the platform. She noticed this issue after she attempted to purchase a Nintendo Switch from a seller who would suspiciously only accept payment through a direct bank transfer without buyer protection. Hamilton blamed the company for its lack of action and relaxed security measures compared to other e-commerce sites, which made the platform especially susceptible to hackers. Without a clear strategy for managing scams, Depop lost some users' trust because of its negligence. In October 2020, some Depop buyers were tricked into paying sellers directly to bypass Depop's buyer protections, and the Depop sellers then sold those users' information on the dark web. In response, Depop claimed that it would improve security through mandatory password updates and multi-factor authentication. Users have criticized Depop for belatedly taking action against this issue.

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  Comparison of user features of messaging platforms

  Comparison of user features of messaging platforms refers to a comparison of all the various user features of various electronic instant messaging platforms. This includes a wide variety of resources; it includes standalone apps, platforms within websites, computer software, and various internal functions available on specific devices, such as iMessage for iPhones. This entry includes only the features and functions that shape the user experience for such apps. A comparison of the underlying system components, programming aspects, and other internal technical information, is outside the scope of this entry. == Overview and background == Instant messaging technology is a type of online chat that offers real-time text transmission over the Internet. A LAN messenger operates in a similar way over a local area network. Short messages are typically transmitted between two parties when each user chooses to complete a thought and select "send". Some IM applications can use push technology to provide real-time text, which transmits messages character by character, as they are composed. More advanced instant messaging can add file transfer, clickable hyperlinks, Voice over IP, or video chat. Non-IM types of chat include multicast transmission, usually referred to as "chat rooms", where participants might be anonymous or might be previously known to each other (for example collaborators on a project that is using chat to facilitate communication). Instant messaging systems tend to facilitate connections between specified known users (often using a contact list also known as a "buddy list" or "friend list"). Depending on the IM protocol, the technical architecture can be peer-to-peer (direct point-to-point transmission) or client-server (an Instant message service center retransmits messages from the sender to the communication device). By 2010, instant messaging over the Web was in sharp decline, in favor of messaging features on social networks. The most popular IM platforms were terminated, such as AIM which closed down and Windows Live Messenger which merged into Skype. Instant messaging has since seen a revival in popularity in the form of "messaging apps" (usually on mobile devices) which by 2014 had more users than social networks. As of 2010, social networking providers often offer IM abilities. Facebook Chat is a form of instant messaging, and Twitter can be thought of as a Web 2.0 instant messaging system. Similar server-side chat features are part of most dating websites, such as OkCupid or PlentyofFish. The spread of smartphones and similar devices in the late 2000s also caused increased competition with conventional instant messaging, by making text messaging services still more ubiquitous. Many instant messaging services offer video calling features, voice over IP and web conferencing services. Web conferencing services can integrate both video calling and instant messaging abilities. Some instant messaging companies are also offering desktop sharing, IP radio, and IPTV to the voice and video features. The term "Instant Messenger" is a service mark of Time Warner and may not be used in software not affiliated with AOL in the United States. For this reason, in April 2007, the instant messaging client formerly named Gaim (or gaim) announced that they would be renamed "Pidgin". In the 2010s, more people started to use messaging apps on modern computers and devices like WhatsApp, WeChat, Viber, Facebook Messenger, Telegram, Signal and Line rather than instant messaging on computers like AIM and Windows Live Messenger. For example, WhatsApp was founded in 2009, and Facebook acquired in 2014, by which time it already had half a billion users. === Concepts === ==== Backchannel ==== Backchannel is the practice of using networked computers to maintain a real-time online conversation alongside the primary group activity or live spoken remarks. The term was coined in the field of linguistics to describe listeners' behaviours during verbal communication. (See Backchannel (linguistics).) The term "backchannel" generally refers to online conversation about the conference topic or speaker. Occasionally backchannel provides audience members a chance to fact-check the presentation. First growing in popularity at technology conferences, backchannel is increasingly a factor in education where WiFi connections and laptop computers allow participants to use ordinary chat like IRC or AIM to actively communicate during presentation. More recent research include works where the backchannel is brought publicly visible, such as the ClassCommons, backchan.nl and Fragmented Social Mirror. Twitter is also widely used today by audiences to create backchannels during broadcasting of content or at conferences. For example, television drama, other forms of entertainment and magazine programs. This practice is often also called live tweeting. Many conferences nowadays also have a hashtag that can be used by the participants to share notes and experiences; furthermore such hashtags can be user generated. == Features == Various platforms and apps are distinguished by their strengths and features in regards to specific functions. === Group messaging === === Official channels === Some apps include a feature known as "official channels" which allows companies, especially news media outlets, publications, and other mass media companies, to offer an official channel, which users can join, and thereby receive regular updates, published articles, or news updates from companies or news outlets. Two apps which have a large amount of such channels available are Line and Telegram. === Video group calls === == Basic default platforms == Basic platforms which are common across entire categories of mobile devices, computers, or operating systems. === SMS === SMS (short message service) is a text messaging service component of most telephone, Internet, and mobile device systems. It uses standardized communication protocols to enable mobile devices to exchange short text messages. An intermediary service can facilitate a text-to-voice conversion to be sent to landlines. SMS, as used on modern devices, originated from radio telegraphy in radio memo pagers that used standardized phone protocols. These were defined in 1985 as part of the Global System for Mobile Communications (GSM) series of standards. The first test SMS message was sent on December 3, 1992, when Neil Papwort, a test engineer for Sema Group, used a personal computer to send "Merry Christmas" to the phone of colleague Richard Jarvis. It commercially rolled out to many cellular networks that decade. SMS became hugely popular worldwide as a way of text communication. By the end of 2010, SMS was the most widely used data application, with an estimated 3.5 billion active users, or about 80% of all mobile phone subscribers. The protocols allowed users to send and receive messages of up to 160 characters (when entirely alpha-numeric) to and from GSM mobiles. Although most SMS messages are sent from one mobile phone to another, support for the service has expanded to include other mobile technologies, such as ANSI CDMA networks and Digital AMPS. Mobile marketing, a type of direct marketing, uses SMS. According to a 2018 market research report the global SMS messaging business was estimated to be worth over US$100 billion, accounting for almost 50 percent of all the revenue generated by mobile messaging. A Flash SMS is a type of SMS that appears directly on the main screen without user interaction and is not automatically stored in the inbox. It can be useful in emergencies, such as a fire alarm or cases of confidentiality, as in delivering one-time passwords. ==== Threaded SMS format ==== Threaded SMS is a visual styling orientation of SMS message history that arranges messages to and from a contact in chronological order on a single screen. It was first invented by a developer working to implement the SMS client for the BlackBerry, who was looking to make use of the blank screen left below the message on a device with a larger screen capable of displaying far more than the usual 160 characters, and was inspired by threaded Reply conversations in email. Visually, this style of representation provides a back-and-forth chat-like history for each individual contact. Hierarchical-threading at the conversation-level (as typical in blogs and on-line messaging boards) is not widely supported by SMS messaging clients. This limitation is due to the fact that there is no session identifier or subject-line passed back and forth between sent and received messages in the header data (as specified by SMS protocol) from which the client device can properly thread an incoming message to a specific dialogue, or even to a specific message within a dialogue. Most smart phone text-messaging-clients are able to create some contextual threading of "group messages" which narrows the context of the thread around the common interests shared by

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  Information extraction

  Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. Typically, this involves processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction. Recent advances in NLP techniques have allowed for significantly improved performance compared to previous years. An example is the extraction from newswire reports of corporate mergers, such as denoted by the formal relation: MergerBetween ⁡ ( c o m p a n y 1 , c o m p a n y 2 , d a t e ) {\displaystyle \operatorname {MergerBetween} (\mathrm {company} _{1},\mathrm {company} _{2},\mathrm {date} )} , from an online news sentence such as: "Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp." A broad goal of IE is to allow computation to be done on the previously unstructured data. A more specific goal is to allow automated reasoning about the logical form of the input data. Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context. Information extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR) has developed automatic methods, typically of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing (NLP) which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis, IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events in a manner that is similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator, victim, and weapon of the terroristic act, and the date on which the event happened. An IE system for this problem is required to "understand" an attack article only enough to find data corresponding to the slots in this template. == History == Information extraction dates back to the late 1970s in the early days of NLP. An early commercial system from the mid-1980s was JASPER built for Reuters by the Carnegie Group Inc with the aim of providing real-time financial news to financial traders. Beginning in 1987, IE was spurred by a series of Message Understanding Conferences. MUC is a competition-based conference that focused on the following domains: MUC-1 (1987), MUC-3 (1989): Naval operations messages. MUC-3 (1991), MUC-4 (1992): Terrorism in Latin American countries. MUC-5 (1993): Joint ventures and microelectronics domain. MUC-6 (1995): News articles on management changes. MUC-7 (1998): Satellite launch reports. Considerable support came from the U.S. Defense Advanced Research Projects Agency (DARPA), who wished to automate mundane tasks performed by government analysts, such as scanning newspapers for possible links to terrorism. == Present significance == The present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the World Wide Web, refers to the existing Internet as the web of documents and advocates that more of the content be made available as a web of data. Until this transpires, the web largely consists of unstructured documents lacking semantic metadata. Knowledge contained within these documents can be made more accessible for machine processing by means of transformation into relational form, or by marking-up with XML tags. An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with. A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted. == Tasks and subtasks == Applying information extraction to text is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more easily machine-readable text to process the sentences. Typical IE tasks and subtasks include: Template filling: Extracting a fixed set of fields from a document, e.g. extract perpetrators, victims, time, etc. from a newspaper article about a terrorist attack. Event extraction: Given an input document, output zero or more event templates. For instance, a newspaper article might describe multiple terrorist attacks. Knowledge Base Population: Fill a database of facts given a set of documents. Typically the database is in the form of triplets, (entity 1, relation, entity 2), e.g. (Barack Obama, Spouse, Michelle Obama) Named entity recognition: recognition of known entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions, by employing existing knowledge of the domain or information extracted from other sentences. Typically the recognition task involves assigning a unique identifier to the extracted entity. A simpler task is named entity detection, which aims at detecting entities without having any existing knowledge about the entity instances. For example, in processing the sentence "M. Smith likes fishing", named entity detection would denote detecting that the phrase "M. Smith" does refer to a person, but without necessarily having (or using) any knowledge about a certain M. Smith who is (or, "might be") the specific person whom that sentence is talking about. Coreference resolution: detection of coreference and anaphoric links between text entities. In IE tasks, this is typically restricted to finding links between previously extracted named entities. For example, "International Business Machines" and "IBM" refer to the same real-world entity. If we take the two sentences "M. Smith likes fishing. But he doesn't like biking", it would be beneficial to detect that "he" is referring to the previously detected person "M. Smith". Relationship extraction: identification of relations between entities, such as: PERSON works for ORGANIZATION (extracted from the sentence "Bill works for IBM.") PERSON located in LOCATION (extracted from the sentence "Bill is in France.") Semi-structured information extraction which may refer to any IE that tries to restore some kind of information structure that has been lost through publication, such as: Table extraction: finding and extracting tables from documents. Table information extraction : extracting information in structured manner from the tables. This task is more complex than table extraction, as table extraction is only the first step, while understanding the roles of the cells, rows, columns, linking the information inside the table and understanding the information presented in the table are additional tasks necessary for table information extraction. Comments extraction : extracting comments from the actual content of articles in order to restore the link between authors of each of the sentences Language and vocabulary analysis Terminology extraction: finding the relevant terms for a given corpus Audio extraction Template-based music extraction: finding relevant characteristic in an audio signal taken from a given repertoire; for instance time indexes of occurrences of percussive sounds can be extracted in order to represent the essential rhythmic component of a music piece. Note that this list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in IE. IE on non-text documents is becoming an increasingly interesting topic in research, and information extracted from multimedia documents can now be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources. == World Wide Web applications == IE has been the focus of the MUC conferences. The proliferation of the Web, however, intensified the need for developing IE systems that help people

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  G'MIC

  G'MIC (GREYC's Magic for Image Computing) is a free and open-source framework for image processing. It defines a script language that allows the creation of complex macros. Originally usable only through a command line interface, it is currently mostly popular as a GIMP plugin, and is also included in Krita. G'MIC is dual-licensed under CECILL-2.1 or CECILL-C. == Features == G'MIC's graphical interface is notable for its noise removal filters, which came from an earlier project called GREYCstoration by the same authors. G'MIC offers many built-in commands for image processing, including basic mathematical manipulations, look up tables, and filtering operations. More complex macros and pipelines built out of those commands are defined in its library files. == Interpreters == === Command line === G'MIC is primarily a script language callable from a shell. For example, to display an image: This command displays the image contained in the file image.jpg and allows zooming in to examine values. Several filters can be applied in succession. For example, to crop and resize an image: === Graphical interface === G'MIC comes with a Qt-based graphical interface, which may be integrated as a Gimp or Krita plugin. It contains several hundred filters written in the G'MIC language, dynamically updated through an internet feed. The interface provides a preview and setting sliders for each filter. G'MIC is one of the most popular Gimp plugins. === G'MIC Online === Most of the filters available for the graphical interface are also available online. === ZArt === ZArt is a graphical interface for real-time manipulation of webcam images. === libgmic === Libgmic is a C++ library that can be linked to third-party applications. It sees integration in Flowblade and Veejay.

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  Stairstep interpolation

  In the field of image processing, stairstep interpolation is a widely employed method technique for interpolating pixels after enlarging an image. The fundamental concept is to interpolate multiple times, in small increments, using any interpolation algorithm that is better than nearest-neighbor interpolation such as; bilinear interpolation, and bicubic interpolation. A common scenario is to interpolate an image by using a bicubic interpolation which increases the image size by no more than 10% (110% of the original size) at a time until the desired size is reached. Fred Miranda, a developer, popularized this method by creating and developing several Photoshop plug-ins that incorporate this technique. == Example ==

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  3D-Coat

  3DCoat is a commercial digital sculpting program from Pilgway designed to create free-form organic and hard surfaced 3D models, with tools which enable users to sculpt, add polygonal topology (automatically or manually), create UV maps (automatically or manually), texture the resulting models with natural painting tools, and render static images or animated "turntable" movies. The program can also be used to modify imported 3D models from a number of commercial 3D software products by means of plugins called Applinks. Imported models can be converted into voxel objects for further refinement and for adding high resolution detail, complete UV unwrapping and mapping, as well as adding PBR textures for displacement, bump maps, specular and diffuse color maps. A live connection to a chosen external 3D application can be established through the Applink pipeline, allowing for the transfer of model and texture information. 3DCoat specializes in voxel sculpting and polygonal sculpting using dynamic patch tessellation technology and polygonal sculpting tools. It includes "auto-retopology", a proprietary skinning algorithm which generates a polygonal mesh skin over any voxel sculpture, composed primarily of quadrangles.

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  Physical access

  Physical access is a term in computer security that refers to the ability of people to physically gain access to a computer system. According to Gregory White, "Given physical access to an office, the knowledgeable attacker will quickly be able to find the information needed to gain access to the organization's computer systems and network." == Attacks and countermeasures == === Attacks === Physical access opens up a variety of avenues for hacking. Michael Meyers notes that "the best network software security measures can be rendered useless if you fail to physically protect your systems," since an intruder could simply walk off with a server and crack the password at his leisure. Physical access also allows hardware keyloggers to be installed. An intruder may be able to boot from a CD or other external media and then read unencrypted data on the hard drive. They may also exploit a lack of access control in the boot loader; for instance, pressing F8 while certain versions of Microsoft Windows are booting, specifying 'init=/bin/sh' as a boot parameter to Linux (usually done by editing the command line in GRUB), etc. One could also use a rogue device to access a poorly secured wireless network; if the signal were sufficiently strong, one might not even need to breach the perimeter. === Countermeasures === IT security standards in the United States typically call for physical access to be limited by locked server rooms, sign-in sheets, etc. Physical access systems and IT security systems have historically been administered by separate departments of organizations, but are increasingly being seen as having interdependent functions needing a single, converged security policy. An IT department could, for instance, check security log entries for suspicious logons occurring after business hours, and then use keycard swipe records from a building access control system to narrow down the list of suspects to those who were in the building at that time. Surveillance cameras might also be used to deter or detect unauthorized access.

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  Pulse-coupled networks

  Pulse-coupled networks or pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex, and developed for high-performance biomimetic image processing. In 1989, Eckhorn introduced a neural model to emulate the mechanism of cat's visual cortex. The Eckhorn model provided a simple and effective tool for studying small mammal’s visual cortex, and was soon recognized as having significant application potential in image processing. In 1994, Johnson adapted the Eckhorn model to an image processing algorithm, calling this algorithm a pulse-coupled neural network. The basic property of the Eckhorn's linking-field model (LFM) is the coupling term. LFM is a modulation of the primary input by a biased offset factor driven by the linking input. These drive a threshold variable that decays from an initial high value. When the threshold drops below zero it is reset to a high value and the process starts over. This is different than the standard integrate-and-fire neural model, which accumulates the input until it passes an upper limit and effectively "shorts out" to cause the pulse. LFM uses this difference to sustain pulse bursts, something the standard model does not do on a single neuron level. It is valuable to understand, however, that a detailed analysis of the standard model must include a shunting term, due to the floating voltages level in the dendritic compartment(s), and in turn this causes an elegant multiple modulation effect that enables a true higher-order network (HON). A PCNN is a two-dimensional neural network. Each neuron in the network corresponds to one pixel in an input image, receiving its corresponding pixel's color information (e.g. intensity) as an external stimulus. Each neuron also connects with its neighboring neurons, receiving local stimuli from them. The external and local stimuli are combined in an internal activation system, which accumulates the stimuli until it exceeds a dynamic threshold, resulting in a pulse output. Through iterative computation, PCNN neurons produce temporal series of pulse outputs. The temporal series of pulse outputs contain information of input images and can be used for various image processing applications, such as image segmentation and feature generation. Compared with conventional image processing means, PCNNs have several significant merits, including robustness against noise, independence of geometric variations in input patterns, capability of bridging minor intensity variations in input patterns, etc. A simplified PCNN called a spiking cortical model was developed in 2009. == Applications == PCNNs are useful for image processing, as discussed in a book by Thomas Lindblad and Jason M. Kinser. PCNNs have been used in a variety of image processing applications, including: image segmentation, pattern recognition, feature generation, face extraction, motion detection, region growing, image denoising and image enhancement Multidimensional pulse image processing of chemical structure data using PCNN has been discussed by Kinser, et al. They have also been applied to an all pairs shortest path problem.

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  Shape analysis (digital geometry)

  This article describes shape analysis to analyze and process geometric shapes. == Description == Shape analysis is the (mostly) automatic analysis of geometric shapes, for example using a computer to detect similarly shaped objects in a database or parts that fit together. For a computer to automatically analyze and process geometric shapes, the objects have to be represented in a digital form. Most commonly a boundary representation is used to describe the object with its boundary (usually the outer shell, see also 3D model). However, other volume based representations (e.g. constructive solid geometry) or point based representations (point clouds) can be used to represent shape. Once the objects are given, either by modeling (computer-aided design), by scanning (3D scanner) or by extracting shape from 2D or 3D images, they have to be simplified before a comparison can be achieved. The simplified representation is often called a shape descriptor (or fingerprint, signature). These simplified representations try to carry most of the important information, while being easier to handle, to store and to compare than the shapes directly. A complete shape descriptor is a representation that can be used to completely reconstruct the original object (for example the medial axis transform). == Application fields == Shape analysis is used in many application fields: archeology for example, to find similar objects or missing parts architecture for example, to identify objects that spatially fit into a specific space medical imaging to understand shape changes related to illness or aid surgical planning virtual environments or on the 3D model market to identify objects for copyright purposes security applications such as face recognition entertainment industry (movies, games) to construct and process geometric models or animations computer-aided design and computer-aided manufacturing to process and to compare designs of mechanical parts or design objects. == Shape descriptors == Shape descriptors can be classified by their invariance with respect to the transformations allowed in the associated shape definition. Many descriptors are invariant with respect to congruency, meaning that congruent shapes (shapes that could be translated, rotated and mirrored) will have the same descriptor (for example moment or spherical harmonic based descriptors or Procrustes analysis operating on point clouds). Another class of shape descriptors (called intrinsic shape descriptors) is invariant with respect to isometry. These descriptors do not change with different isometric embeddings of the shape. Their advantage is that they can be applied nicely to deformable objects (e.g. a person in different body postures) as these deformations do not involve much stretching but are in fact near-isometric. Such descriptors are commonly based on geodesic distances measures along the surface of an object or on other isometry invariant characteristics such as the Laplace–Beltrami spectrum (see also spectral shape analysis). There are other shape descriptors, such as graph-based descriptors like the medial axis or the Reeb graph that capture geometric and/or topological information and simplify the shape representation but can not be as easily compared as descriptors that represent shape as a vector of numbers. From this discussion it becomes clear, that different shape descriptors target different aspects of shape and can be used for a specific application. Therefore, depending on the application, it is necessary to analyze how well a descriptor captures the features of interest.

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  BeeSafe

  BeeSafe is a personal safety mobile app launched in 2015 as a Slovak startup. It is a location-based security service that notifies family members and friends in case the user of the app gets in danger. The app has received numerous awards. The app has more than 700 downloads and 250 active logins from more than 60 countries worldwide. == History == BeeSafe was founded on March 20, 2015 by Peter Stražovec and Michal Kačerík. The project was a winner of Žilina’s Startup Weekend 2013 and a StartupAwards.SK 2015 finalist. Later on, the app was released in the Android and iOS marketplace. The whole BeeSafe project was in The Spot booster and incubator in Bratislava for three months. BeeSafe entered into an agreement with the city of Piešťany in November 2015 to increase the security of its citizen by connecting the mobile app with the police platform. It is the first city that started using the BeeSafe platform. Further on, the application tries to help people in other Slovak cities. The cities can see the users only if they are in danger. == Awards == BeeSafe app received the Via Bona award, it is a winner of a Slovak startup and has other nominations too.

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  Lucy–Hook coaddition method

  The Lucy–Hook coaddition method is an image processing technique for combining sub-stepped astronomical image data onto a finer grid. The method allows the option of resolution and contrast enhancement or the choice of a conservative, re-convolved, output. Tests with very deep Hubble Space Telescope Wide Field and Planetary Camera 2 (WFPC2) imaging data of excellent quality show that these methods can be very effective and allow fine-scale features to be studied better than on the unprocessed images. The Lucy–Hook coaddition method is an extension of the standard Richardson–Lucy deconvolution iterative restoration method. For many purposes it may be more convenient to combine dithered datasets using the Drizzle method.

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  Autonomous aircraft

  An autonomous aircraft is an aircraft which flies under the control of on-board autonomous robotic systems and needs no intervention from a human pilot or remote control. Most contemporary autonomous aircraft are unmanned aerial vehicles (drones) with pre-programmed algorithms to perform designated tasks, but advancements in artificial intelligence technologies (e.g. machine learning) mean that autonomous control systems are reaching a point where several air taxis and associated regulatory regimes are being developed. == History == === Unmanned aerial vehicles === The earliest recorded use of an unmanned aerial vehicle for warfighting occurred in July 1849, serving as a balloon carrier (the precursor to the aircraft carrier) Significant development of radio-controlled drones started in the early 1900s, and originally focused on providing practice targets for training military personnel. The earliest attempt at a powered UAV was A. M. Low's "Aerial Target" in 1916. Autonomous features such as the autopilot and automated navigation were developed progressively through the twentieth century, although techniques such as terrain contour matching (TERCOM) were applied mainly to cruise missiles. Before the introduction of the Bayraktar Kızılelma some modern drones have a high degree of autonomy, although they were not fully capable and the regulatory environment prohibits their widespread use in civil aviation. However some limited trials had been undertaken. On December 17, 2025, two Bayraktar Kızılelma performed the world's first autonomous close-formation flight by two unmanned fighter jets, using artificial intelligence. This was the first time in the history of aviation when two unmanned aerial vehicles flew in close formation on their own. === Passengers === As flight, navigation and communications systems have become more sophisticated, safely carrying passengers has emerged as a practical possibility. Autopilot systems are relieving the human pilot of progressively more duties, but the pilot currently remains necessary. A number of air taxis are under development and larger autonomous transports are also being planned. The personal air vehicle is another class where from one to four passengers are not expected to be able to pilot the aircraft and autonomy is seen as necessary for widespread adoption. == Control system architecture == The computing capability of aircraft flight and navigation systems followed the advances of computing technology, beginning with analog controls and evolving into microcontrollers, then system-on-a-chip (SOC) and single-board computers (SBC). === Sensors === Position and movement sensors give information about the aircraft state. Exteroceptive sensors deal with external information like distance measurements, while proprioceptive ones correlate internal and external states. Degrees of freedom (DOF) refers to both the amount and quality of sensors on board: 6 DOF implies 3-axis gyroscopes and accelerometers (a typical inertial measurement unit – IMU), 9 DOF refers to an IMU plus a compass, 10 DOF adds a barometer and 11 DOF usually adds a GPS receiver. === Actuators === UAV actuators include digital electronic speed controllers (which control the RPM of the motors) linked to motors/engines and propellers, servomotors (for planes and helicopters mostly), weapons, payload actuators, LEDs and speakers. === Software === UAV software called the flight stack or autopilot. The purpose of the flight stack is to obtain data from sensors, control motors to ensure UAV stability, and facilitate ground control and mission planning communication. UAVs are real-time systems that require rapid response to changing sensor data. As a result, UAVs rely on single-board computers for their computational needs. Examples of such single-board computers include Raspberry Pis, Beagleboards, etc. shielded with NavIO, PXFMini, etc. or designed from scratch such as NuttX, preemptive-RT Linux, Xenomai, Orocos-Robot Operating System or DDS-ROS 2.0. Civil-use open-source stacks include: Due to the open-source nature of UAV software, they can be customized to fit specific applications. For example, researchers from the Technical University of Košice have replaced the default control algorithm of the PX4 autopilot. This flexibility and collaborative effort has led to a large number of different open-source stacks, some of which are forked from others, such as CleanFlight, which is forked from BaseFlight and from which three other stacks are forked from. === Loop principles === UAVs employ open-loop, closed-loop or hybrid control architectures. Open loop – This type provides a positive control signal (faster, slower, left, right, up, down) without incorporating feedback from sensor data. Closed loop – This type incorporates sensor feedback to adjust behavior (reduce speed to reflect tailwind, move to altitude 300 feet). The PID controller is common. Sometimes, feedforward is employed, transferring the need to close the loop further. == Communications == Most UAVs use a radio for remote control and exchange of video and other data. Early UAVs had only narrowband uplink. Downlinks came later. These bi-directional narrowband radio links carried command and control (C&C) and telemetry data about the status of aircraft systems to the remote operator. For very long range flights, military UAVs also use satellite receivers as part of satellite navigation systems. In cases when video transmission was required, the UAVs will implement a separate analog video radio link. In most modern autonomous applications, video transmission is required. A broadband link is used to carry all types of data on a single radio link. These broadband links can leverage quality of service techniques to optimize the C&C traffic for low latency. Usually, these broadband links carry TCP/IP traffic that can be routed over the Internet. Communications can be established with: Ground control – a military ground control station (GCS). The MAVLink protocol is increasingly becoming popular to carry command and control data between the ground control and the vehicle. Remote network system, such as satellite duplex data links for some military powers. Downstream digital video over mobile networks has also entered consumer markets, while direct UAV control uplink over the cellular mesh and LTE have been demonstrated and are in trials. Another aircraft, serving as a relay or mobile control station – military manned-unmanned teaming (MUM-T). As mobile networks have increased in performance and reliability over the years, drones have begun to use mobile networks for communication. Mobile networks can be used for drone tracking, remote piloting, over the air updates, and cloud computing. Modern networking standards have explicitly considered autonomous aircraft and therefore include optimizations. The 5G standard has mandated reduced user plane latency to 1ms while using ultra-reliable and low-latency communications. == Autonomy == Basic autonomy comes from proprioceptive sensors. Advanced autonomy calls for situational awareness, knowledge about the environment surrounding the aircraft from exteroceptive sensors: sensor fusion integrates information from multiple sensors. Civil aviation regulators and standards bodies have published high-level roadmaps and discussion papers focused on assurance, safety and governance of AI-enabled systems in aviation, particularly as autonomy increases in operations and decision support. === Basic principles === One way to achieve autonomous control employs multiple control-loop layers, as in hierarchical control systems. As of 2016 the low-layer loops (i.e. for flight control) tick as fast as 32,000 times per second, while higher-level loops may cycle once per second. The principle is to decompose the aircraft's behavior into manageable "chunks", or states, with known transitions. Hierarchical control system types range from simple scripts to finite state machines, behavior trees and hierarchical task planners. The most common control mechanism used in these layers is the PID controller which can be used to achieve hover for a quadcopter by using data from the IMU to calculate precise inputs for the electronic speed controllers and motors. Examples of mid-layer algorithms: Path planning: determining an optimal path for vehicle to follow while meeting mission objectives and constraints, such as obstacles or fuel requirements Trajectory generation (motion planning): determining control maneuvers to take in order to follow a given path or to go from one location to another Trajectory regulation: constraining a vehicle within some tolerance to a trajectory Evolved UAV hierarchical task planners use methods like state tree searches or genetic algorithms. === Autonomy features === UAV manufacturers often build in specific autonomous operations, such as: Self-level: attitude stabilization on the pitch and roll axes. Altitude hold: The aircraft maint

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  Dyme (company)

  Dyme is a Dutch fintech start-up and subscription management app that allows users to cancel and renegotiate their recurring costs. In 2019, Dyme was the first independent Dutch company to receive a PSD2 licence from the Netherlands' central bank (DNB). == History == Dyme was founded in 2018 by Joran Iedema, David Knap, David Schogt and Wouter Florijn. The four had previously founded Cycleswap, a bicycle rental platform launched in 2015 and sold to the American platform Spinlister in 2016. The company gained notability in the Netherlands in 2020 when it appeared on Dutch television in Dragons Den, where Pieter Schoen made a €750,000 bid in an attempt to acquire 51.01% of the company. Dyme's Joran Iedema rejected the deal. == Recognition == Wired described Dyme as one of the "hottest start-ups in Europe" in 2021. As of 2021, the company reportedly had 350,000 registered users in the Netherlands and Great Britain.

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