AI Email Message Generator

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  World Database of Happiness

  The World Database of Happiness is a web-based archive of research findings on subjective appreciation of life, based in the Erasmus Happiness Economics Research Organization of the Erasmus University Rotterdam in The Netherlands. The database contains both an overview of scientific publications on happiness and a digest of research findings. Happiness is defined as the degree to which an individual judges the quality of his or her life as a whole favorably. Two 'components' of happiness are distinguished: hedonic level of affect (the degree to which pleasant affect dominates) and contentment (perceived realization of wants). == Aims == The World Database of Happiness is a tool to quickly acquire an overview on the ever-growing stream of research findings on happiness Medio 2023 the database covered some 16,000 scientific publications on happiness, from which were extracted 23,000 distributional findings (on how happy people are) and another 24,000 correlational findings (on factors associated with more and less happiness). The first findings date from 1915. == Technique == The World Database of Happiness is a ‘findings archive’, which consists of electronic ‘finding pages’ on which separate research results are described in a standard format and terminology. These finding pages can be selected on various characteristics, such as population studies, the measure of happiness used and observed co-variates. All finding-pages have a specific internet address to which links can be made in scientific review papers or policy recommendations. This allows a concise presentation of many findings in a table, while providing readers with access to detail. == Scientific use == The Database has been cited in 254 scientific papers, for example to access under what conditions economic growth enhances average happiness or to show that rising mean happiness at first raises happiness inequality, but further rise will diminish these differences, or that healthy eating is associated with more happiness, even after controlling for the effect on health Another finding is that relative simple happiness training techniques raise happiness by some 5% == Popular use == The World Database of Happiness is often used by popular media to make lists of the happiest countries around the globe. An example is the Happy Planet Index, which aims to chart sustainable happiness all over the world by combining data on longevity, happiness and the size of the ecological footprint of citizens. == Strengths and weaknesses == The database has a clear conceptual focus, it includes only research findings on subjective enjoyment of one's life as a whole. Thereby it evades the Babel that has haunted the study of happiness for ages. The other side of that coin is that much interesting research is left out. The findings are reported with technical details about measurement and statistical analysis. This detail is welcomed by scholars, but makes the information difficult to digest for lay-persons. Still another limitation is that the determinants of happiness appear to vary considerably across persons and situations, which make it hard to draw general conclusions about the causes of happiness. What is clear is that poor health, separation, unemployment and lack of social contact are all strongly negatively associated with happiness. Another problem for the World database of happiness is that the studies on happiness increase with such a high rate that it gets increasingly difficult to offer a complete overview of all research findings. A further concern is that the Database of Happiness is exclusively focused on hedonic happiness (feeling good) and not on mature happiness that might exist in the face of suffering

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  Cut, copy, and paste

  Cut, copy, and paste are essential commands of modern human–computer interaction and user interface design. They offer an interprocess communication technique for transferring data through a computer's user interface. The cut command removes the selected data from its original position, and the copy command creates a duplicate; in both cases the selected data is kept in temporary storage called the clipboard. Clipboard data is later inserted wherever a paste command is issued. The data remains available to any application supporting the feature, thus allowing easy data transfer between applications. The command names are a (skeuomorphic) interface metaphor based on the physical procedure used in manuscript print editing to create a page layout, like with paper. The commands were pioneered into computing by Xerox PARC in 1974, popularized by Apple Computer in the 1983 Lisa workstation and the 1984 Macintosh computer, and in a few home computer applications such as the 1984 word processor Cut & Paste. This interaction technique has close associations with related techniques in graphical user interfaces (GUIs) that use pointing devices such as a computer mouse (by drag and drop, for example). Typically, clipboard support is provided by an operating system as part of its GUI and widget toolkit. The capability to replicate information with ease, changing it between contexts and applications, involves privacy concerns because of the risks of disclosure when handling sensitive information. Terms like cloning, copy forward, carry forward, or re-use refer to the dissemination of such information through documents, and may be subject to regulation by administrative bodies. == History == === Origins === The term "cut and paste" comes from the traditional practice in manuscript editing, whereby people cut paragraphs from a page with scissors and paste them onto another page. This practice remained standard into the 1980s. Stationery stores sold "editing scissors" with blades long enough to cut an 8½"-wide page. The advent of photocopiers made the practice easier and more flexible. The act of copying or transferring text from one part of a computer-based document ("buffer") to a different location within the same or different computer-based document was a part of the earliest on-line computer editors. As soon as computer data entry moved from punch-cards to online files (in the mid/late 1960s) there were "commands" for accomplishing this operation. This mechanism was often used to transfer frequently-used commands or text snippets from additional buffers into the document, as was the case with the QED text editor. === Early methods === The earliest editors (designed for teleprinter terminals) provided keyboard commands to delineate a contiguous region of text, then delete or move it. Since moving a region of text requires first removing it from its initial location and then inserting it into its new location, various schemes had to be invented to allow for this multi-step process to be specified by the user. Often this was done with a "move" command, but some text editors required that the text be first put into some temporary location for later retrieval/placement. In 1983, the Apple Lisa became the first text editing system to call that temporary location "the clipboard". Earlier control schemes such as NLS used a verb—object command structure, where the command name was provided first and the object to be copied or moved was second. The inversion from verb—object to object—verb on which copy and paste are based, where the user selects the object to be operated before initiating the operation, was an innovation crucial for the success of the desktop metaphor as it allowed copy and move operations based on direct manipulation. === Popularization === Inspired by early line and character editors, such as Pentti Kanerva's TV-Edit, that broke a move or copy operation into two steps—between which the user could invoke a preparatory action such as navigation—Lawrence G. "Larry" Tesler proposed the names "cut" and "copy" for the first step and "paste" for the second step. Beginning in 1974, he and colleagues at Xerox PARC implemented several text editors that used cut/copy-and-paste commands to move and copy text. Apple Computer popularized this paradigm with its Lisa (1983) and Macintosh (1984) operating systems and applications. The functions were mapped to key combinations using the ⌘ Command key as a special modifier, which is held down while also pressing X for cut, C for copy, or V for paste. These few keyboard shortcuts allow the user to perform all the basic editing operations, and the keys are clustered at the left end of the bottom row of the standard QWERTY keyboard. These are the standard shortcuts: Control-Z (or ⌘ Command+Z) to undo Control-X (or ⌘ Command+X) to cut Control-C (or ⌘ Command+C) to copy Control-V (or ⌘ Command+V) to paste The IBM Common User Access (CUA) standard also uses combinations of the Insert, Del, Shift and Control keys. Early versions of Windows used the IBM standard. Microsoft later also adopted the Apple key combinations with the introduction of Windows, using the control key as modifier key. Similar patterns of key combinations, later borrowed by others, are widely available in most GUI applications. The original cut, copy, and paste workflow, as implemented at PARC, utilizes a unique workflow: With two windows on the same screen, the user could use the mouse to pick a point at which to make an insertion in one window (or a segment of text to replace). Then, by holding shift and selecting the copy source elsewhere on the same screen, the copy would be made as soon as the shift was released. Similarly, holding shift and control would copy and cut (delete) the source. This workflow requires many fewer keystrokes/mouse clicks than the current multi-step workflows, and did not require an explicit copy buffer. It was dropped, one presumes, because the original Apple and IBM GUIs were not high enough density to permit multiple windows, as were the PARC machines, and so multiple simultaneous windows were rarely used. == Cut and paste == Computer-based editing can involve very frequent use of cut-and-paste operations. Most software-suppliers provide several methods for performing such tasks, and this can involve (for example) key combinations, pulldown menus, pop-up menus, or toolbar buttons. The user selects or "highlights" the text or file for moving by some method, typically by dragging over the text or file name with the pointing-device or holding down the Shift key while using the arrow keys to move the text cursor. The user performs a "cut" operation via key combination Ctrl+x (⌘+x for Macintosh users), menu, or other means. Visibly, "cut" text immediately disappears from its location. "Cut" files typically change color to indicate that they will be moved. Conceptually, the text has now moved to a location often called the clipboard. The clipboard typically remains invisible. On most systems only one clipboard location exists, hence another cut or copy operation overwrites the previously stored information. Many UNIX text-editors provide multiple clipboard entries, as do some Macintosh programs such as Clipboard Master, and Windows clipboard-manager programs such as the one in Microsoft Office. The user selects a location for insertion by some method, typically by clicking at the desired insertion point. A paste operation takes place which visibly inserts the clipboard text at the insertion point. (The paste operation does not typically destroy the clipboard text: it remains available in the clipboard and the user can insert additional copies at other points). Whereas cut-and-paste often takes place with a mouse-equivalent in Windows-like GUI environments, it may also occur entirely from the keyboard, especially in UNIX text editors, such as Pico or vi. Cutting and pasting without a mouse can involve a selection (for which Ctrl+x is pressed in most graphical systems) or the entire current line, but it may also involve text after the cursor until the end of the line and other more sophisticated operations. The clipboard usually stays invisible, because the operations of cutting and pasting, while actually independent, usually take place in quick succession, and the user (usually) needs no assistance in understanding the operation or maintaining mental context. Some application programs provide a means of viewing, or sometimes even editing, the data on the clipboard. == Copy and paste == The term "copy-and-paste" refers to the popular, simple method of reproducing text or other data from a source to a destination. It differs from cut and paste in that the original source text or data does not get deleted or removed. The popularity of this method stems from its simplicity and the ease with which users can move data between various applications visually – without resorting to permanent storage. Use in healthcare do

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  Media contacts database

  In public relations (PR) and marketing, a media contacts database is a resource which catalogs the names, contact information, and other details about people who work in various media professions. These include journalists, reporters, editors, publishers, contributors, freelance journalists, opinion writers, social media personalities/ influencers, TV show anchors, radio show hosts, DJs, and others. A media contacts database usually contains the following information: Full name of the media contact, The publication or channel they work for Designations (past and present) Topics they cover, or their beat Contact information found in public domains Online presence like blogs and other social networking sites Education Information == Overview == A media contacts database is a public relations tool that is maintained and used by PR professionals to pitch stories on a particular topic, product, or company to a specific group of journalists. These journalists would then write or speak about the particular topic in a relevant issue or episode of their shows. A media contacts database allows a PR professional to gain easy access to hundreds of journalists within a short span of time. Media contacts database are created and sold by many media research companies that offer such PR software for professionals.

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  Cambridge Analytica

  Cambridge Analytica Ltd. (CA), previously known as SCL USA, was a British political consulting firm that came to prominence through the Facebook–Cambridge Analytica data scandal. It was founded in 2013, as a subsidiary of the private intelligence company and self-described "global election management agency" SCL Group by long-time SCL executives Nigel Oakes, Alexander Nix and Alexander Oakes, with Nix as CEO. Cambridge Analytica was hired by a variety of political actors, including the Trinidadian government in 2010 and the 2016 presidential campaigns of Ted Cruz and Donald Trump. The firm maintained offices in London, New York City, and Washington, D.C. The company closed operations in 2018 due to backlash from the scandal, although firms related to both Cambridge Analytica and its parent firm SCL still exist. == History == Cambridge Analytica was founded in 2013 as a subsidiary of the private intelligence company SCL Group, which describes itself as providing "data, analytics and strategy to governments and military organisations worldwide". The company was part of "an international web of companies" headed by the London-based SCL Group. Cambridge Analytica (SCL USA) was incorporated in January 2013 with its registered office being in Westferry Circus, London and consisting of just one staff member, director and CEO Alexander Nix (also appointed in January 2015). Nix was also the director of nine similar companies sharing the same registered offices in London, including Firecrest technologies, Emerdata and six SCL Group companies including "SCL elections limited". Nigel Oakes, known as the former boyfriend of Lady Helen Windsor, had founded the predecessor SCL Group in the 1990s, and in 2005 Oakes established SCL Group together with his brother Alexander Oakes and Alexander Nix; SCL Group was the parent company of Cambridge Analytica. Former Conservative minister and MP Sir Geoffrey Pattie was the founding chairman of SCL; Lord Ivar Mountbatten also joined Oakes as a director of the company. As a result of the Facebook–Cambridge Analytica data scandal, Nix was removed as CEO and replaced by Julian Wheatland before the company closed. Several of the company's executives were Old Etonians. The company's owners included several of the Conservative Party's largest donors such as billionaire Vincent Tchenguiz, former British Conservative minister Jonathan Marland, Baron Marland and the family of American hedge fund manager Robert Mercer. The company combined misappropriation of digital assets, data mining, data brokerage, and data analysis with strategic communication during electoral processes. While its parent SCL had focused on influencing elections in developing countries since the 1990s, Cambridge Analytica focused more on the western world, including the United Kingdom and the United States; CEO Alexander Nix has said CA was involved in 44 U.S. political races in 2014. In 2015, CA performed data analysis services for Ted Cruz's presidential campaign. In 2016, CA worked for Donald Trump's presidential campaign as well as for Leave.EU (one of the organisations campaigning in the United Kingdom's referendum on European Union membership). CA's role in those campaigns has been controversial and is the subject of ongoing inquiries in both countries. Political scientists question CA's claims about the effectiveness of its methods of targeting voters. == Data scandal == In March 2018, media outlets broke news of Cambridge Analytica's business practices. The New York Times and The Observer reported that the company had acquired and used personal data about Facebook users from an external researcher who had told Facebook he was collecting it for academic purposes. Shortly afterwards, Channel 4 News aired undercover investigative videos showing Nix boasting about using prostitutes, bribery sting operations, and honey traps to discredit politicians on whom it had conducted opposition research, and saying that the company "ran all of (Donald Trump's) digital campaign". In response to the media reports, the Information Commissioner's Office (ICO) of the UK pursued a warrant to search the company's servers. Facebook banned Cambridge Analytica from advertising on its platform, saying that it had been deceived. On 23 March 2018, the British High Court granted the ICO a warrant to search Cambridge Analytica's London offices. As a result, Nix was suspended as CEO, and replaced by Julian Wheatland. The personal data of up to 87 million Facebook users were acquired via the 270,000 Facebook users who used a Facebook app created by Aleksandr Kogan called "This Is Your Digital Life". This was a personality profiling app and asked simple personality questions similar to other Facebook quizzes. Kogan was a scientist and psychologist, also being an employed lecturer for the University of Cambridge from 2012 to 2018. Alexander Nix claimed they had close to five thousand data points on each person who participated. They also gathered information through other data brokers ending with them acquiring millions of data points from American citizens. Kogan's app exploited a feature of Facebook's Graph API (version 1.0), which permitted any third-party app to access not only the app user's data, but also the full profile data of all of that user's Facebook friends, without those friends' knowledge or consent. This platform-wide design was available to all developers and was used by tens of thousands of apps; Facebook CEO Mark Zuckerberg later told the House Energy and Commerce Committee that the company was auditing "tens of thousands" of apps that had had access to large amounts of user data. Because the average Facebook user at the time had approximately 300 friends, the 270,000 users who installed Kogan's app yielded data on up to 87 million people. Facebook deprecated the friends-data API in April 2014 and shut it down entirely in April 2015, but data already collected by apps remained in developers' possession. Kogan passed this data to Cambridge Analytica, breaching Facebook's terms of service. On 1 May 2018, Cambridge Analytica and its parent company SCL filed for insolvency proceedings and closed operations. Alexander Tayler, a former director for Cambridge Analytica, was appointed director of Emerdata on 28 March 2018. Rebekah Mercer, Jennifer Mercer, Alexander Nix and Johnson Chun Shun Ko, who has links to American businessman Erik Prince, are in leadership positions at Emerdata. The Russo brothers are producing an upcoming film on Cambridge Analytica. In 2019 the Federal Trade Commission filed an administrative complaint against Cambridge Analytica for misuse of data. In 2020, the British Information Commissioner's Office closed a three-year inquiry into the company, concluded that Cambridge Analytica was "not involved" in the 2016 Brexit referendum and found no additional evidence for Russia's alleged interference during the campaign. US sensitive polling and election data, however, were passed to Russian Intelligence via a Cambridge Analytica contractor Sam Patten, Trump campaign manager Paul Manafort, and Russian agent Konstantin Kilimnik, who was indicted during the affair. Publicly, parent company SCL Group called itself a "global election management agency", Politico reported it was known for involvement "in military disinformation campaigns to social media branding and voter targeting". SCL gained work on a large number of campaigns for the US and UK governments' war on terror advancing their model of behavioral conflict during the 2000s. SCL's involvement in the political world has been primarily in the developing world where it has been used by the military and politicians to study and manipulate public opinion and political will. Slate writer Sharon Weinberger compared one of SCL's hypothetical test scenarios to fomenting a coup. Among the investors in Cambridge Analytica were some of the Conservative Party's largest donors such as billionaire Vincent Tchenguiz, former Conservative minister Jonathan Marland, Baron Marland, Roger Gabb, the family of American hedge fund manager Robert Mercer, and Steve Bannon. A minimum of 15 million dollars has been invested into the company by Mercer, according to The New York Times. Bannon's stake in the company was estimated at 1 to 5 million dollars, but he divested his holdings in April 2017 as required by his role as White House Chief Strategist. In March 2018, Jennifer Mercer and Rebekah Mercer became directors of Emerdata limited. In March 2018 it became public by Christopher Wylie, that Cambridge Analytica's first activities were founded on a data set, which its parent company SCL bought 2014 from a company named Global Science Research founded by Aleksandr Kogan and his team present across the world who worked as a psychologist at Cambridge. During Boris Johnson's tenure as foreign secretary, the Foreign Office sought advice from Cambridge Analytica and Boris Johnson had a meeting with Alexander N

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  Color picker

  A color picker (also color chooser or color tool) is a graphical user interface widget, usually found within graphics software or online, used to select colors and, in some cases, to create color schemes (the color picker might be more sophisticated than the palette included with the program). Operating systems such as Microsoft Windows or macOS have a system color picker, which can be used by third-party programs (e.g., Adobe Photoshop). == History == The concept of color pickers dates back to the early days of computer graphics and digital design. Early versions were rudimentary, often featuring basic color palettes and limited functionality. One of the first drawing programs to include a color picker was SketchPad (also referred to as LisaSketch), designed by Bill Atkinson in 1983 to showcase LisaGraf's capabilities. It used a black and white pattern system, using dithering to create the illusion of color depth. With the increased popularity of personal computers with color graphics, there soon came software similar to SketchPad that supported more than two colors, like Broderbund's Dazzle Draw for the Apple II or Electronic Arts' Deluxe Paint. However, the color pickers present in those programs relied on indexed colors. Color pickers, resembling ones used in modern software with support for direct, 24-bit color, appeared soon after the release of the Macintosh II, with the release of programs like Adobe Photoshop and Corel Painter. As the increase of color depth allowed the choice of significantly more colors, the shape and form of color pickers started to diverge. For example, Adobe Photoshop used a hue-saturation color wheel with a slider for brightness in version 0.63, later on switching to a rectangular design accompanied by a hue slider. Corel Painter pioneered the triangular saturation and brightness picker with a hue ring around it, aiming to better represent the continuity of the hue spectrum and the relationship between saturation and brightness. == Purpose == A color picker is used to select and adjust color values. In graphic design and image editing, users typically choose colors via an interface with a visual representation of a color—organized with quasi-perceptually-relevant hue, saturation and lightness dimensions (HSL) – instead of keying in alphanumeric text values. Because color appearance depends on comparison of neighboring colors (see color vision), many interfaces attempt to clarify the relationships between colors. == Interface == Color tools can vary in their interface. Some may use sliders, buttons, text boxes for color values, or direct manipulation. Often a two-dimensional square is used to create a range of color values (such as lightness and saturation) that can be clicked on or selected in some other manner. Drag and drop, color droppers, and various other forms of interfaces are commonly used as well. Usually, color values are also displayed numerically, so they can be precisely remembered and keyed-in later, such as three values of 0-255 representing red, green, and blue, respectively. === Eyedropper === The eyedropper is a tool present in most color pickers and graphics software that allows a user to read a color at a specific point in an image, or position on a display. This enables the color to be transferred to other applications particularly quickly. Modern implementations of eyedropper tools are also available as browser extensions, allowing users to pick colors directly from web pages, such as in Google Chrome and Microsoft Edge. == Working == A color picker has two main parts, first a color slider and second a color canvas. The color slider has a linear or radial gradient of the seven rainbow colors i.e. Violet, Indigo, Blue, Green, Yellow, Orange and Red. It allows one to choose any of the seven primary colors. The color value chosen from the color slider instantly reflects in the color canvas. The color canvas is a mixture of two linear color gradients. First a linear gradient of the current chosen color and second a linear gradient of the black color. This mixture of color gradients lets one choose a lighter and darker version of the current chosen color from the color slider.

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  NATGRID

  The National Intelligence Grid or NATGRID is an integrated intelligence master database structure for counter-terrorism purposes which connects databases of various core security agencies under the Government of India. It collects and analyses comprehensive patterns procured from 21 different organizations that can be readily accessed by security agencies round the clock. As of September 2025 its CEO is Hirdesh Kumar. NATGRID came into existence after the 2008 Mumbai attacks. The Government of India in July 2016 appointed Ashok Patnaik as the Chief Executive Officer (CEO) of NATGRID. The appointment is being seen as the government's effort to revive the project. Patnaik's appointment was valid till 31 December 2018. As of 2019, NATGRID is headed by an Indian Police Service (IPS) officer Ashish Gupta. The Ministry of Home Affairs on 5 February 2020 announced in Parliament that Project NATGRID with all its required physical infrastructures been completed as of 31 March 2020 and the NATGRID solution went live as of 31 December 2020. == Reason for establishment == The landscape of Terrorism in India and the subsequent response by Law enforcement in India have necessitated a sophisticated data-integration framework, positioning NATGRID as a vital tool for national security agencies. This shift towards Mass surveillance in India is rooted in a broader policy evolution of state monitoring, which is technologically enabled by the India Stack—the foundational digital infrastructure providing the API-based backbone for government service delivery and identity verification. This ecosystem is further bolstered by advanced Signal intelligence capabilities and the implementation of SIM binding, a security protocol that anchors a user’s digital identity to a specific mobile device and verified SIM card to prevent identity fraud and unauthorized access. Collectively, these elements form a 360-degree surveillance and authentication grid designed to preemptively identify threats by synthesizing historical, financial, and real-time communication data across disparate platforms. === Terror attacks in India === The 2008 Mumbai attacks led to the exposure of several weaknesses in India's intelligence gathering and action networks. NATGRID is part of the radical overhaul of the security and intelligence apparatuses of India that was mooted by the then Home Minister P. Chidambaram in 2009. The National Investigation Agency (NIA) and the National Counter Terrorism Centre (NCTC) are two organisations established in the aftermath of the Mumbai attacks of 2008. Before the Mumbai attacks, a Pakistani origin American Lashkar-e-Taiba (LeT) operative David Coleman Headley had visited India several times and done a recce of the places that came under attack on 26/11. Despite having travelled to India several times and having returned to the US through Pakistan or West Asia, his trips failed to raise the suspicion of Indian agencies as they lacked a system that could reveal a pattern in his unusual travel itineraries and trips to the country. It was argued that if they had a system like the NATGRID in place, Headley would have been apprehended well before the attacks. === Need for the integrated intelligence system === During the inauguration of NATGRID campus in Bengaluru, the Minister of Home Affairs, Amit Shah stated that a new national database is in the process of being made which will bring a change in the current ways of functioning of agencies once it's ready also adding that the government has entrusted the task of developing and operating a state-of-the-art and innovative technology system. It is accessible to 11 central agencies in the first phase and in later phases will be made accessible to police of all States and Union Territories and only authorized personnel are allowed access to the platform on a case-to-case basis for investigations into suspected cases of terrorism. NATGRID has a total fund allocation of ₹3,400 crore (US$355 million). d == Legal framework == Relevant legal framework: Digital Personal Data Protection Act, 2023 – The legislative framework governing how digital data is handled. Information Technology Act - Interception Rules, 2002 – The specific regulations under the Information Technology Act that govern these agencies. National Security Act of 1980, evidence-based preventative detention of suspects Right to Information Act, 2005, for obtaining information from the government and used by activists and whistleblowers == Structure and functions == === Multi-agency integrated intelligence database === NATGRID is an intelligence sharing network that collates data from the standalone databases of the various agencies and ministries of the Indian government. It is a counter terrorism measure that collects and collates a host of information from government databases including tax and bank account details, credit/debit card transactions, visa and immigration records and itineraries of rail and air travel. It also has access to the Crime and Criminal Tracking Network and Systems, a database that links crime information, including First Information Reports, across 14,000 police stations in India. This combined data will be made available to 11 central agencies, which are: the Research and Analysis Wing (R&AW), Intelligence Bureau (IB), National Investigation Agency (NIA), Central Bureau of Investigation (CBI), Narcotics Control Bureau (NCB), Financial Intelligence Unit (India) (FIU), Enforcement Directorate (ED), Central Board of Direct Taxes (CBDT), Central Board of Indirect Taxes and Customs (CBIC), Directorate of Revenue Intelligence (DRI) and Directorate General of GST Intelligence. Also as stated by the MHA, NATGRID will have an in-built mechanism for continuous upgradation. In the later phases of NATGRID integration, the central government further plans to integrate 950 additional organizations into it. === Key components and users === ==== Some important backend data feeds to the NATGRID (middleware) ==== National Crime Records Bureau's Crime and Criminal Tracking Network and Systems (CCTNS) national-integrated law-and-order database for the state-level police forces: CCTNS is a mission-mode project under the National e-Governance Plan that interconnects over 15,000 police stations across India. It serves as the primary source for NATGRID to access digitized FIR (First Information Report) data and criminal history records from state-level law enforcement. NSA's National Technical Research Organisation (NTRO) national security-based database feed to NATGRID: NTRO serves as a primary technical data provider to NATGRID, offering specialized intercepts and satellite imagery. While NATGRID functions as a centralized data-integration middleware under the Ministry of Home Affairs, NTRO reports to the National Security Advisor within the Prime Minister's Office. DRDO's NETRA (Network Traffic Analysis) ELINT-based mass surveillance system for monitor internal internet traffic for keywords related to terrorism and criminal activity within Indian borders: Developed by the Centre for Artificial Intelligence and Robotics (CAIR), NETRA is an internet monitoring system capable of scanning traffic for specific trigger words. It provides digital behavioral triggers that NATGRID can cross-reference against structural data like financial or travel records. NETRA is a massive software network used to intercept and analyze internet traffic (emails, social media, blogs) for keywords like "bomb," "attack," or "kill." The intelligence gathered by NETRA regarding suspicious digital patterns or "keyword hits" can be fed into NATGRID. This allows an investigator to see if a person flagged by NETRA also has suspicious travel (from airline databases) or financial records (from bank databases) linked within NATGRID. Department of Telecommunications (DoT's Central Monitoring System (CMS) for lawfully intercepting national and international telecomm data: CMS is the centralized system for lawful interception of all telecommunications (phone calls, SMS, and data) in India, managed by the Department of Telecommunications (DoT). While CMS focuses on the content and metadata of real-time communication, NATGRID focuses on historical/structural data (tax, travel, identity). They represent two halves of a 360-degree surveillance profile: CMS listens to what a suspect says, while NATGRID tracks where they go and what they own. The CMS allows for the lawful interception of telecommunications metadata and content in real-time. In the broader surveillance architecture, CMS provides the "active" communication profile while NATGRID provides the "static" historical profile. Telecom Enforcement Resource and Monitoring (TERM) - Telecomm Regulatory & Verification Node for telecomm KYC: TERM cells verify subscriber identity (KYC) and maintain the integrity of telecom databases. NATGRID relies on these audited records to ensure the accuracy of telephone-to-identity mapping. TERM

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  CryptoParty

  CryptoParty (Crypto-Party) is a grassroots global endeavour to introduce the basics of practical cryptography such as the Tor anonymity network, I2P, Freenet, key signing parties, disk encryption and virtual private networks to the general public. The project primarily consists of a series of free public workshops. == History == As a successor to the Cypherpunks of the 1990s, CryptoParty was conceived in late August 2012 by the Australian journalist Asher Wolf in a Twitter post following the passing of the Cybercrime Legislation Amendment Bill 2011 and the proposal of a two-year data retention law in that country, the Cybercrime Legislation Amendment Bill 2011. The DIY, self-organizing movement immediately went viral, with a dozen autonomous CryptoParties being organized within hours in cities throughout Australia, the US, the UK, and Germany. Many more parties were soon organized or held in Chile, The Netherlands, Hawaii, Asia, etc. Tor usage in Australia itself spiked, and CryptoParty London with 130 attendees—some of whom were veterans of the Occupy London movement—had to be moved from London Hackspace to the Google campus in east London's Tech City. As of mid-October 2012 some 30 CryptoParties have been held globally, some on a continuing basis, and CryptoParties were held on the same day in Reykjavik, Brussels, and Manila. The first draft of the 442-page CryptoParty Handbook (the hard copy of which is available at cost) was pulled together in three days using the book sprint approach, and was released 2012-10-04 under a CC BY-SA license. === Edward Snowden involvement === In May 2014, Wired reported that Edward Snowden, while employed by Dell as an NSA contractor, organized a local CryptoParty at a small hackerspace in Honolulu, Hawaii on December 11, six months before becoming well known for leaking tens of thousands of secret U.S. government documents. During the CryptoParty, Snowden taught 20 Hawaii residents how to encrypt their hard drives and use the Internet anonymously. The event was filmed by Snowden's then-girlfriend, but the video has never been released online. In a follow-up post to the CryptoParty wiki, Snowden pronounced the event a "huge success." == Media response == In 2013, CryptoParty received messages of support from the Electronic Frontier Foundation and (purportedly) AnonyOps, as well as the NSA whistleblower Thomas Drake, WikiLeaks central editor Heather Marsh, and Wired reporter Quinn Norton. Eric Hughes, the author of A Cypherpunk's Manifesto nearly two decades before, delivered the keynote address, Putting the Personal Back in Personal Computers, at the Amsterdam CryptoParty on 2012-09-27. Marcin de Kaminski, founding member of Piratbyrån which in turn founded The Pirate Bay, regarded CryptoParty as the most important civic project in cryptography in 2012, and Cory Doctorow has characterized a CryptoParty as being "like a Tupperware party for learning crypto." Der Spiegel in December 2014 mentioned "crypto parties" in the wake of the Edward Snowden leaks in an article about the NSA.

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  Social media as a news source

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

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  Artificial reproduction

  Artificial reproduction is the re-creation of life brought about by means other than natural ones. It is new life built by human plans and projects. Examples include artificial selection, artificial insemination, in vitro fertilization, artificial womb, artificial cloning, and kinematic replication. Artificial reproduction is one aspect of artificial life. Artificial reproduction can be categorized into one of two classes according to its capacity to be self-sufficient: non-assisted reproductive technology and assisted reproductive technology. Cutting plants' stems and placing them in compost is a form of assisted artificial reproduction, xenobots are an example of a more autonomous type of reproduction, while the artificial womb presented in the movie the Matrix illustrates a non assisted hypothetical technology. The idea of artificial reproduction has led to various technologies. == Theology == Humans have aspired to create life since immemorial times. Most theologies and religions have conceived this possibility as exclusive of deities. Christian religions consider the possibility of artificial reproduction, in most cases, as heretical and sinful. == Philosophy == Although ancient Greek philosophy raised the concept that man could imitate the creative capacity of nature, classic Greeks thought that if possible, human beings would reproduce things as nature does, and vice versa, nature would do the things that man does in the same way. Aristotle, for example, wrote that if nature made tables, it would make them just as men do. In other words, Aristotle said that if nature were to create a table, such table will look like a human-made table. Correspondingly, Descartes envisioned the human body, and nature, as a machine. Cartesian philosophy does not stop seeing a perfect mirror between nature and the artificial. However, Kant revolutionized this old idea by criticizing such naturalism. Kant pedagogically wrote: "Reason, in order to be taught by nature, must approach nature with its principles in one hand, according to which the agreement among appearances can count as laws, and, in the other hand, the experiment thought out in accord with these principles—in order to be instructed by nature not like a pupil, who has recited to him whatever the teacher wants to say, but like an appointed judge who compels witnesses to answer the questions he puts to them.". Humans are not instructed by nature but rather use nature as raw material to invent. Humans find alternatives to the natural restrictions imposed by natural laws thus, nature is not necessarily mirrored. In accordance with Kant (and contrary to what Aristotle thought) Karl Marx, Alfred Whitehead, Jaques Derrida and Juan David García Bacca noticed that nature is incapable of reproducing tables; or airplanes, or submarines, or computers. If nature tried to create airplanes, it would produce birds. If nature tried to create submarines, it would get fishes. If nature tried to create computers, brains would grow. And if nature tried to create man, modern man, monkeys will be evolved. According to Whitehead, if we look for something natural in artificial life, in the most elaborate cases, if anything, only atoms remain natural. Juan David Garcia Bacca summarized, “It will not come out from wood, it will not be born, a galley; from clay, a vessel; from linen, a dress; from iron, a lever,...From natural, artificial. In the artificial, the natural is reduced to a simple raw material, even though it is perfectly specified with natural specification. The artificial is the real, positive, and original negation of the natural: of species, of genus and of essence. Thus, its ontology is superior to natural ontology. And for this very reason Marx did not attach any importance to Darwin, whose evolutionism is confined to the natural order: to changes, at most, from variety to variety, from species to species... natural. For the same reason, nature has no dialectics, even though continuous evolution and selection can occur. The dialectic cannot emerge from the natural, for deeper reasons than, using today's terms, from a bird, an airplane cannot emerge; from fish, a submarine; from ears, a telephone; from eyes, a television; from a brain, a digital computer; from feet, a car; from hands, an engine; from Euclid, Descartes; from Aristotle, Newton; from Plato, Marx.” According to García Bacca, the major difference between natural causes and artificial causes is that nature does not have plans and projects, while humans design things following plans and projects. In contrast, other influential authors such as Michael Behe have depicted the concept and promoted the idea of intelligent design, a notion that has aroused several doubts and heated controversies, as it reframe natural causes in accordance with a natural plan. Previous ideas that have also provided a positive 'sense' to natural reproduction, are orthogenesis, syntropy, orgone and morphic resonance, among others. Although, these ideas have been historically marginalized and often called pseudoscience, recently Bio-semioticians are reconsidering some of them under symbolic approaches. Current metaphysics of science actually recognizes that the artificial ways of reproduction are diverse from nature, i.e., unnatural, anti-natural or supernatural. Because Biosemiotics does not focus on the function of life but on its meaning, it has a better understanding of the artificial than classic biology. == Science == Biology, being the study of cellular life, addresses reproduction in terms of growth and cellular division (i.e., binary fission, mitosis and meiosis); however, the science of artificial reproduction is not restricted by the mirroring of these natural processes.The science of artificial reproduction is actually transcending the natural forms, and natural rules, of reproduction. For example, xenobots have redefined the classical conception of reproduction. Although xenobots are made of eukariotic cells they do not reproduce by mitosis, but rather by kinematic replication. Such constructive replication does not involve growing but rather building. == Assisted reproductive technologies == Assisted reproductive technology (ART)'s purpose is to assist the development of a human embryo, commonly because of medical concerns due to fertility limitations. == Non-assisted reproductive technologies == Non-assisted reproductive technologies (NART) could have medical motivations but are mostly driven by a wider heterotopic ambition. Although, NARTs are initially designed by humans, they are programed to become independent of humans to a relative or absolute extent. James Lovelock proposed that such novelties could overcome humans. === Artificial cloning === Cloning is the cellular reproductive processes where two or more genetically identical organisms are created, either by natural or artificial means. Artificial cloning normally involves editing the genetic code, somatic cell nuclear transfer and 3D bioprinting. === Non-assisted artificial womb === A non-assisted artificial womb or artificial uterus is a device that allow for ectogenesis or extracorporeal pregnancy by growing an embryonic form outside the body of an organism (that would normally carry the embryo to term) without any human assistance. The aspect of non-assistance is the key distinction between the current artificial womb technology (AWT) in modern medical research, which still relies on human assistance. With this non-assisted hypothetical technology, a zygote or stem cells are used to create an embryo that is then incubated and monitored by artificial intelligence (AI) within a chamber composed of biocompatible material. The AI maintains the necessary conditions for the embryo to develop and thrive, proceeding to mimic organic labor and childbirth in order to best help the embryo adjust to the outside world. Ectogenesis—gestation, depicted in the science fiction movie The Matrix, is a fast approaching reality. This type of innovation presupposes that vertebrate wombs are not the only way for bearing humans or other similar forms of life. === Kinematic replication === Self-replication without binary fission, meiosis, mitosis (or any other form of cellular reproduction that involves division and growing) can be achieved. Xenobots are an example of kinematic replication. They are biobots, named after the African clawed frog (Xenopus laevis). Xenobots are cellular life forms designed by using artificial intelligence to build more of themselves by combining frog cells in a liquid medium. The term kinematic replication is usually reserved for biomolecules (e.g. DNA, RNA, prions, etc.) and artificially designed cellular forms (e.g. xenobots). === Machine constructive replication === Machine constructive replication mimics human traditional manufacturing but is entirely self-automated. Such constructive replication is a more general form of kinematic replication, which does not necessarily

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  Social media use in the fashion industry

  Social media in the fashion industry refers to the use of social media platforms by fashion designers and users to promote and participate in trends. Over the past several decades, the development of social media has increased along with its usage by consumers. The COVID-19 pandemic was a sharp turn of reliance on the virtual sphere for the industry and consumers alike. Social media has created new channels of advertising for fashion houses to reach their target markets. Since its surge in 2009, luxury fashion brands have used social media to build interactions between the brand and its customers to increase awareness and engagement. The emergence of influencers on social media has created a new way of advertising and maintaining customer relationships in the fashion industry. Numerous social media platforms are used to promote fashion trends, with Instagram and TikTok being the most popular among Generation Y and Z. The overall impact of social media in the fashion industry included the creation of online communities, direct communication between industry leaders and consumers, and criticized ideals that are promoted by the industry through social media. == Background == In 2003, at the beginning of social media development, MySpace was founded as a “social networking service.” It allowed people to create a profile, connect with other people, and post videos, pictures, and songs. As MySpace grew in popularity, it attracted interest from companies wishing to promote their brands on the social platform. MySpace is most well known for exposing musicians and artists who made it big in the industry, and companies wanted to capitalize on their popularity by making brand deals. One of MySpace's deals was with Chevrolet, putting on a ‘secret show’. They had a ‘secret’ list of 10 top artists on MySpace, and many artists posted about the show on their accounts. Another brand deal was with Gucci promoting their “Gucci Synch Watch”, which was very successful as Gucci tapped into the youthful audience on MySpace and advertised a sleek, simple, trendy unisex watch. In 2005, YouTube was released and remains one of the most popular social media platforms today. YouTube allows users to upload videos and is free to anyone with access to the internet. It grew in popularity offering a range of videos: vlogs, cooking, health and diet videos, step-by-step tutorials, tutoring help, and more. Much like MySpace, users create accounts and can build a following, often referring to themselves as ‘YouTubers.’ When YouTube grew in popularity, it piqued the interest of brands wanting to partner with YouTube and individual YouTubers. Some brand deals were made by having ads at the beginning of each video, and the YouTuber would make a profit from each view they receive. Some deals are made by individual YouTubers thanking the brand in videos and promoting the brand's products. More recently, YouTube has delved into fashion. While there were always YouTube channels for Vogue and other fashion companies, popular YouTubers have been invited to different fashion shows and have filmed experiences there. Brands are able to target individual YouTubers based on their followers and the target audiences. In 2010, Instagram was launched, which enlarged the scope of fashion advertising. Instagram allows people to post pictures and short videos with the ability to tag different accounts. For brand deals, companies can simply be tagged in a picture instead of creating ads or lines for a user to say. In each picture, users can tag the brands of clothing they were wearing, making it very easy to promote brands. Additionally, Instagram could display ads on users' feed based on other posts the users liked, which used by fashion companies to target their potential customers. Users also use Instagram to promote fashion when they get invited to fashion events. For example, they can take a picture at the event and post it to their Instagram and put their location at the venue and tag the company. During the beginning of the COVID-19 pandemic, companies relied more on social media to keep their public virtually engaged. Fashion companies had virtual fashion shows, creating videos and content about their designs. As social media expands and new platforms come into existence, new ways of advertising are projected to be created. == Uses == === Advertising === Social media is a popular use of advertisement in the fashion industry. Information sharing has expanded due to the growth of social media platforms, which impacts social consumer involvement with fashion brands. Fashion companies use social media platforms to reach customers on emotional levels and stoke engagement with brand images and messages. Researchers in the United Kingdom have demonstrated that engaging with customers with social media messages that express social passion, social tendency, and personal warmth can boost social engagement with fashion brands. In social spheres, fashion is a method for individuals to represent their distinction through clothing. Some people who desire to socially influence others through their fashion and style now have the possibility thanks to social media in the fashion sector. Customers who want to purchase fashion brands frequently follow fashion authorities on social media and heed their recommendations for purchasing fashion products. === Influencers === Companies leveraged celebrities' fame and social standing to advertise their brands, as Tommy Hilfiger did when incorporating social media into their marketing strategy, making Gigi Hadid, who has 15.5 million Instagram followers as of 2016, a brand ambassador. Though recent developments in social media platforms have led to an increase in the awareness of influencers. Influencer marketing has emerged as a fast expanding marketing strategy in various industries as a result of the unheard-of increase in the number of social media influencers' followers. Recently, influencer marketing has received significant attention in the fashion industry. Research shows that influencer marketing may provide a rate of influence that is 11x times greater than that of other conventional advertising channels. Fashion consumers, specifically those in generations Y and Z, may be more influenced by influencers in the context of the fashion industries as they often view them as friends and personal assistants. Fashion influencer marketing on social media platforms have led fashion consumption on social sopping services. One of these social fashion services is LTK (LIKEtoKNOW.it before 2021) where everyday consumers can find and purchase clothing worn by social media fashion influencers (also known as SMFIs). Launched in 2014, LTK has gained a massive following on Instagram (over 3 million) and has 1.3 million registered users on their mobile application. Utilizing SMFIs has led to massive sales within the fashion industry, 80% of visitors of Nordstrom's mobile platform are referred by influencers. Social media fashion influencers try new fashion products, adopt fashion trends and have power in what their audience purchases. Social media fashion influencers gain a following though promoting fashion products, and posting about their lavish lifestyles attained through their higher socioeconomic status. The attractive lifestyles of the influencers influence their followers to mimic their luxurious lifestyle and are allowed to consume the same products through social shopping services. In addition to brands themselves having direct access to social media users, many content creators have great influence over consumers. "Influencers" across all social media platforms have great power when it comes to where people shop and what they purchase. Influencer marketing has become one of the most effective marketing strategies for many fashion brands. These brand deals and creator partnerships are targeted towards Millennial and Gen Z consumers, specifically on Instagram and TikTok, and 74% of consumers have made a purchase simply because an influencer they follow had recommended it. === Trends === The connection between social media and fashion has become common. Influencer marketing has emerged as a necessity and crucial component of advertising. 85% of American businesses are presently using influencer marketing as part of their marketing plan. Wearing fashion brands is a method to show oneself at social gatherings. Through their clothing, people try to demonstrate how distinct they are. Some people who really desire to socially influence others through their fashion and style now have the possibility thanks to social media in the fashion sector. Customers who want to purchase fashion brands frequently follow fashion authorities on social media and heed their recommendations for purchasing fashion products. In January 2021, the Italian fashion house Bottega Veneta deleted all its social media accounts "to lean much more on its ambassadors and fans" to spread the com

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  Tokenization (data security)

  Tokenization, when applied to data security, is the process of substituting a sensitive data element with a non-sensitive equivalent, referred to as a token, that has no intrinsic or exploitable meaning or value. The token is a reference (i.e. identifier) that maps back to the sensitive data through a tokenization system. The mapping from original data to a token uses methods that render tokens infeasible to reverse in the absence of the tokenization system, for example using tokens created from random numbers. A one-way cryptographic function is used to convert the original data into tokens, making it difficult to recreate the original data without obtaining entry to the tokenization system's resources. To deliver such services, the system maintains a vault database of tokens that are connected to the corresponding sensitive data. Protecting the system vault is vital to the system, and improved processes must be put in place to offer database integrity and physical security. The tokenization system must be secured and validated using security best practices applicable to sensitive data protection, secure storage, audit, authentication and authorization. The tokenization system provides data processing applications with the authority and interfaces to request tokens, or detokenize back to sensitive data. The security and risk reduction benefits of tokenization require that the tokenization system is logically isolated and segmented from data processing systems and applications that previously processed or stored sensitive data replaced by tokens. Only the tokenization system can tokenize data to create tokens, or detokenize back to redeem sensitive data under strict security controls. The token generation method must be proven to have the property that there is no feasible means through direct attack, cryptanalysis, side channel analysis, token mapping table exposure or brute force techniques to reverse tokens back to live data. Replacing live data with tokens in systems is intended to minimize exposure of sensitive data to those applications, stores, people and processes, reducing risk of compromise or accidental exposure and unauthorized access to sensitive data. Applications can operate using tokens instead of live data, with the exception of a small number of trusted applications explicitly permitted to detokenize when strictly necessary for an approved business purpose. Tokenization systems may be operated in-house within a secure isolated segment of the data center, or as a service from a secure service provider. Tokenization may be used to safeguard sensitive data involving, for example, bank accounts, financial statements, medical records, criminal records, driver's licenses, loan applications, stock trades, voter registrations, and other types of personally identifiable information (PII). Tokenization is often used in credit card processing. The PCI Council defines tokenization as "a process by which the primary account number (PAN) is replaced with a surrogate value called a token. A PAN may be linked to a reference number through the tokenization process. In this case, the merchant simply has to retain the token and a reliable third party controls the relationship and holds the PAN. The token may be created independently of the PAN, or the PAN can be used as part of the data input to the tokenization technique. The communication between the merchant and the third-party supplier must be secure to prevent an attacker from intercepting to gain the PAN and the token. De-tokenization is the reverse process of redeeming a token for its associated PAN value. The security of an individual token relies predominantly on the infeasibility of determining the original PAN knowing only the surrogate value". The choice of tokenization as an alternative to other techniques such as encryption will depend on varying regulatory requirements, interpretation, and acceptance by respective auditing or assessment entities. This is in addition to any technical, architectural or operational constraint that tokenization imposes in practical use. == Concepts and origins == The concept of tokenization, as adopted by the industry today, has existed since the first currency systems emerged centuries ago as a means to reduce risk in handling high value financial instruments by replacing them with surrogate equivalents. In the physical world, coin tokens have a long history of use replacing the financial instrument of minted coins and banknotes. In more recent history, subway tokens and casino chips found adoption for their respective systems to replace physical currency and cash handling risks such as theft. Exonumia and scrip are terms synonymous with such tokens. In the digital world, similar substitution techniques have been used since the 1970s as a means to isolate real data elements from exposure to other data systems. In databases for example, surrogate key values have been used since 1976 to isolate data associated with the internal mechanisms of databases and their external equivalents for a variety of uses in data processing. More recently, these concepts have been extended to consider this isolation tactic to provide a security mechanism for the purposes of data protection. In the payment card industry, tokenization is one means of protecting sensitive cardholder data in order to comply with industry standards and government regulations. Tokenization was applied to payment card data by Shift4 Corporation and released to the public during an industry Security Summit in Las Vegas, Nevada in 2005. The technology is meant to prevent the theft of the credit card information in storage. Shift4 defines tokenization as: "The concept of using a non-decryptable piece of data to represent, by reference, sensitive or secret data. In payment card industry (PCI) context, tokens are used to reference cardholder data that is managed in a tokenization system, application or off-site secure facility." To protect data over its full lifecycle, tokenization is often combined with end-to-end encryption to secure data in transit to the tokenization system or service, with a token replacing the original data on return. For example, to avoid the risks of malware stealing data from low-trust systems such as point of sale (POS) systems, as in the Target breach of 2013, cardholder data encryption must take place prior to card data entering the POS and not after. Encryption takes place within the confines of a security hardened and validated card reading device and data remains encrypted until received by the processing host, an approach pioneered by Heartland Payment Systems as a means to secure payment data from advanced threats, now widely adopted by industry payment processing companies and technology companies. The PCI Council has also specified end-to-end encryption (certified point-to-point encryption—P2PE) for various service implementations in various PCI Council Point-to-point Encryption documents. == The tokenization process == The process of tokenization consists of the following steps: The application sends the tokenization data and authentication information to the tokenization system. It is stopped if authentication fails and the data is delivered to an event management system. As a result, administrators can discover problems and effectively manage the system. The system moves on to the next phase if authentication is successful. Using one-way cryptographic or random generation techniques, a token is generated and kept in a highly secure data vault. The new token is provided to the application for further use, replacing the sensitive data for processing and storage. Tokenization systems share several components according to established standards. Token generation is the process of producing a token using any means, such as one-way nonreversible cryptographic functions (e.g., a hash function with a strong, secret salt) or assignment via a randomly generated number. Random number generator (RNG) techniques are often the best choice for generating token values. Token mapping – this is the process of assigning the created token value to its original value. To enable permitted look-ups of the original value using the token as the index, a secure cross-reference database must be constructed. Token data store – this is a central repository for the token mapping process that holds the original sensitive values and their related token values. Sensitive data and token values must be securely kept in an encrypted format. Management of cryptographic keys. Strong key management procedures are required for sensitive data encryption on token data stores. == Difference from encryption == Tokenization and "classic" encryption effectively protect data if implemented properly, and a computer security system may use both. While similar in certain regards, tokenization and classic encryption differ in a few key aspects. Both are cryptographic data security methods and the

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  Coreu

  COREU (French: Correspondance Européenne – Telex network of European correspondents, also EUKOR-Netzwerk in Austria) is a communication network of the European Union for the communication of the Council of the European Union, the European correspondents of the foreign ministries of the EU member states, permanent representatives of member states in Brussels, the European Commission, and the General Secretariat of the Council of the European Union. The European Parliament is not among the participants. COREU is the European equivalent of the American Secret Internet Protocol Router Network (SIPRNet, also known as Intelink-S). COREU's official aim is fast communication in case of crisis. The network enables a closer cooperation in matters regarding foreign affairs. In actuality the system's function exceeds that of mere communication, it also enables decision-making. COREU's first goal is to enable the exchange of information before and after decisions. Relaying upfront negotiations in preparation of meetings is the second goal. In addition, the system also allows the editing of documents and the decision-making, especially if there is little time. While the first two goals are preparatory measures for a shared foreign policy, the third is a methodical variant marked by practise that is defining for the image of the Common Foreign and Security Policy. == Members == (The following information dates from 2013): There is one representative in each of the capital cities in the EU.(since 1973) In Germany for example, this is the European correspondent (EU-KOR) from the Foreign Office. In Austria it is the European correspondent from the Referat II.1.a in the Federal Ministry for Europe, Integration and Foreign Affairs They are the correspondents (since 1982) for the European Commission They comprise the secretariat for the European Council They also make up the European External Action Service (EEAS) (responsible for foreign policy issues, since 1987) == Data volume and technical details == COREU functions as a spoke-hub distribution paradigm system with the hub in Brussels. The network is operated by the European Union Intelligence and Situation Centre (formerly Joint Situation Center, JSC). The technical infrastructure is located in a building of the European Council. COREU may be described as an advanced telex system with encrypted messages via dedicated terminals. Once a message has reached the destination, it is then redistributed via the local media. In contrast, messages of governments are transmitted via local media to the correspondents and from there delivered point-to-point to Brussels via COREU. In 2010, approximately 8500 communications had been distributed over this network. == History == A telex-based communication system under the name COREU was established in 1973. Originally, only the ministries of Foreign Affairs in the European capitals were connected to it. This telex system was replaced in 1997 by the mail system CORTESY (COREU Terminal Equipment System). The name was retained despite the technical innovation. COREU was reportedly compromised by hackers working for the People's Liberation Army Strategic Support Force, allowing for the theft of thousands of low-classified documents and diplomatic cables.

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  Reasoning model

  A reasoning model, also known as a reasoning language model (RLM) or large reasoning model (LRM), is a type of large language model (LLM) that has been specifically trained to solve complex tasks requiring multiple steps of logical reasoning. These models demonstrate superior performance on logic, mathematics, and programming tasks compared to standard LLMs. They possess the ability to revisit and revise earlier reasoning steps and utilize additional computation during inference as a method to scale performance, complementing traditional scaling approaches based on training data size, model parameters, and training compute. == Overview == Unlike traditional language models that generate responses immediately, reasoning models allocate additional compute, or thinking, time before producing an answer to solve multi-step problems. OpenAI introduced this terminology in September 2024 when it released the o1 series, describing the models as designed to "spend more time thinking" before responding. The company framed o1 as a reset in model naming that targets complex tasks in science, coding, and mathematics, and it contrasted o1's performance with GPT-4o on benchmarks such as AIME and Codeforces. Independent reporting the same week summarized the launch and highlighted OpenAI's claim that o1 automates chain-of-thought style reasoning to achieve large gains on difficult exams. In operation, reasoning models generate internal chains of intermediate steps, then select and refine a final answer. OpenAI reported that o1's accuracy improves as the model is given more reinforcement learning during training and more test-time compute at inference. The company initially chose to hide raw chains and instead return a model-written summary, stating that it "decided not to show" the underlying thoughts so researchers could monitor them without exposing unaligned content to end users. Commercial deployments document separate "reasoning tokens" that meter hidden thinking and a control for "reasoning effort" that tunes how much compute the model uses. These features make the models slower than ordinary chat systems while enabling stronger performance on difficult problems. == History == The research trajectory toward reasoning models combined advances in supervision, prompting, and search-style inference. Early alignment work on reinforcement learning from human feedback showed that models can be fine-tuned to follow instructions with "human feedback" and preference-based rewards. In 2022, Google Research scientists Jason Wei and Denny Zhou showed that chain-of-thought prompting "significantly improves the ability" of large models on complex reasoning tasks. Input → Step 1 → Step 2 → ⋯ → Step n ⏟ Reasoning chain → Answer {\displaystyle {\text{Input}}\rightarrow \underbrace {{\text{Step}}_{1}\rightarrow {\text{Step}}_{2}\rightarrow \cdots \rightarrow {\text{Step}}_{n}} _{\text{Reasoning chain}}\rightarrow {\text{Answer}}} A companion result demonstrated that the simple instruction "Let's think step by step" can elicit zero-shot reasoning. Follow-up work introduced self-consistency decoding, which "boosts the performance" of chain-of-thought by sampling diverse solution paths and choosing the consensus, and tool-augmented methods such as ReAct, a portmanteau of Reason and Act, that prompt models to "generate both reasoning traces" and actions. Research then generalized chain-of-thought into search over multiple candidate plans. The Tree-of-Thoughts framework from Princeton computer scientist Shunyu Yao proposes that models "perform deliberate decision making" by exploring and backtracking over a tree of intermediate thoughts. OpenAI's reported breakthrough focused on supervising reasoning processes rather than only outcomes, with Lightman et al.'s "Let's Verify Step by Step" reporting that rewarding each correct step "significantly outperforms outcome supervision" on challenging math problems and improves interpretability by aligning the chain-of-thought with human judgment. OpenAI's o1 announcement ties these strands together with a large-scale reinforcement learning algorithm that trains the model to refine its own chain of thought, and it reports that accuracy rises with more training compute and more time spent thinking at inference. Together, these developments define the core of reasoning models. They use supervision signals that evaluate the quality of intermediate steps, they exploit inference-time exploration such as consensus or tree search, and they expose controls for how much internal thinking compute to allocate. OpenAI's o1 family made this approach available at scale in September 2024 and popularized the label "reasoning model" for LLMs that deliberately think before they answer. The development of reasoning models illustrates Richard S. Sutton's "bitter lesson" that scaling compute typically outperforms methods based on human-designed insights. This principle was demonstrated by researchers at the Generative AI Research Lab (GAIR), who initially attempted to replicate o1's capabilities using sophisticated methods including tree search and reinforcement learning in late 2024. Their findings, published in the "o1 Replication Journey" series, revealed that knowledge distillation, a comparatively straightforward technique that trains a smaller model to mimic o1's outputs, produced unexpectedly strong performance. This outcome illustrated how direct scaling approaches can, at times, outperform more complex engineering solutions. === Drawbacks === Reasoning models require significantly more computational resources during inference compared to non-reasoning models. Research on the American Invitational Mathematics Examination (AIME) benchmark found that reasoning models were 10 to 74 times more expensive to operate than their non-reasoning counterparts. The extended inference time is attributed to the detailed, step-by-step reasoning outputs that these models generate, which are typically much longer than responses from standard large language models that provide direct answers without showing their reasoning process. One researcher in early 2025 argued that these models may face potential additional denial-of-service concerns with "overthinking attacks." === Releases === ==== 2024 ==== In September 2024, OpenAI released o1-preview, a large language model with enhanced reasoning capabilities. The full version, o1, was released in December 2024. OpenAI initially shared preliminary results on its successor model, o3, in December 2024, with the full o3 model becoming available in 2025. Alibaba released reasoning versions of its Qwen large language models in November 2024. In December 2024, the company introduced QvQ-72B-Preview, an experimental visual reasoning model. In December 2024, Google introduced Deep Research in Gemini, a feature designed to conduct multi-step research tasks. On December 16, 2024, researchers demonstrated that by scaling test-time compute, a relatively small Llama 3B model could outperform a much larger Llama 70B model on challenging reasoning tasks. This experiment suggested that improved inference strategies can unlock reasoning capabilities even in smaller models. ==== 2025 ==== In January 2025, DeepSeek released R1, a reasoning model that achieved performance comparable to OpenAI's o1 at significantly lower computational cost. The release demonstrated the effectiveness of Group Relative Policy Optimization (GRPO), a reinforcement learning technique used to train the model. On January 25, 2025, DeepSeek enhanced R1 with web search capabilities, allowing the model to retrieve information from the internet while performing reasoning tasks. Research during this period further validated the effectiveness of knowledge distillation for creating reasoning models. The s1-32B model achieved strong performance through budget forcing and scaling methods, reinforcing findings that simpler training approaches can be highly effective for reasoning capabilities. On February 2, 2025, OpenAI released Deep Research, a feature powered by their o3 model that enables users to conduct comprehensive research tasks. The system generates detailed reports by automatically gathering and synthesizing information from multiple web sources. OpenAI called GPT-4.5 its "last non-chain-of-thought model", and implemented with GPT-5 a router model that selects a model based on the difficulty of the task. ==== 2026 ==== In January 2026, Moonshot AI released Kimi K2.5, an open-source 1 trillion parameter MoE model with 32 billion active parameters. It uses an “Agent Swarm” system that dynamically decomposes tasks into sub-agents for reasoning and execution, enabling more scalable multi-step problem solving than a single sequential reasoning chain. == Training == Reasoning models follow the familiar large-scale pretraining used for frontier language models, then diverge in the post-training and optimization. OpenAI reports that o1 is trained with a large-

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  Conditional disclosure of secrets

  Conditional disclosure of secrets (CDS) is a primitive, studied in information-theoretic cryptography, that allows distributed, non-communicating parties to coordinate the release of information to a third party. CDS was initially introduced for use in the context of private information retrieval, and has been related to communication complexity and non-local quantum computation. == Definition of conditional disclosure of secrets == The conditional disclosure of secrets setting involves three players; Alice, Bob and the referee. Alice receives an input x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} and a secret z ∈ { 0 , 1 } {\displaystyle z\in \{0,1\}} , and Bob receives a string y ∈ { 0 , 1 } n {\displaystyle y\in \{0,1\}^{n}} . A choice of Boolean function f : { 0 , 1 } 2 n → { 0 , 1 } {\displaystyle f:\{0,1\}^{2n}\rightarrow \{0,1\}} is fixed in advance and known to all players. Alice and Bob cannot communicate with one another, but share a string of random bits which we label r {\displaystyle r} . Alice and Bob compute messages m A = m A ( x , z , r ) {\displaystyle m_{A}=m_{A}(x,z,r)} and m B = m B ( y , r ) {\displaystyle m_{B}=m_{B}(y,r)} , which they send to the referee. The referee knows ( x , y ) {\displaystyle (x,y)} . A CDS protocol consists of the encoding maps applied by Alice and Bob. A protocol is said to be ϵ {\displaystyle \epsilon } -correct if, for all ( x , y ) ∈ f − 1 ( 1 ) {\displaystyle (x,y)\in f^{-1}(1)} , the referee can determine z {\displaystyle z} with probability 1 − ϵ {\displaystyle 1-\epsilon } . A protocol is said to be δ {\displaystyle \delta } -secure if, for all ( x , y ) ∈ f − 1 ( 0 ) {\displaystyle (x,y)\in f^{-1}(0)} the distribution of the messages is δ {\displaystyle \delta } close to a simulator distribution (in total variation distance), where the simulator distribution is independent of z {\displaystyle z} . The communication complexity of a CDS protocol P is the total number of bits of message sent by Alice and Bob. The CDS communication cost of a function, C D S ϵ , δ ( f ) {\displaystyle CDS_{\epsilon ,\delta }(f)} is the minimal communication cost of an ϵ {\displaystyle \epsilon } -correct, δ {\displaystyle \delta } secure protocol that implements f {\displaystyle f} . The randomness complexity and randomness cost of implementing a function in the CDS model are defined similarly, but consider the number of bits of shared random bits held by Alice and Bob. == Basic properties of the primitive == === Amplification === Supposing we have an ϵ {\displaystyle \epsilon } -correct and δ {\displaystyle \delta } -secure CDS protocol, it is known that we can find a new protocol which reduces the correctness and privacy errors at the expense of an increased communication and randomness cost. More specifically, the following theorem has been proven Theorem (Amplification). A CDS protocol for f which supports a single-bit secret with privacy and correctness error of 1/3 can be transformed into a new CDS protocol with privacy and correctness error of 2 − Ω ( k ) {\displaystyle 2^{-\Omega (k)}} and communication/randomness complexity which are larger than those of the original protocol by a multiplicative factor of O(k). In fact, somewhat more than the above theorem is true in that the size of the secret can also be made to be of length k {\displaystyle k} , while simultaneously reducing the correctness and privacy errors as above. The proof involves first encoding the secret z {\displaystyle z} into a secret sharing scheme, and then running the original CDS protocol on each share of the resulting scheme. === Closure === If a CDS protocol for a function f {\displaystyle f} is known, then certain simple modifications of f {\displaystyle f} have CDS protocols with similar efficiency. The simplest case is to consider a CDS protocol for function f {\displaystyle f} and ask for a similarly efficient protocol for the negation of f {\displaystyle f} , labelled ¬ f {\displaystyle \neg f} . This is addressed by the following theorem Theorem (CDS is closed under complement). Suppose that f has a CDS protocol with randomness cost of ρ {\displaystyle \rho } bits, communication complexity of t {\displaystyle t} bits, and privacy and correctness errors δ = ϵ = 2 − k {\displaystyle \delta =\epsilon =2^{-k}} . Then ¬ f {\displaystyle \neg f} has a CDS scheme with similar privacy and correctness errors, and randomness and communication complexity of O ( k 3 ρ 2 t + k 3 ρ 3 ) {\displaystyle O(k^{3}\rho ^{2}t+k^{3}\rho ^{3})} . The cost of a CDS protocol is also closed under formula's, in the following sense. Consider two functions f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . Then, the communication and randomness costs of f 1 ∧ f 2 {\displaystyle f_{1}\wedge f_{2}} as well as f 1 ∨ f 2 {\displaystyle f_{1}\vee f_{2}} are not much larger than the sum of the costs for f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . See Applebaum et al. for a precise statement. == Upper and lower bounds on communication cost == Given a function f {\displaystyle f} we would like to understand the communication and randomness costs to implement f {\displaystyle f} in the CDS setting. Towards understanding this, protocols for implementing CDS have been developed (which give an upper bound on the cost) and lower bound strategies have been developed. For most functions, there is a large gap between the known upper and lower bound, so understanding the cost of CDS remains largely an open problem. This section presents some of what is known so far about the cost of CDS. === Secret sharing based upper bound === A subject with a close relationship to CDS is secret sharing. Secret sharing constructions provide an upper bound on the cost of CDS protocols. A secret sharing scheme encodes a secret, s {\displaystyle s} into a set of shares S 1 , . . . , S n {\displaystyle S_{1},...,S_{n}} . Associated to any secret sharing scheme is an access structure, which consists of a set of authorized sets A = A 1 , . . . , A k {\displaystyle {\mathcal {A}}={A_{1},...,A_{k}}} with A i ⊆ { S 1 , . . . , S n } {\displaystyle A_{i}\subseteq \{S_{1},...,S_{n}\}} . The authorized sets are those subsets of the A i {\displaystyle A_{i}} from which it is possible to recover the secret recorded into the scheme. A succinct way to describe an access structure is in terms of a function f A : { 0 , 1 } n → { 0 , 1 } {\displaystyle f_{\mathcal {A}}:\{0,1\}^{n}\rightarrow \{0,1\}} . Each subset of the shares K [ x ] ⊂ { S 1 , . . . , S n } {\displaystyle K[x]\subset \{S_{1},...,S_{n}\}} is labelled by a string x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} such that x i = 1 {\displaystyle x_{i}=1} if and only if S i ∈ K {\displaystyle S_{i}\in K} . Then we define f A {\displaystyle f_{\mathcal {A}}} to be such that f A ( x ) = 1 {\displaystyle f_{\mathcal {A}}(x)=1} if and only if K [ x ] ∈ A {\displaystyle K[x]\in {\mathcal {A}}} . In words, the function f A {\displaystyle f_{\mathcal {A}}} is 1 when given an authorized subset as input, and 0 otherwise. A basic result in the theory of secret sharing is that an access structure A {\displaystyle {\mathcal {A}}} can be realized in a secret sharing scheme if and only if f A {\displaystyle f_{\mathcal {A}}} is monotone. The size of a secret sharing scheme is defined as the total number of bits in the shares S i {\displaystyle S_{i}} . For monotone functions, there is an upper bound on the communication cost in CDS for any monotone function f {\displaystyle f} in terms of the size of any secret sharing scheme with access structure given by f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S h a r i n g S i z e ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SharingSize(f)} For some concrete classes of secret sharing schemes, this relationship can be extended to general (non-monotone) Boolean functions. This leads to an upper bound on CDS cost in terms of the size of any span program that computes f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S P k ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SP_{k}(f)} The class of problems with efficient (polynomial size) span program is the complexity class M o d k L {\displaystyle Mod_{k}L} , so problems in this class have efficient CDS protocols. === Sub-exponential upper bounds for all functions === Using a matching vector family based construction, it has been proven that ∀ f , C D S ϵ = 0 , δ = 0 ( f ) ≤ 2 O ( n log ⁡ n ) {\displaystyle \forall f,\,\,\,\,\,\,CDS_{\epsilon =0,\delta =0}(f)\leq 2^{O({\sqrt {n\log n}})}} . The technique for this proof is similar to one used to prove upper bounds on private information retrieval. This upper bound on CDS also leads to sub-exponential upper bounds on the size of a large class of secret sharing schemes. === Lower bounds from communication complexity === In a CDS protocol, the referee is given the inputs ( x , y ) {\displaystyle (x,y)} . This means it is not clear if the messages sent by Alice a

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  Caste census

  Caste census is a proposed census to be conducted in India by the Central Government of India. The proposed census was decided under the leadership of Prime Minister Narendra Modi by the cabinet committee of political affairs (CCPA) on 30 April 2025. It has been decided that a caste enumeration should be included with the forthcoming census. The exact time has not been declared yet. It is unclear that when the next census will be held. The decision of the cabinet was announced by the Central Railway Minister Ashwini Vaishnaw. It has been seen as a step that would help in drafting "equitable and targeted" policies by the present Central Government of India led by the Bhartiya Janta Party in India. The Central Home Minister Amit Shah has described the decision as a "historic decision". He has also described that the historic decision as “committed to social justice”. The leader of opposition Rahul Gandhi has welcomed the decision. He said "We have shown we can pressure govt" He has demanded a clear timeline for its completion. He has called it "The first step towards deep social reform". == Description == The caste census is a systematic recording of individuals’ caste identities during the nationwide census in the country. The Central minister Ashwini Vaishnaw expressed his view on the proposed census and said that it would "strengthen the social and economic structure of our society while the nation continues to progress”. The Caste census will happen for the first time in 100 years by the Central Government of India. It will be the part of the upcoming census in India. == History == According to Peabody, the first systematic caste-wise enumeration of households in the Indian subcontinent was conducted between 1658 and 1664 across seven districts of the then Marwar Kingdom, including Jodhpur city which was its capital. It was conducted by the then home minister Munhata Nainsi of the kingdom for the purpose of tax documentation. It was not to for classification of society or creation of social hierarchies but solving a tax related problem. During the period of the British rule in India, caste census was included in the decadal censuses to categorise the population by caste, religion and occupation. In 1871–72, the first detailed caste census was conducted by the government of British Raj in India. It was practiced between the period 1881 to 1931. The last caste census was conducted in the year 1931 in which 4,147 castes were recorded. The largest population in the whole of British India (including Pakistan and Bangladesh) was of Brahmins. The population of Brahmins was recorded more than 1.5 crores. After Brahmin community, the second place was of Jatav (Chamar)community. The population of Jatav was a little more than 1.23 crores. On the third place were Rajputs. The population of Rajputs was 81 lakhs. The Rajput caste was followed by the Kunbi caste of Maharashtra. The population of Kunbi caste was 64 lakhs and 34 thousands. The Kunbi caste was followed by Yadav (Ahir) caste. The population of Yadav (Ahir) community was 56 lakhs and 82 thousands. The Yadav (Ahir) caste was followed by Teli community. The population of Teli community was 42 lakhs and 58 thousands. The Teli community was followed by Gwala community. The population of the Gwala community was 40 lakhs. After the independence of India, the caste enumeration was stopped by the newly independent Government of India led by the prime minister Pandit Jawahar Lal Nehru in 1951. The caste enumeration was stopped to avoid reinforcing social divisions in the Indian society. But, there was an exception made for the enumeration of the Scheduled Castes (SCs) and Scheduled Tribes (STs) in the decadal censuses. Therefore, the enumeration of the Scheduled Castes and the Scheduled Tribes is being conducted in every census since 1951. In 1961, the Government of India permitted states for conducting their own surveys to compile OBC lists, but national caste census was not conducted.

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