AI For Students With Adhd

AI For Students With Adhd — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  And–or tree

  An and–or tree is a graphical representation of the reduction of problems (or goals) to conjunctions and disjunctions of subproblems (or subgoals). == Example == The and–or tree: represents the search space for solving the problem P, using the goal-reduction methods: P if Q and R P if S Q if T Q if U == Definitions == Given an initial problem P0 and set of problem solving methods of the form: P if P1 and … and Pn the associated and–or tree is a set of labelled nodes such that: The root of the tree is a node labelled by P0. For every node N labelled by a problem or sub-problem P and for every method of the form P if P1 and ... and Pn, there exists a set of children nodes N1, ..., Nn of the node N, such that each node Ni is labelled by Pi. The nodes are conjoined by an arc, to distinguish them from children of N that might be associated with other methods. A node N, labelled by a problem P, is a success node if there is a method of the form P if nothing (i.e., P is a "fact"). The node is a failure node if there is no method for solving P. If all of the children of a node N, conjoined by the same arc, are success nodes, then the node N is also a success node. Otherwise the node is a failure node. == Search strategies == An and–or tree specifies only the search space for solving a problem. Different search strategies for searching the space are possible. These include searching the tree depth-first, breadth-first, or best-first using some measure of desirability of solutions. The search strategy can be sequential, searching or generating one node at a time, or parallel, searching or generating several nodes in parallel. == Relationship with logic programming == The methods used for generating and–or trees are propositional logic programs (without variables). In the case of logic programs containing variables, the solutions of conjoint sub-problems must be compatible. Subject to this complication, sequential and parallel search strategies for and–or trees provide a computational model for executing logic programs. == Relationship with two-player games == And–or trees can also be used to represent the search spaces for two-person games. The root node of such a tree represents the problem of one of the players winning the game, starting from the initial state of the game. Given a node N, labelled by the problem P of the player winning the game from a particular state of play, there exists a single set of conjoint children nodes, corresponding to all of the opponents responding moves. For each of these children nodes, there exists a set of non-conjoint children nodes, corresponding to all of the player's defending moves. For solving game trees with proof-number search family of algorithms, game trees are to be mapped to and–or trees. MAX-nodes (i.e. maximizing player to move) are represented as OR nodes, MIN-nodes map to AND nodes. The mapping is possible, when the search is done with only a binary goal, which usually is "player to move wins the game".

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  Philco computers

  Philco was one of the pioneers of transistorized computers, also known as second-generation computers. After the company developed the surface-barrier transistor, which was much faster than previous point-contact types, it was awarded contracts for military and government computers. Commercialized derivatives of some of these designs became successful business and scientific computers. The TRANSAC (Transistor Automatic Computer) Model S-1000 was released as a scientific computer. The TRANSAC S-2000 mainframe computer system was first produced in 1958, and a family of compatible machines, with increasing performance, was released over the next several years. However, the mainframe computer market was dominated by IBM. Other companies could not deploy resources for development, customer support and marketing on the scale that IBM could afford, making competition in this segment difficult after the introduction of the IBM 360 family. Philco went bankrupt and was purchased in 1961 by Ford Motor Company, but the computer division carried on until the Philco division of Ford exited the computer business in 1963. The Ford company maintained one Philco mainframe in use until 1981. == The surface-barrier transistor == The surface-barrier transistor developed by Philco in 1953 had a much higher frequency response than the original point-contact transistors. The transistor was made of a thin crystal of germanium, which was electrolytically etched with pits on either side forming a very thin base region, on the order of 5 micrometers. Philco's process for etching was United States patent number 2,885,571. Philco surface-barrier transistors were used in TX-0, and in early models of what would become the DEC PDP product line. Although relatively fast, the small size of the devices limited their power to circuits operating at a few tens of milliwatts. == Military and government == Between 1955 and 1957, Philco built transistor computers for use in aircraft, models C-1000, C-1100, and C-1102, intended for airborne real-time applications. By 1957, the C-1102 had been used by a civilian sector customer. The BASICPAC AN/TYK 6V (first delivery in 1961), COMPAC AN/TYK 4V (not completed), and LOGICPAC systems were built for the US Army as transportable computer systems for use with their Fieldata concept of integrated information management. BASICPAC was a transistorized computer with up to 28,672 words of 38-bit core memory (including sign and parity), available in several configurations from a minimum system, to a truck-borne mobile version, to a fully expanded system. Basic clock periods was 1 microsecond (which gives a clock rate of 1 MHz), with 12 microsecond memory access and a fixed-point multiplication taking 242 microseconds. Input/output was by paper tape reader and punch, or through a teletypewriter. With additional hardware, magnetic tape storage was also available, with up to seven I/O devices. The instruction set had 31 basic operation codes and nine opcodes for I/O === CXPQ === Philco was contracted by the US Navy to build the CXPQ computer. One model was completed and installed at the David Taylor Model Basin. This design was later adapted to become the commercial TRANSAC S-2000. Only one CXPQ was built. The CXPQ is a 48-bit transistorized computer. === SOLO === In 1955, the National Security Agency through the US Navy contracted with Philco to produce a computer suitable for use as a workstation, with an architecture based on the vacuum-tube computer system called Atlas II already in use at the NSA, and similar to the commercial UNIVAC 1103. At the time, Philco was the largest producer of surface barrier transistors, which were the only type available with the speed and quantities required for a computer. The SOLO prototype was delivered in 1958, but required extensive debugging at NSA. Difficulties were encountered with core memory and power supplies. SOLO used paper tape and teleprinter machines for input and output. SOLO cost about $1 million US, and contained 8,000 transistors. While the system was extensively used for training, testing, research and development, no additional units were ordered. SOLO was removed from active service in 1963. The design of the SOLO became commercialized as Philco's TRANSAC Model S-1000. == Commercial == === S-1000 === The TRANSAC S-1000 was a scientific computer with a 36-bit word length and 4096 words of core memory. It was packaged in a container about the size of a large office desk, and used only 1.2 kilowatts, much less than vacuum-tube-based computers of similar capacity. In a 1961 survey, about 15 S-1000 computer installations had been identified. It weighed about 1,650 pounds (750 kg). === S-2000 === The TRANSAC S-2000 was a large mainframe system intended for both business and scientific work. It had a 48-bit word length and supported calculations in fixed point, floating point and binary-coded decimal formats. The original S-2000 "TRANSAC" (Transistor Automatic Computer) released in 1958 was later designated Model 210; it was used internally at Philco. Similar to the Control Data Corporation Model 1604, it was a 48-bit fully transistorized computer. Three succeeding models were released in the series, all compatible with the software of the original model. The Model 211 was introduced in 1960, using micro-alloy diffused field-effect transistors, requiring significant redesign of circuits compared to the original. The TRANSAC S-2000/Philco 210/211 weighed about 2,000 pounds (910 kg). By 1964, eighteen Model 210, eighteen Model 211 and seven Model 212 systems had been sold. After Philco was purchased by Ford Motor Company, the Model 212 was introduced in 1962 and released in 1963. It had 65,535 words of 48-bit memory. Initially made with 6-microsecond core memory, it had better performance than the IBM 7094 transistor computer. It was later upgraded in 1964 to 2-microsecond core memory, which gave the machine floating-point performance greater than the IBM 7030 Stretch computer. A Model 213 was announced in 1964 but never built. By that time competition from IBM had made the Philco computer operations no longer profitable for Ford, and the division was closed down. The Model 212 could carry out a floating-point multiplication in 22 microseconds. Each word contained two 24-bit instructions with 16 bits of address information and eight bits for the opcode. There were 225 different valid opcodes in the Model 212; invalid opcodes were detected and halted the machine. The CPU had an accumulator register of 48 bits, three general-purpose registers of 24 bits, and 32 index registers of 15 bits. Main memory size ranged from 4K words to 64K words. Only the first model had a magnetic drum memory; later editions used tape drives. The Model 212 weighed about 6,500 pounds (3.3 short tons; 2.9 t). Software for the S-2000 initially consisted of TAC (Translator-Assembler-Compiler), and ALTAC, a FORTRAN II-like language with some differences from the IBM 704 FORTRAN implementation. A COBOL compiler was also available, targeted at business applications. The Philco 2400 was the input/output system for the S-2000. Operations such as reading cards or printing were carried out through magnetic tapes, thereby offloading the S-2000 from relatively slow input/output processing. The 2400 had a 24-bit word length and could be supplied with 4K to 32K characters (1K to 8K words) of core memory, rated at 3-microsecond cycle time. The instruction set was aimed at character I/O use. The idea of base registers, implemented in Philco computers, influenced the design of IBM/360. The last Philco TRANSAC S-2000 Model 212 was taken out of service in December 1981, after 19 years of service at Ford.

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  Professional network service

  A professional network service (or, in an Internet context, simply a professional network) is a type of social network service that focuses on interactions and relationships for business opportunities and career growth, with less emphasis on activities in personal life. A professional network service is used by working individuals, job-seekers, and businesses to establish and maintain professional contacts, to find work or hire employees, share professional achievements, sell or promote services, and stay up-to-date with industry news and trends. According to LinkedIn managing director Clifford Rosenberg in an interview with AAP in 2010, "[t]his is a call to action for professionals to re-address their use of social networks and begin to reap as many rewards from networking professionally as they do personally." Businesses mostly depend on resources and information outside the company and to get what they need, they need to reach out and professionally network with others, such as employees or clients as well as potential opportunities. "Nardi, Whittaker, and Schwarz (2002) point out three main tasks that they believe networkers need to attend to keep a successful professional (intentional) network: building a network, maintaining the network, and activating selected contacts. They stress that networkers need to continue to add new contacts to their network to access as many resources as possible and to maintain their network by staying in touch with their contacts. This is so that the contacts are easy to activate when the networker has work that needs to be done." By using a professional network service, businesses can keep all of their networks up-to-date, and in order, and helps figure out the best way to efficiently get in touch with each of them. A service that can do all that helps relieve some of the stress when trying to get things done. Not all professional network services are online sites that help promote a business. Some services connect the user to other services that help promote the business other than online sites, such as phone/Internet companies that provide services and companies that specifically are designed to do all of the promoting, online and in person, for a business. == History == In 1997, professional network services started up throughout the world and continue to grow. The first recognizable site to combine all features, such as creating profiles, adding friends, and searching for friends, was SixDegrees.com. According to Boyd and Ellison's article, "Social Network Sites: Definition, History, and Scholarship", from 1997 to 2001, several community tools began supporting various combinations of profiles and publicly articulated Friends. Boyd and Ellison go on to say that the next wave began with Ryze.com in 2001. It was introduced as a new way "to help people leverage their business networks". == Inside the works == Quite a lot of work is put into a professional network service, such as the number of hours that go into them and the type of people they work for, as well as the business model of it all, such as the professional interaction and the multiple services they deal with. === Types of services === Some professional network services not only help promote the business but can also help in connecting to other people. Those services may include a specific phone and/or Internet company or a company that helps to connect with other businesses. According to the Society for New Communications Research (SNCR), there are at least nine online professional networks that are being used. === Professional interaction === Kaplan and Haenlein elaborate on five key considerations for companies when utilizing media. These include the importance of careful selection, the option to choose existing applications or develop custom ones, ensuring alignment with organizational activities, integrating a comprehensive media plan, and providing accessibility to all stakeholders. ==== Choose carefully ==== "Choosing the right medium for any given purpose depends on the target group to be reached and the message to be communicated. On one hand, each Social Media application usually attracts a certain group of people, and firms should be active wherever their customers are present. On the other hand, there may be situations whereby certain features are necessary to ensure effective communication, and these features are only offered by one specific application." ==== Ensure activity alignment ==== "Sometimes you may decide to rely on various Social Media, or a set of different applications within the same group, to have the largest possible reach." "Using different contact channels can be a worthwhile and profitable strategy." According to the Society for New Communications Research at Harvard University, "the average professional belongs to 3–5 online networks for business use, and LinkedIn, Facebook, and Twitter are among the top used." ==== Integrate a media plan ==== Social media and traditional media are "both part of the same: your corporate image" in the customers' eyes. ==== Allow access to all ==== "...once the firm has decided to utilize Social Media applications, it is worth checking that all employees may access them." According to the SNCR, "the convergence of Internet, mobile, and social media has taken significant shape as professionals rely on anywhere access to information, relationships, and networks." ==== Online usage ==== "Half of the respondents report participating in 3 to 5 online professional networks. Another three in ten participate in 6 or more professional networks." "Popular social networks are now being used frequently as Professional Communities. More than nine in ten respondents indicated that they use LinkedIn and half reported using Facebook. Twitter and blogs were frequently listed as 'professional networks'." === Business model === According to Michael Rappa's article, Business models on the Web", "a business model is the method of doing business by which a company can sustain itself – that is, generate revenue. The business model spells out how a company makes money by specifying where it is positioned in the value chain." Rappa mentions that there are at least nine basic categories from which a business model can be separated. Those categories are a brokerage, advertising, infomediary, merchant, manufacturer, affiliate, community, subscription, and utility. "...a firm may combine several different models as part of its overall Internet business strategy." At first, Flickr started as a way to mainstream public relations. == Social impact == When it comes to the social impact that professional network services have on today's society, it has proved to increase activity. According to the SNCR, "[t]hree quarters of respondents rely on professional networks to support business decisions. Reliance has increased for essentially all respondents over the past three years. Younger (20–35) and older professionals (55+) are more active users of social tools than middle-aged professionals. More people are collaborating outside their company wall than within their organizational intranet." == Limitations == Since the internet and social media are a part of this "world where consumers can speak so freely with each other and businesses have increasingly less control over the information available about them in cyberspace", most firms and businesses are uncomfortable with all the freedom. According to Kaplan and Haenlein's article, "Users of the world, unite! The challenges and opportunities of Social Media", businesses are pushed aside and are only able to sit back and watch as their customers publicly post comments, which may or may not be well-written.

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  Data room

  Data rooms are secure spaces used for housing data, usually of a privileged or confidential nature. They can be physical data rooms, virtual data rooms (VDRs), or data centers. They are primarily used for a variety of corporate purposes, including data storage, document exchange, file sharing, financial transactions, and legal proceedings. Today, data rooms are central to workflows in mergers and acquisitions, venture capital, and corporate restructuring, increasingly utilizing artificial intelligence to securely manage and review large datasets. Historically, data rooms were strictly physical locations heavily guarded and monitored. Today, the vast majority of corporate data rooms are hosted virtually on secure cloud platforms, though physical rooms are still occasionally used for highly sensitive government or proprietary intelligence. == Physical Data Rooms == In mergers and acquisitions (M&A), the traditional data room genuinely consists of a physically secured and continually monitored room, normally in the vendor's offices or those of their legal counsel. Bidders and their advisers visit this room in order to inspect and report on various documents, legal contracts, and financial statements made available during the due diligence process. Historically, physical data rooms presented significant logistical challenges. Often, only one bidder at a time was allowed to enter to maintain document integrity and confidentiality. If new documents or new versions of documents were required, they had to be brought in by courier as hardcopies. Teams involved in large due diligence processes typically had to be flown in from many regions or countries and remain available throughout the process. Because these teams comprised a number of experts in different fields—such as legal counsel, forensic accountants, and industry specialists—the overall cost of keeping such groups on call near the physical data room was often extremely high. == Virtual Data Rooms (VDRs) == To address the costs and logistical bottlenecks of physical data rooms, virtual data rooms (VDRs) were developed to provide secure, online dissemination of confidential information. A VDR is essentially a secure cloud repository with strictly controlled access. Access is managed through secure log-ons supplied by the vendor or authority, which can be disabled at any time if a bidder withdraws from a transaction. Because much of the information released during corporate transactions is highly confidential, VDRs utilize digital rights management (DRM) to control information. Restrictions are applied to the viewers' ability to release data to third parties by disabling forwarding, copying, or printing capabilities. Modern VDRs also employ dynamic watermarking and detailed auditing capabilities. Detailed auditing is required for legal reasons so that a precise digital footprint is kept of who has viewed which version of each document, and for how long. Furthermore, modern VDR platforms are typically built to comply with stringent information security standards such as ISO 27001 and SOC 2. Transitioning from sequential physical data rooms to parallel virtual data rooms has been shown to significantly reduce the duration of M&A transactions while allowing sellers to field multiple bidders simultaneously. == Key Applications == Data rooms are commonly used by legal, accounting, investment banking, and private equity firms. Primary applications include: Mergers and Acquisitions (M&A): VDRs are central to the sell-side M&A process. After potential buyers sign a Non-Disclosure Agreement (NDA) and review a Confidential Information Memorandum (CIM), they are granted data room access to perform deep financial due diligence, such as Quality of Earnings (QoE) analysis and legal liability assessments. Venture Capital and Startups: Startups use data rooms as a centralized location for key operational data, capitalization tables, and financial projections to streamline due diligence for angel investors and venture capital firms during fundraising rounds. Initial Public Offerings (IPOs): Taking a company public requires intense regulatory scrutiny. Data rooms are used to securely share company histories and financial audits with investment bankers, legal teams, and regulatory bodies. Corporate Restructuring and Insolvency: During bankruptcies or corporate carve-outs, data rooms are used to organize outstanding debt profiles, creditor agreements, and operational liabilities. == Emerging Technologies == In recent years, the management of virtual data rooms has increasingly incorporated Artificial Intelligence (AI) and Machine Learning (ML). Generative AI and Natural Language Processing (NLP) tools are now integrated into VDRs to automatically index thousands of documents, perform auto-redaction of personally identifiable information (PII), and assist buy-side analysts in identifying hidden liabilities within unstructured text data during the due diligence phase. Modern AI algorithms can extract line items from financial statements to instantly populate structured databases.

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  AdBlock

  AdBlock is an ad-blocking browser extension for Google Chrome, Apple Safari (desktop and mobile), Firefox, Samsung Internet, Microsoft Edge and Opera. AdBlock allows users to prevent page elements, such as advertisements, from being displayed. It is free to download and use, and it includes optional donations to the developers. The AdBlock extension was created on December 8, 2009, which is the day that supports for extensions was added to Google Chrome. It was one of the first Google Chrome extensions that was made. Since 2016, AdBlock has been based on the Adblock Plus source code. In July 2018, AdBlock acquired uBlock, a commercial ad-blocker owned by uBlock LLC and based on uBlock Origin. In April 2021, eyeo GmbH (developer of Adblock Plus) announced its purchase of AdBlock, Inc (formerly BetaFish, Inc). == Crowdfunding == Gundlach launched a crowdfunding campaign on Crowdtilt in August 2013 in order to fund an ad campaign to raise awareness of ad-blocking and to rent a billboard at Times Square. After the one-month campaign, it raised $55,000. == Sales and acceptable ads == AdBlock was sold to an anonymous buyer in 2015 and on October 15, 2015, Gundlach's name was taken down from the site. In the terms of the deal, the original developer Michael Gundlach left operations to Adblock's continuing director, Gabriel Cubbage, and as of October 2, 2015, AdBlock began participating in the Acceptable Ads program. Acceptable Ads identifies "non-annoying" ads, which AdBlock shows by default. The intent is to allow non-invasive advertising, to either maintain support for websites that rely on advertising as a main source of revenue or for websites that have an agreement with the program. == Filters == AdBlock uses EasyList, the same filter syntax as Adblock Plus for Firefox, and natively supports the use of a number of filter lists. == Partnership with Amnesty International == On March 12, 2016, in support of World Day Against Cyber Censorship, and in partnership with Amnesty International, instead of blocking ads, AdBlock replaced ads with banners linked to articles on Amnesty's website, written by prominent free speech advocates such as Edward Snowden, to raise awareness of government-imposed online censorship and digital privacy issues around the world. The campaign was met with both praise and criticism, with AdBlock's CEO, Gabriel Cubbage, defending the decision in an essay on AdBlock's website, saying "We’re showing you Amnesty banners, just for today, because we believe users should be part of the conversation about online privacy. Tomorrow, those spaces will be vacant again. But take a moment to consider that in an increasingly information-driven world, when your right to digital privacy is threatened, so is your right to free expression." Meanwhile, Simon Sharwood of The Register characterized Cubbage's position as "'You should control your computer except when we feel political', says AdBlock CEO". == AdBlock for Firefox == On September 13, 2014, the AdBlock team released a version for Firefox users, ported from the code for Google Chrome, released under the same free software license as the original Adblock. The extension was removed on April 2, 2015, by an administrator on Mozilla Add-ons. On December 7, 2015, the official AdBlock site's knowledge base article stated that with version 44 or higher of Firefox desktop and Firefox Mobile, AdBlock will not be supported. The last version of Adblock for those platforms will work on older versions of Firefox. AdBlock was released again on Mozilla Add-ons on November 17, 2016. On April 1, 2012, Adblock developer Michael Gundlach tweaked the code to display LOLcats instead of simply blocking ads. Initially developed as a short-lived April Fools joke, the response was so positive that CatBlock was continued to be offered as an optional add-on supported by a monthly subscription. On October 23, 2014, the developer decided to end official support for CatBlock, and made it open-source, under GPLv3 licensing, as the original extension.

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  Menu hack

  A menu hack is a non-standard method of ordering food, usually at fast-food or fast casual restaurants, that offers a different result than what is explicitly stated on a menu. Menu hacks may range from a simple alternate flavor to "gaming the system" in order to obtain more food than normal. They are often spread on social media platforms such as TikTok, and are more popular with Generation Z, which has been known to customize their orders more than previous generations. Hacks are sometimes officially added to the menu after their popularity grows. However, in some cases, they have been criticized for overburdening fast food employees with outlandish requests, sparking debate as to whether certain menu hacks are unethical. The list of all possible menu hacks is called a secret menu. == History == The term "menu hack" stems from hacker culture and its tradition of overcoming previously imposed limitations. However, the tradition of ordering from a secret menu dates back to the early days of fast food. "Animal style" fries, a word of mouth menu item ordered from In-N-Out since the 1960s, was rumored to have been created by local surfers. In the Information Age, the rise of social media gave influencers the ability to communicate unique food combinations to their followers, which proved to go viral easily. Design mistakes in food ordering apps also proved to be easily exploitable. In some cases, these hacks boosted the profile of brands on social media, while in others, they caused financial harm when the company was unprepared to handle the sudden influx of unusual orders. One restaurant chain notable for the phenomenon is Chipotle Mexican Grill. A viral hack from Alexis Frost, suggesting a quesadilla with fajita vegetables inside, dipped in Chipotle vinaigrette mixed with sour cream, obtained 1.9 million views on TikTok, overloading the chain's workers, who had to work harder to prepare more vegetables and vinaigrette. Some restaurants began to deny the dish to customers, forcing them to only order meat and cheese on quesadillas. The company ultimately left the dish on the menu, but urged customers to stop ordering it via social media. When it later officially added the Fajita Quesadilla to the menu, digital sales nearly doubled. A method to order nachos, which are not officially on the menu, was also noted by customers. Starbucks is also famous for menu hacks, including the Pink Drink, a "Barbiecore" beverage in which coconut milk replaced the water in the strawberry açaí refresher. After it went viral, the company made it a permanent menu item and distributed it bottled in grocery stores. == Controversy == Menu hacks have been subject to a growing backlash, with employees stating that they "dread" younger customers due to the proliferation of unusual orders. Service industry workers, already overworked and underpaid, have called the rise of menu hacks and their difficulty to make an additional reason to unionize and demand higher wages.

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  Social media and identity

  Social media can have both positive and negative impacts on a user's identity. Scholars within the fields of psychology and communication study the relationship between social media and identity in order to understand individual behavior, psychological impacts, and social patterns. Communication within political or social groups online can result in practice application, real-world implementation of a concept, of those found identities or the adoption of them as a whole. Young people, defined as emerging adults in or entering college, are especially found to have their identities shaped through social media. Sometimes it seems as though social media is taking over and changing us for the worse. Social media is always changing and can be hard to keep up with. Platforms come and go trends change everyday. What was cool yesterday is lame today. The biggest change from recent years that users are still adjusting to is the name change of Twitter now called X. Since Elon Musk purchased the platform he changed the name but nothing else about the app. Users now feel the need to explain when talking about X. Now it is often referred to as ‘X(Twitter)’ to clarify. == Social Media Usage and Demographics == We know what social media is and how it is used but who uses it? The Pew Research center conducted a 10 year study from 2005-2015 about the demographics of social media usage. While this article is 10 years old the statistics in it are from a very formative time in social media. This is when most people joined and were consistently using social media. Age: While it is no surprise that 90% of young adults use social media they are the main demographic of users. Older adults (65 and older) really hit a boom on social media. In 2005 only 2% of older adults used any form of social media. By 2015 35% of older adults used social media. We can infer that that percentage has grown even more since 2015. Gender: It is known that women tend to use social media more than men. In 2015 it was noted that 65% of women used social media. Men were not far behind, 62% of men were reported to use social media. There are no notable differences of users from various races and ethnicities. The research also shows that more suburban and urban residents use social media over those who live in rural areas. == Young adults == Young adults are especially influenced by social media, where they find social groups to belong to. Research shows that nearly half of teens believe social media platforms has a negative impact on people their age. Psychologists believe that at a time when young adults are coming into adolescence, they are more likely to be influenced by what they see on sites like Instagram or Twitter. Most young adults will widely share, with varying degrees of accuracy, honesty, and openness, information that in the past would have been private or reserved for select individuals. Key questions include whether they accurately portray their identities online and whether the use of social media might impact young adults' identity development. Media Imagery, in particular, is said to be a major influence on the minds of young men and women. Studies have shown that it is even more relevant when it comes to the issue of body image. Social media, in part, has been created to host a safe haven for those who do not claim a solid identity in the material world, but past identities are not easy to escape from since the Internet preserves much of the information that was shared. Social media is an essential part of the social lives of young adults. They rely on it to maintain relationships, create new relationships, and stay up to date with the world around them. Adolescents find social media to be extremely helpful when changing environments, like moving off to university for example. Social media provides students, especially first year students, the opportunity to create the identity they want the world to see. However, it has been seen that these students create online personas that may not reflect their true selves bringing up the issues of impression management. Social media provides young adults with the opportunity to present themselves as something other than their authentic self. Social media providers can help build relationships and community on their platforms. This is something that will create a more positive impact from social media. When young adults interact with each other using social media they are creating something called a social self-identity. Social self identity is what individuals create when they assimilate to being in a group. Social media has gained the reputation of being isolating. If these platforms encourage community then they can help grow users' social self-identity. == Media literacy == The definition of media literacy has evolved over time to encompass a range of experiences that can occur in social media or other digital spaces. The definition of media literacy is also broad and wide ranging in its context. Currently, media literacy is the idea that one is able to analyze, evaluate, and interact with media content in a meaningful way. Educators teach media literacy skills because of the vulnerable relationship that young adults can have with social media. Some examples of media literacy practices, particularly on Twitter, include using hashtags, live tweeting, and sharing information. One of the overall goals of media literacy within the context of social media is to keep young adults aware of potentially violent, graphic, or dangerous content that they may come across on the internet, and how to determine if the content is credible while engaging responsibly with it. In order to be considered media-literate, a person must be able to take in media from online and social platforms and have the correct competencies and context to be able to organize the information. In order to be considered media-literate, the digital information must be given to the user in a way that it can be put into the correct perspective and analyzed, deducted and synthesized.Teenagers and young adults can be vulnerable to specific content online outside of their age-range. Media literacy campaigns and education research shows that targeting those who fall into this age category would be the best way to understand and target their needs as young online users. There are multiple individual studies investigating social media identity relating to media literacy online, however there is a need for much more conclusive information that analyzes multiple studies at a time. Social media literacy is still considered an under-researched topic. Many scholars in media literacy research emphasize the impact of training young adults to consume media in a safe way is the major solution for furthering internet education in children and young adults. The more information the young adults are given on media literacy, the better prepared they are to enter the digital world confidently. One scientific model that has been proposed, known as The Social Media Literacy (SMILE) model is a framework that hypothesizes that at the core of this model it is helping young adults truly know the meaning and display the actions of media literacy online. SMILE is also meant to inspire more research on the subject of media literacy as it relates to social media effects and young adult learning abilities. The model was applied through the lens of a social media positivity bias among adolescents and puts forth five different assumptions about social media and media literacy; Social media literacy as a moderator (what is seen on social media) Social media literacy as a predictor (what is seen for specific individuals on social media) Media literacy within social media is a reciprocal process The development of social media literacy depends on a conditional process of variables affecting other variables Media literacy within social media is a differential learning process, and who teaches it is highly affective of the outcome This model also stresses that human beings learn media literacy (and social media literacy) naturally as they go through life. Research suggests that having young adults taught media literacy from an educator may make them less interested (and therefore less careful) of threats on social media. == Self Presentation == People create images of themselves to present to the public, a process called self presentation. Depending on the demographic, presenting oneself as authentic can result in identity clarity. Methods of self presentation can also be influenced by geography. The framework for this relationship between a user's location and their social media presentation is called the spatial self. Users depict their spatial self in order to include their physical space as a part of their self presentation to an audience. According to a 2018 research paper, patients of plastic surgeons have gone in and asked for specific snapchat "filter" features. This led to a theory of Snap

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  Perfectly Imperfect (platform)

  Perfectly Imperfect is an online newsletter and social media platform. It was initially founded in 2020 as a biweekly email newsletter that focused on recommendations. In January 2024, Perfectly Imperfect launched PI.FYI, a social media platform. The platform is based around sharing recommendations. Its main feed is presented in reverse chronological order and is not algorithmically curated. == History == Perfectly Imperfect was started during the COVID-19 pandemic by Tyler Bainbridge, alongside college friends Alex Cushing and Serey Morm, whom he met at UMass Lowell; Morm later departed. Motivated by a dissatisfaction with algorithm-driven recommendation culture, they launched on Substack in September 2020. Its early newsletter format, PI, published brief recommendation lists and personal notes from contributors. Contributors have included a mix of underground artists and more established creative figures, such as Charli XCX, Chloe Cherry, Chloe Wise, and Meetka Otto. In October 2024, PI announced it was leaving Substack to launch its own site. == Overview == The current platform, PI.FYI, features both editorial content (guest columns, long-form essays, staff picks) and user-generated recommendations. The platform also supports "Ask" posts, where users can solicit recommendations from the community, and allows commenting, liking, and profile customization. In August 2025, it launched an events feature. In 2022, Perfectly Imperfect hosted their first offline event at Baby's All Right in Brooklyn, with a performance by The Dare. They have since expanded their event promotion/sponsorship to markets such as Los Angeles, San Francisco, and even Auckland.

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  Intelligent database

  Until the 1980s, databases were viewed as computer systems that stored record-oriented and business data such as manufacturing inventories, bank records, and sales transactions. A database system was not expected to merge numeric data with text, images, or multimedia information, nor was it expected to automatically notice patterns in the data it stored. In the late 1980s the concept of an intelligent database was put forward as a system that manages information (rather than data) in a way that appears natural to users and which goes beyond simple record keeping. The term was introduced in 1989 by the book Intelligent Databases by Kamran Parsaye, Mark Chignell, Setrag Khoshafian and Harry Wong. The concept postulated three levels of intelligence for such systems: high level tools, the user interface and the database engine. The high level tools manage data quality and automatically discover relevant patterns in the data with a process called data mining. This layer often relies on the use of artificial intelligence techniques. The user interface uses hypermedia in a form that uniformly manages text, images and numeric data. The intelligent database engine supports the other two layers, often merging relational database techniques with object orientation. In the twenty-first century, intelligent databases have now become widespread, e.g. hospital databases can now call up patient histories consisting of charts, text and x-ray images just with a few mouse clicks, and many corporate databases include decision support tools based on sales pattern analysis.

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  Key (cryptography)

  A key in cryptography is a piece of information, usually a string of numbers or letters that are stored in a file, which, when processed through a cryptographic algorithm, can encode or decode cryptographic data. Based on the used method, the key can be different sizes and varieties, but in all cases, the strength of the encryption relies on the security of the key being maintained. A key's security strength is dependent on its algorithm, the size of the key, the generation of the key, and the process of key exchange. == Scope == The key is what is used to encrypt data from plaintext to ciphertext. There are different methods for utilizing keys and encryption. === Symmetric cryptography === Symmetric cryptography refers to the practice of the same key being used for both encryption and decryption. === Asymmetric cryptography === Asymmetric cryptography has separate keys for encrypting and decrypting. These keys are known as the public and private keys, respectively. == Purpose == Since the key protects the confidentiality and integrity of the system, it is important to be kept secret from unauthorized parties. With public key cryptography, only the private key must be kept secret, but with symmetric cryptography, it is important to maintain the confidentiality of the key. Kerckhoff's principle states that the entire security of the cryptographic system relies on the secrecy of the key. == Key sizes == Key size is the number of bits in the key defined by the algorithm. This size defines the upper bound of the cryptographic algorithm's security. The larger the key size, the longer it will take before the key is compromised by a brute force attack. Since perfect secrecy is not feasible for key algorithms, researches are now more focused on computational security. In the past, keys were required to be a minimum of 40 bits in length, however, as technology advanced, these keys were being broken quicker and quicker. As a response, restrictions on symmetric keys were enhanced to be greater in size. Currently, 2048 bit RSA is commonly used, which is sufficient for current systems. However, current RSA key sizes would all be cracked quickly with a powerful quantum computer. "The keys used in public key cryptography have some mathematical structure. For example, public keys used in the RSA system are the product of two prime numbers. Thus public key systems require longer key lengths than symmetric systems for an equivalent level of security. 3072 bits is the suggested key length for systems based on factoring and integer discrete logarithms which aim to have security equivalent to a 128 bit symmetric cipher." == Key generation == To prevent a key from being guessed, keys need to be generated randomly and contain sufficient entropy. The problem of how to safely generate random keys is difficult and has been addressed in many ways by various cryptographic systems. A key can directly be generated by using the output of a Random Bit Generator (RBG), a system that generates a sequence of unpredictable and unbiased bits. A RBG can be used to directly produce either a symmetric key or the random output for an asymmetric key pair generation. Alternatively, a key can also be indirectly created during a key-agreement transaction, from another key or from a password. Some operating systems include tools for "collecting" entropy from the timing of unpredictable operations such as disk drive head movements. For the production of small amounts of keying material, ordinary dice provide a good source of high-quality randomness. == Establishment scheme == The security of a key is dependent on how a key is exchanged between parties. Establishing a secured communication channel is necessary so that outsiders cannot obtain the key. A key establishment scheme (or key exchange) is used to transfer an encryption key among entities. Key agreement and key transport are the two types of a key exchange scheme that are used to be remotely exchanged between entities . In a key agreement scheme, a secret key, which is used between the sender and the receiver to encrypt and decrypt information, is set up to be sent indirectly. All parties exchange information (the shared secret) that permits each party to derive the secret key material. In a key transport scheme, encrypted keying material that is chosen by the sender is transported to the receiver. Either symmetric key or asymmetric key techniques can be used in both schemes. The Diffie–Hellman key exchange and Rivest-Shamir-Adleman (RSA) are the most two widely used key exchange algorithms. In 1976, Whitfield Diffie and Martin Hellman constructed the Diffie–Hellman algorithm, which was the first public key algorithm. The Diffie–Hellman key exchange protocol allows key exchange over an insecure channel by electronically generating a shared key between two parties. On the other hand, RSA is a form of the asymmetric key system which consists of three steps: key generation, encryption, and decryption. Key confirmation delivers an assurance between the key confirmation recipient and provider that the shared keying materials are correct and established. The National Institute of Standards and Technology recommends key confirmation to be integrated into a key establishment scheme to validate its implementations. == Management == Key management concerns the generation, establishment, storage, usage and replacement of cryptographic keys. A key management system (KMS) typically includes three steps of establishing, storing and using keys. The base of security for the generation, storage, distribution, use and destruction of keys depends on successful key management protocols. == Key vs password == A password is a memorized series of characters including letters, digits, and other special symbols that are used to verify identity. It is often produced by a human user or a password management software to protect personal and sensitive information or generate cryptographic keys. Passwords are often created to be memorized by users and may contain non-random information such as dictionary words. On the other hand, a key can help strengthen password protection by implementing a cryptographic algorithm which is difficult to guess or replace the password altogether. A key is generated based on random or pseudo-random data and can often be unreadable to humans. A password is less safe than a cryptographic key due to its low entropy, randomness, and human-readable properties. However, the password may be the only secret data that is accessible to the cryptographic algorithm for information security in some applications such as securing information in storage devices. Thus, a deterministic algorithm called a key derivation function (KDF) uses a password to generate the secure cryptographic keying material to compensate for the password's weakness. Various methods such as adding a salt or key stretching may be used in the generation.

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  ServerNet

  ServerNet is a switched fabric communications link primarily used in proprietary computers made by Tandem Computers, Compaq, and HP. Its features include good scalability, clean fault containment, error detection and failover. The ServerNet architecture specification defines a connection between nodes, either processor or high performance I/O nodes such as storage devices. == History == Tandem Computers developed the original ServerNet architecture and protocols for use in its own proprietary computer systems starting in 1992, and released the first ServerNet systems in 1995. Early attempts to license the technology and interface chips to other companies failed, due in part to a disconnect between the culture of selling complete hardware / software / middleware computer systems and that needed for selling and supporting chips and licensing technology. A follow-on development effort ported the Virtual Interface Architecture to ServerNet with PCI interface boards connecting personal computers. Infiniband directly inherited many ServerNet features. As of 2017, systems still ship based on the ServerNet architecture.

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  Rassd News Network

  Rassd News Network, also known by its initials of RNN (Arabic:شبكة رصد الاخبارية), is an alternative media network based in Cairo, Egypt. RNN was launched as a Facebook-based news source launched on January 25, 2011. It quickly advanced to become a primary contributor of Egyptian revolution-related news that year. Applying the motto "From the people to the people," the citizen journalists who created RNN have since added a Twitter feed and launched an independent website dedicated to short news stories favored by an online audience. RNN is an organized citizen news network with four working committees; one for editing the news, another to support the correspondents covering Egypt, a third for managing the multimedia feeds and a fourth for staff functions such as development, training and public relations. RNN's Arabic name, Rassd, is an acronym that stands for Rakeb (observe), Sawwer (record) and Dawwen (blog). RNN created a Ustream channel on January 27, 2011, and a YouTube account a month later. The success of RNN and its new social media model is evidenced in its recent local network expansion into Libya, Morocco, Syria, Jerusalem and Turkey. Even so, one media scholar in the US (commenting in 2011) called the accuracy of RNN's reporting "fairly mediocre". RNN has endured closures of their Facebook profile and YouTube account as part of the attacks from private media, attempting to thwart their work and influence their content. == Use of RNN's news by international media == RNN has been a global source of Egyptian revolution-related news since its launch. During the early days of the citizen uprisings across the Middle East, major networks such as BBC, Reuters, Al Jazeera and Al Arabiya used some of Rassd's news and photos, and followed the network on Twitter. Three days after the online portal went live it was streaming video to MSNBC through its Facebook page. Then on February 5, 2011, Louisville's NBC-affiliate cited RNN, Cairo when it reported that President Hosni Mubarak had stepped down as head of Egypt's ruling party.

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  Cost-sensitive machine learning

  Cost-sensitive machine learning is an approach within machine learning that considers varying costs associated with different types of errors. This method diverges from traditional approaches by introducing a cost matrix, explicitly specifying the penalties or benefits for each type of prediction error. The inherent difficulty which cost-sensitive machine learning tackles is that minimizing different kinds of classification errors is a multi-objective optimization problem. == Overview == Cost-sensitive machine learning optimizes models based on the specific consequences of misclassifications, making it a valuable tool in various applications. It is especially useful in problems with a high imbalance in class distribution and a high imbalance in associated costs Cost-sensitive machine learning introduces a scalar cost function in order to find one (of multiple) Pareto optimal points in this multi-objective optimization problem (similar to the Weighted sum model) == Cost Matrix == The cost matrix is a crucial element within cost-sensitive modeling, explicitly defining the costs or benefits associated with different prediction errors in classification tasks. Represented as a table, the matrix aligns true and predicted classes, assigning a cost value to each combination. For instance, in binary classification, it may distinguish costs for false positives and false negatives. The utility of the cost matrix lies in its application to calculate the expected cost or loss. The formula, expressed as a double summation, utilizes joint probabilities: Expected Loss = ∑ i ∑ j P ( Actual i , Predicted j ) ⋅ Cost Actual i , Predicted j {\displaystyle {\text{Expected Loss}}=\sum _{i}\sum _{j}P({\text{Actual}}_{i},{\text{Predicted}}_{j})\cdot {\text{Cost}}_{{\text{Actual}}_{i},{\text{Predicted}}_{j}}} Here, P ( Actual i , Predicted j ) {\displaystyle P({\text{Actual}}_{i},{\text{Predicted}}_{j})} denotes the joint probability of actual class i {\displaystyle i} and predicted class j {\displaystyle j} , providing a nuanced measure that considers both the probabilities and associated costs. This approach allows practitioners to fine-tune models based on the specific consequences of misclassifications, adapting to scenarios where the impact of prediction errors varies across classes. == Applications == === Fraud Detection === In the realm of data science, particularly in finance, cost-sensitive machine learning is applied to fraud detection. By assigning different costs to false positives and false negatives, models can be fine-tuned to minimize the overall financial impact of misclassifications. === Medical Diagnostics === In healthcare, cost-sensitive machine learning plays a role in medical diagnostics. The approach allows for customization of models based on the potential harm associated with misdiagnoses, ensuring a more patient-centric application of machine learning algorithms. == Challenges == A typical challenge in cost-sensitive machine learning is the reliable determination of the cost matrix which may evolve over time. == Literature == Cost-Sensitive Machine Learning. USA, CRC Press, 2011. ISBN 9781439839287 Abhishek, K., Abdelaziz, D. M. (2023). Machine Learning for Imbalanced Data: Tackle Imbalanced Datasets Using Machine Learning and Deep Learning Techniques. (n.p.): Packt Publishing. ISBN 9781801070881

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  Application delivery network

  An application delivery network (ADN) is a suite of technologies that, when deployed together, provide availability, security, visibility, and acceleration for Internet applications such as websites. ADN components provide supporting functionality that enables website content to be delivered to visitors and other users of that website, in a fast, secure, and reliable way. Gartner defines application delivery networking as the combination of WAN optimization controllers (WOCs) and application delivery controllers (ADCs). At the data center end of an ADN is the ADC, an advanced traffic management device that is often also referred to as a web switch, content switch, or multilayer switch, the purpose of which is to distribute traffic among a number of servers or geographically dislocated sites based on application specific criteria. In the branch office portion of an ADN is the WAN optimization controller, which works to reduce the number of bits that flow over the network using caching and compression, and shapes TCP traffic using prioritization and other optimization techniques. Some WOC components are installed on PCs or mobile clients, and there is typically a portion of the WOC installed in the data center. Application delivery networks are also offered by some CDN vendors. The ADC, one component of an ADN, evolved from layer 4-7 switches in the late 1990s when it became apparent that traditional load balancing techniques were not robust enough to handle the increasingly complex mix of application traffic being delivered over a wider variety of network connectivity options. == Application delivery techniques == The Internet was designed according to the end-to-end principle. This principle keeps the core network relatively simple and moves the intelligence as much as possible to the network end-points: the hosts and clients. An Application Delivery Network (ADN) enhances the delivery of applications across the Internet by employing a number of optimization techniques. Many of these techniques are based on established best-practices employed to efficiently route traffic at the network layer including redundancy and load balancing In theory, an Application Delivery Network (ADN) is closely related to a content delivery network. The difference between the two delivery networks lies in the intelligence of the ADN to understand and optimize applications, usually referred to as application fluency. Application Fluent Network (AFN) is based on the concept of Application Fluency to refer to WAN optimization techniques applied at Layer Four to Layer Seven of the OSI model for networks. Application Fluency implies that the network is fluent or intelligent in understanding and being able to optimize delivery of each application. Application Fluent Network is an addition of SDN capabilities. The acronym 'AFN' is used by Alcatel-Lucent Enterprise to refer to an Application Fluent Network. Application delivery uses one or more layer 4–7 switches, also known as a web switch, content switch, or multilayer switch to intelligently distribute traffic to a pool, also known as a cluster or farm, of servers. The application delivery controller (ADC) is assigned a single virtual IP address (VIP) that represents the pool of servers. Traffic arriving at the ADC is then directed to one of the servers in the pool (cluster, farm) based on a number of factors including application specific data values, application transport protocol, availability of servers, current performance metrics, and client-specific parameters. An ADN provides the advantages of load distribution, increase in capacity of servers, improved scalability, security, and increased reliability through application specific health checks. Increasingly the ADN comprises a redundant pair of ADC on which is integrated a number of different feature sets designed to provide security, availability, reliability, and acceleration functions. In some cases these devices are still separate entities, deployed together as a network of devices through which application traffic is delivered, each providing specific functionality that enhances the delivery of the application. == ADN optimization techniques == === TCP multiplexing === TCP Multiplexing is loosely based on established connection pooling techniques utilized by application server platforms to optimize the execution of database queries from within applications. An ADC establishes a number of connections to the servers in its pool and keeps the connections open. When a request is received by the ADC from the client, the request is evaluated and then directed to a server over an existing connection. This has the effect of reducing the overhead imposed by establishing and tearing down the TCP connection with the server, improving the responsiveness of the application. Some ADN implementations take this technique one step further and also multiplex HTTP and application requests. This has the benefit of executing requests in parallel, which enhances the performance of the application. === TCP optimization === There are a number of Request for Comments (RFCs) which describe mechanisms for improving the performance of TCP. Many ADN implement these RFCs in order to provide enhanced delivery of applications through more efficient use of TCP. The RFCs most commonly implemented are: Delayed Acknowledgements Nagle Algorithm Selective Acknowledgements Explicit Congestion Notification ECN Limited and Fast Retransmits Adaptive Initial Congestion Windows === Data compression and caching === ADNs also provide optimization of application data through caching and compression techniques. There are two types of compression used by ADNs today: industry standard HTTP compression and proprietary data reduction algorithms. It is important to note that the cost in CPU cycles to compress data when traversing a LAN can result in a negative performance impact and therefore best practices are to only utilize compression when delivering applications via a WAN or particularly congested high-speed data link. HTTP compression is asymmetric and transparent to the client. Support for HTTP compression is built into web servers and web browsers. All commercial ADN products currently support HTTP compression. A second compression technique is achieved through data reduction algorithms. Because these algorithms are proprietary and modify the application traffic, they are symmetric and require a device to reassemble the application traffic before the client can receive it. A separate class of devices known as WAN Optimization Controllers (WOC) provide this functionality, but the technology has been slowly added to the ADN portfolio over the past few years as this class of device continues to become more application aware, providing additional features for specific applications such as CIFS and SMB. == ADN reliability and availability techniques == === Advanced health checking === Advanced health checking is the ability of an ADN to determine not only the state of the server on which an application is hosted, but the status of the application it is delivering. Advanced health checking techniques allow the ADC to intelligently determine whether or not the content being returned by the server is correct and should be delivered to the client. This feature enables other reliability features in the ADN, such as resending a request to a different server if the content returned by the original server is found to be erroneous. === Load balancing algorithms === The load balancing algorithms found in today's ADN are far more advanced than the simplistic round-robin and least connections algorithms used in the early 1990s. These algorithms were originally loosely based on operating systems' scheduling algorithms, but have since evolved to factor in conditions peculiar to networking and application environments. It is more accurate to describe today's "load balancing" algorithms as application routing algorithms, as most ADN employ application awareness to determine whether an application is available to respond to a request. This includes the ability of the ADN to determine not only whether the application is available, but whether or not the application can respond to the request within specified parameters, often referred to as a service level agreement. Typical industry standard load balancing algorithms available today include: Round Robin Least Connections Fastest Response Time Weighted Round Robin Weighted Least Connections Custom values assigned to individual servers in a pool based on SNMP or other communication mechanism === Fault tolerance === The ADN provides fault tolerance at the server level, within pools or farms. This is accomplished by designating specific servers as a 'backup' that is activated automatically by the ADN in the event that the primary server(s) in the pool fail. The ADN also ensures application availability and reliability through its ability to seamlessly "failover"

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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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