AI Face Blur

AI Face Blur — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Weibo

  Weibo (Chinese: 微博; pinyin: Wēibó), or Sina Weibo (Chinese: 新浪微博; pinyin: Xīnlàng Wēibó), is a Chinese microblogging (weibo) website. Launched by Sina Corporation on 14 August 2009, it is one of the biggest social media platforms in China, with over 582 million monthly active users (252 million daily active users) as of Q1 2022. The platform has been highly successful but has faced criticism for heavy censorship. Sina had gone public on the Nasdaq in 2000. In March 2014, Sina announced a spinoff of Weibo and filed an IPO under the symbol WB. Sina carved out 11% of Weibo in the IPO, with Alibaba owning 32% post-IPO. The company began trading publicly on 17 April 2014. In March 2017, Sina launched Sina Weibo International Version. In November 2018, Sina Weibo suspended its registration function for minors under the age of 14. In July 2019, Sina Weibo announced that it would launch a two-month campaign to clean up pornographic and vulgar information, named "Project Deep Blue" (蔚蓝计划). On 29 September 2020, the company announced it would go private again due to rising tensions between the US and China. == Name == "Weibo" (微博) is the Chinese word for "microblog". Sina Weibo launched its new domain name weibo.com on 7 April 2011, deactivating and redirecting from the old domain, t.sina.com.cn, to the new one. Due to its popularity, the media sometimes refers to the platform simply as "Weibo", despite the numerous other Chinese microblogging services including Tencent Weibo, Sohu Weibo, and NetEase Weibo. However, the latter three have stopped providing services. == Background == Sina Weibo is a platform based on fostering user relationships to share, disseminate, and receive information. Through the website or the mobile app, users can upload pictures and videos publicly for instant sharing, with other users being able to comment with text, pictures and videos, or use a multimedia instant messaging service. The company initially invited a large number of celebrities to join the platform at the beginning and has since invited many media personalities, government departments, businesses and non-governmental organizations to open accounts for the purpose of publishing and communicating information. To avoid the impersonation of celebrities, Sina Weibo uses verification symbols; celebrity accounts have an orange letter "V" and organizations' accounts have a blue letter "V". Sina Weibo has more than 500 million registered users; out of these, 313 million are monthly active users, 85% use the Weibo mobile app, 70% are college-aged, 50.10% are male and 49.90% are female. There are over 100 million messages posted by users each day. With more than 100 million followers, actress Xie Na holds the record for the most followers on the platform. Despite fierce competition among Chinese social media platforms, Sina Weibo remains the most popular. == History == After the July 2009 Ürümqi riots, China shut down most domestic microblogging services, including Fanfou, the very first weibo service. Many popular non-China-based microblogging services like Twitter, Facebook, and Plurk have since been blocked. Sina Corporation CEO Charles Chao considered this to be an opportunity, and on 14 August 2009, Sina launched the tested version of Sina Weibo. Basic functions including message, private message, comment and reposting were made available that September. A Sina Weibo–compatible API platform for developing third-party applications was launched on 28 July 2010. On 1 December 2010, the website experienced an outage, which administrators later said was due to the ever-increasing numbers of users and posts. Registered users surpassed 100 million in February 2011. Since 23 March 2011, t.cn has been used as Sina Weibo's official shortened URL in lieu of sinaurl.cn. On 7 April 2011, weibo.com replaced t.sina.com.cn as the new main domain name used by the website. The official logo was also updated. In June 2011, Sina announced an English-language version of Sina Weibo would be developed and launched, though content would still be governed by Chinese law. On 11 January 2013, Sina Weibo and Alibaba China (a subsidiary of Alibaba Group) signed a strategic cooperation agreement. With more and more foreign celebrities using Sina Weibo, language translation has become an urgent need for Chinese users who wish to communicate with their idols online, especially Korean. In January 2013, Sina Weibo and NetEase.com announced that they had reached a strategic cooperation agreement. When users browse foreign language content, they can now directly obtain translation results through the YouDao Dictionary. The Sina Weibo financial report in February 2013 showed that its total revenue was approximately US$66 million and that the number of registered users had exceeded the 500 million mark. In April 2013, Sina officially announced that Sina Weibo had signed a strategic cooperation agreement with Alibaba. The two sides conducted in-depth cooperation in areas such as user account interoperability, data exchange, online payment, and internet marketing. At the same time, Sina announced that Alibaba, through its wholly owned subsidiary, had purchased the preferred shares and common shares issued by Sina Weibo Company for US$586 million, which accounted for approximately 18% of Weibo's fully diluted and diluted total shares. === Ownership === On 9 April 2013, Alibaba Group announced that it would acquire 18% of Sina Weibo for US$586 million, with the option to buy up to 30% in the future. Alibaba exercised this option when Weibo was listed on the NASDAQ in April 2014. == Users == According to iResearch's report on 30 March 2011, Sina Weibo had 56.5% of China's microblogging market based on active users and 86.6% based on browsing time over competitors such as Tencent Weibo and Baidu. According to research by Sina Corporation, the number of active users reached over 400 million by Q1 2018, making Sina Weibo the 7th platform with at least 400 million active users, and daily usage increased by 21%. As of 2017, approximately 80% of its users were in their 20s and 30s. The top 100 users had over 485 million followers combined. More than 5,000 companies and 2,700 media organizations in China use Sina Weibo. The site is maintained by a growing microblogging department of 200 employees responsible for technology, design, operations, and marketing. Sina executives invited and persuaded many Chinese celebrities to join the platform. Users now include Asian celebrities, movie stars, singers, famous business and media figures, athletes, scholars, artists, organizations, religious figures, government departments, and officials from Hong Kong, Mainland China, Malaysia, Singapore, Taiwan, and Macau, as well as some famous foreign individuals and organizations, including Kevin Rudd, Boris Johnson, David Cameron, Narendra Modi, Toshiba, and the Germany national football team. Sina Weibo has a verification program for known people and organizations. Once an account is verified, a verification badge is added beside the account name. == Features == Many of Sina Weibo's features resemble those of Twitter. A user may post with a 140-character limit (increased to 2,000 as of January 2016 with the exception of reposts and comments). An analysis of 29 million Weibo posts found the median length was 14 characters. Users may mention or talk to other people using "@UserName" formatting, add hashtags, follow other users to make their posts appear in one's own timeline, re-post with "//@UserName" similar to Twitter's retweet function "RT @UserName", select posts for one's favorites list, and verify the account if the user is a celebrity, brand, business or otherwise of public interest. URLs are automatically shortened using the domain name t.cn, akin to Twitter's t.co. Official and third-party applications can access Sina Weibo from other websites or platforms. Users may: Submit up to 18 images/video files in every post Send personal messages to followers Follow others and be followed Post "stories" like on Instagram React to posts using different emojis Receive monetary rewards that can be used in a digital store linked to Weibo View posts identified as "hot" or popular Display the location they post from Hashtags differ slightly between Sina Weibo and Twitter, using the double-hashtag "#HashName#" format (the lack of spacing between Chinese characters necessitates a closing tag). Users can own a hashtag by requesting hashtag monitoring; the company reviews these requests and responds within one to three days. Once a user owns a hashtag, they have access to a wide variety of functions available only to them on the condition that they remain active (less than 1 post per calendar week revokes these privileges). Additionally, comments appear as a list below each post. A commenter can also choose to re-post the comment, quoting the whole original post, to their own page. Unregistered users can only browse a few post

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  Code (cryptography)

  In cryptology, a code is a method used to encrypt a message that operates at the level of meaning; that is, words or phrases are converted into something else. A code might transform "change" into "CVGDK" or "cocktail lounge". The U.S. National Security Agency defined a code as "A substitution cryptosystem in which the plaintext elements are primarily words, phrases, or sentences, and the code equivalents (called "code groups") typically consist of letters or digits (or both) in otherwise meaningless combinations of identical length." A codebook is needed to encrypt, and decrypt the phrases or words. By contrast, ciphers encrypt messages at the level of individual letters, or small groups of letters, or even, in modern ciphers, individual bits. Messages can be transformed first by a code, and then by a cipher. Such multiple encryption, or "superencryption" aims to make cryptanalysis more difficult. Another comparison between codes and ciphers is that a code typically represents a letter or groups of letters directly without the use of mathematics. As such the numbers are configured to represent these three values: 1001 = A, 1002 = B, 1003 = C, ... . The resulting message, then would be 1001 1002 1003 to communicate ABC. Ciphers, however, utilize a mathematical formula to represent letters or groups of letters. For example, A = 1, B = 2, C = 3, ... . Thus the message ABC results by multiplying each letter's value by 13. The message ABC, then would be 13 26 39. Codes have a variety of drawbacks, including susceptibility to cryptanalysis and the difficulty of managing the cumbersome codebooks, so ciphers are now the dominant technique in modern cryptography. In contrast, because codes are representational, they are not susceptible to mathematical analysis of the individual codebook elements. In the example, the message 13 26 39 can be cracked by dividing each number by 13 and then ranking them alphabetically. However, the focus of codebook cryptanalysis is the comparative frequency of the individual code elements matching the same frequency of letters within the plaintext messages using frequency analysis. In the above example, the code group, 1001, 1002, 1003, might occur more than once and that frequency might match the number of times that ABC occurs in plain text messages. (In the past, or in non-technical contexts, code and cipher are often used to refer to any form of encryption). == One- and two-part codes == Codes are defined by "codebooks" (physical or notional), which are dictionaries of codegroups listed with their corresponding plaintext. Codes originally had the codegroups assigned in 'plaintext order' for convenience of the code designed, or the encoder. For example, in a code using numeric code groups, a plaintext word starting with "a" would have a low-value group, while one starting with "z" would have a high-value group. The same codebook could be used to "encode" a plaintext message into a coded message or "codetext", and "decode" a codetext back into plaintext message. In order to make life more difficult for codebreakers, codemakers designed codes with no predictable relationship between the codegroups and the ordering of the matching plaintext. In practice, this meant that two codebooks were now required, one to find codegroups for encoding, the other to look up codegroups to find plaintext for decoding. Such "two-part" codes required more effort to develop, and twice as much effort to distribute (and discard safely when replaced), but they were harder to break. The Zimmermann Telegram in January 1917 used the German diplomatic "0075" two-part code system which contained upwards of 10,000 phrases and individual words. == One-time code == A one-time code is a prearranged word, phrase or symbol that is intended to be used only once to convey a simple message, often the signal to execute or abort some plan or confirm that it has succeeded or failed. One-time codes are often designed to be included in what would appear to be an innocent conversation. Done properly they are almost impossible to detect, though a trained analyst monitoring the communications of someone who has already aroused suspicion might be able to recognize a comment like "Aunt Bertha has gone into labor" as having an ominous meaning. Famous example of one time codes include: In the Bible, Jonathan prearranges a code with David, who is going into hiding from Jonathan's father, King Saul. If, during archery practice, Jonathan tells the servant retrieving arrows "the arrows are on this side of you," David may safely return to court; if the command is "the arrows are beyond you," David must flee. "One if by land; two if by sea" in "Paul Revere's Ride" made famous in the poem by Henry Wadsworth Longfellow "Climb Mount Niitaka" - the signal to Japanese planes to begin the attack on Pearl Harbor During World War II the British Broadcasting Corporation's overseas service frequently included "personal messages" as part of its regular broadcast schedule. The seemingly nonsensical stream of messages read out by announcers were actually one time codes intended for Special Operations Executive (SOE) agents operating behind enemy lines. An example might be "The princess wears red shoes" or "Mimi's cat is asleep under the table". Each code message was read out twice. By such means, the French Resistance were instructed to start sabotaging rail and other transport links the night before D-day. "Over all of Spain, the sky is clear" was a signal (broadcast on radio) to start the nationalist military revolt in Spain on July 17, 1936. Sometimes messages are not prearranged and rely on shared knowledge hopefully known only to the recipients. An example is the telegram sent to U.S. President Harry Truman, then at the Potsdam Conference to meet with Soviet premier Joseph Stalin, informing Truman of the first successful test of an atomic bomb. "Operated on this morning. Diagnosis not yet complete but results seem satisfactory and already exceed expectations. Local press release necessary as interest extends great distance. Dr. Groves pleased. He returns tomorrow. I will keep you posted." == Idiot code == An idiot code is a code that is created by the parties using it. This type of communication is akin to the hand signals used by armies in the field. Example: Any sentence where 'day' and 'night' are used means 'attack'. The location mentioned in the following sentence specifies the location to be attacked. Plaintext: Attack X. Codetext: We walked day and night through the streets but couldn't find it! Tomorrow we'll head into X. An early use of the term appears to be by George Perrault, a character in the science fiction book Friday by Robert A. Heinlein: The simplest sort [of code] and thereby impossible to break. The first ad told the person or persons concerned to carry out number seven or expect number seven or it said something about something designated as seven. This one says the same with respect to code item number ten. But the meaning of the numbers cannot be deduced through statistical analysis because the code can be changed long before a useful statistical universe can be reached. It's an idiot code... and an idiot code can never be broken if the user has the good sense not to go too often to the well. Terrorism expert Magnus Ranstorp said that the men who carried out the September 11 attacks on the United States used basic e-mail and what he calls "idiot code" to discuss their plans. == Cryptanalysis of codes == While solving a monoalphabetic substitution cipher is easy, solving even a simple code is difficult. Decrypting a coded message is a little like trying to translate a document written in a foreign language, with the task basically amounting to building up a "dictionary" of the codegroups and the plaintext words they represent. One fingerhold on a simple code is the fact that some words are more common than others, such as "the" or "a" in English. In telegraphic messages, the codegroup for "STOP" (i.e., end of sentence or paragraph) is usually very common. This helps define the structure of the message in terms of sentences, if not their meaning, and this is cryptanalytically useful. Further progress can be made against a code by collecting many codetexts encrypted with the same code and then using information from other sources spies newspapers diplomatic cocktail party chat the location from where a message was sent where it was being sent to (i.e., traffic analysis) the time the message was sent, events occurring before and after the message was sent the normal habits of the people sending the coded messages etc. For example, a particular codegroup found almost exclusively in messages from a particular army and nowhere else might very well indicate the commander of that army. A codegroup that appears in messages preceding an attack on a particular location may very well stand for that location. Cribs can be an immediate giveaway to the definiti

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  Undeniable signature

  An undeniable signature is a digital signature scheme which allows the signer to be selective to whom they allow to verify signatures. The scheme adds explicit signature repudiation, preventing a signer later refusing to verify a signature by omission; a situation that would devalue the signature in the eyes of the verifier. It was invented by David Chaum and Hans van Antwerpen in 1989. == Overview == In this scheme, a signer possessing a private key can publish a signature of a message. However, the signature reveals nothing to a recipient/verifier of the message and signature without taking part in either of two interactive protocols: Confirmation protocol, which confirms that a candidate is a valid signature of the message issued by the signer, identified by the public key. Disavowal protocol, which confirms that a candidate is not a valid signature of the message issued by the signer. The motivation for the scheme is to allow the signer to choose to whom signatures are verified. However, that the signer might claim the signature is invalid at any later point, by refusing to take part in verification, would devalue signatures to verifiers. The disavowal protocol distinguishes these cases removing the signer's plausible deniability. It is important that the confirmation and disavowal exchanges are not transferable. They achieve this by having the property of zero-knowledge; both parties can create transcripts of both confirmation and disavowal that are indistinguishable, to a third-party, of correct exchanges. The designated verifier signature scheme improves upon deniable signatures by allowing, for each signature, the interactive portion of the scheme to be offloaded onto another party, a designated verifier, reducing the burden on the signer. == Zero-knowledge protocol == The following protocol was suggested by David Chaum. A group, G, is chosen in which the discrete logarithm problem is intractable, and all operation in the scheme take place in this group. Commonly, this will be the finite cyclic group of order p contained in Z/nZ, with p being a large prime number; this group is equipped with the group operation of integer multiplication modulo n. An arbitrary primitive element (or generator), g, of G is chosen; computed powers of g then combine obeying fixed axioms. Alice generates a key pair, randomly chooses a private key, x, and then derives and publishes the public key, y = gx. === Message signing === Alice signs the message, m, by computing and publishing the signature, z = mx. === Confirmation (i.e., avowal) protocol === Bob wishes to verify the signature, z, of m by Alice under the key, y. Bob picks two random numbers: a and b, and uses them to blind the message, sending to Alice: c = magb. Alice picks a random number, q, uses it to blind, c, and then signing this using her private key, x, sending to Bob: s1 = cgq ands2 = s1x. Note that s1x = (cgq)x = (magb)xgqx = (mx)a(gx)b+q = zayb+q. Bob reveals a and b. Alice verifies that a and b are the correct blind values, then, if so, reveals q. Revealing these blinds makes the exchange zero knowledge. Bob verifies s1 = cgq, proving q has not been chosen dishonestly, and s2 = zayb+q, proving z is valid signature issued by Alice's key. Note that zayb+q = (mx)a(gx)b+q. Alice can cheat at step 2 by attempting to randomly guess s2. === Disavowal protocol === Alice wishes to convince Bob that z is not a valid signature of m under the key, gx; i.e., z ≠ mx. Alice and Bob have agreed an integer, k, which sets the computational burden on Alice and the likelihood that she should succeed by chance. Bob picks random values, s ∈ {0, 1, ..., k} and a, and sends: v1 = msga and v2 = zsya, where exponentiating by a is used to blind the sent values. Note that v2 = zsya = (mx)s(gx)a = v1x. Alice, using her private key, computes v1x and then the quotient, v1xv2−1 = (msga)x(zsgxa)−1 = msxz−s = (mxz−1)s. Thus, v1xv2−1 = 1, unless z ≠ mx. Alice then tests v1xv2−1 for equality against the values: (mxz−1)i for i ∈ {0, 1, …, k}; which are calculated by repeated multiplication of mxz−1 (rather than exponentiating for each i). If the test succeeds, Alice conjectures the relevant i to be s; otherwise, she conjectures random value. Where z = mx, (mxz−1)i = v1xv2−1 = 1 for all i, s is unrecoverable. Alice commits to i: she picks a random r and sends hash(r, i) to Bob. Bob reveals a. Alice confirms that a is the correct blind (i.e., v1 and v2 can be generated using it), then, if so, reveals r. Revealing these blinds makes the exchange zero knowledge. Bob checks hash(r, i) = hash(r, s), proving Alice knows s, hence z ≠ mx. If Alice attempts to cheat at step 3 by guessing s at random, the probability of succeeding is 1/(k + 1). So, if k = 1023 and the protocol is conducted ten times, her chances are 1 to 2100.

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  Cover-coding

  Cover-coding is a technique for obscuring the data that is transmitted over an insecure link, to reduce the risks of snooping. An example of cover-coding would be for the sender to perform a bitwise XOR (exclusive OR) of the original data with a password or random number which is known to both sender and receiver. The resulting cover-coded data is then transmitted from sender to the receiver, who uncovers the original data by performing a further bitwise XOR (exclusive OR) operation on the received data using the same password or random number. ISO 18000-6C (EPC Class 1 Generation 2) RFID tags protect some operations with a cover code. The reader requests a random number from the tag, and the tag responds with a new random number. The reader then encrypts future communications with this number, using bitwise XOR, to the data it sends. Cover coding is secure if the tag signal can't be intercepted and the random number is not re-used. Compared to the loud transmissions from the reader, tag backscatter is much weaker and difficult -- but not impossible -- to intercept.

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  Human visual system model

  A human visual system model (HVS model) is used by image processing, video processing and computer vision experts to deal with biological and psychological processes that are not yet fully understood. Such a model is used to simplify the behaviors of what is a very complex system. As our knowledge of the true visual system improves, the model is updated. Psychovisual study is the study of the psychology of vision. The human visual system model can produce desired effects in perception and vision. Examples of using an HVS model include color television, lossy compression, and Cathode-ray tube (CRT) television. Originally, it was thought that color television required too high a bandwidth for the then available technology. Then it was noticed that the color resolution of the HVS was much lower than the brightness resolution; this allowed color to be squeezed into the signal by chroma subsampling. Another example is lossy image compression, like JPEG. Our HVS model says we cannot see high frequency detail, so in JPEG we can quantize these components without a perceptible loss of quality. Similar concepts are applied in audio compression, where sound frequencies inaudible to humans are band-stop filtered. Several HVS features are derived from evolution when we needed to defend ourselves or hunt for food. We often see demonstrations of HVS features when we are looking at optical illusions. == Block diagram of HVS == == Assumptions about the HVS == Low-pass filter characteristic (limited number of rods in human eye): see Mach bands Lack of color resolution (fewer cones in human eye than rods) Motion sensitivity More sensitive in peripheral vision Stronger than texture sensitivity, e.g. viewing a camouflaged animal Texture stronger than disparity – 3D depth resolution does not need to be so accurate Integral Face recognition (babies smile at faces) Depth inverted face looks normal (facial features overrule depth information) Upside down face with inverted mouth and eyes looks normal == Examples of taking advantage of an HVS model == Flicker frequency of film and television using persistence of vision to fool viewer into seeing a continuous image Interlaced television painting half images to give the impression of a higher flicker frequency Color television (chrominance at half resolution of luminance corresponding to proportions of rods and cones in eye) Image compression (difficult to see higher frequencies more harshly quantized) Motion estimation (use luminance and ignore color) Watermarking and Steganography

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  Information Networking Institute

  Information Networking Institute (INI) is an academic department within the College of Engineering at Carnegie Mellon University. The institute was established in 1989 as the nation's first research and education center devoted to information networking. The INI also partners with research and outreach entities to extend educational and training programs to a broad audience of people using information networking as part of their daily lives. The INI is the educational partner of Carnegie Mellon CyLab, a university-wide, multidisciplinary research center involving more than 50 faculty and 100 graduate students. == Center of Academic Excellence Designations == Through the work of the INI and CyLab, Carnegie Mellon University has been designated by the National Security Agency and the Department of Homeland Security as a National Center of Academic Excellence in Information Assurance/Cyber Defense Education (CAE-IA/CD) and a National Center of Academic Excellence in Information Assurance/Cyber Defense Research (CAE-R). It has also been designated by the NSA and the U.S. Cyber Command as a National Center of Academic Excellence in Cyber Operations (CAE-Cyber Ops). Through these designations, the INI and CyLab participate in the: Federal CyberCorps Scholarship for Service (SFS) Program - Students pursuing graduate degrees in information security (MSIS or MSISPM) are eligible for scholarships under the SFS program. Information Assurance Scholarship Program (IASP) - Students pursuing graduate degrees in information security and seeking careers with the Department of Defense may be eligible for scholarships under the IASP. Capacity Building Program for Faculty from Historically Black and Hispanic Serving Institutions - The INI and CyLab developed a month-long, in-residence summer program to help build information assurance education and research capacity at colleges and universities designated as Minority Serving Institutions – specifically, Historically Black Colleges and Universities (HBCUs) and Hispanic Serving Institutions (HSIs). This program is supported through a grant from the National Science Foundation. == Faculty and researchers == Faculty involved in teaching and advising in the INI programs are conducting research in all aspects of information networking and information security. Affiliated research centers are: Carnegie Mellon CyLab SEI's CERT Division == Alumni == The INI has graduated over 1,400 alumni who currently occupy positions in a variety of sectors across industry, government and academia.

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  Dynamic knowledge repository

  The dynamic knowledge repository (DKR) is a concept developed by Douglas C. Engelbart as a primary strategic focus for allowing humans to address complex problems. He has proposed that a DKR will enable us to develop a collective IQ greater than any individual's IQ. References and discussion of Engelbart's DKR concept are available at the Doug Engelbart Institute. == Definition == A knowledge repository is a computerized system that systematically captures, organizes and categorizes an organization's knowledge. The repository can be searched and data can be quickly retrieved. The effective knowledge repositories include factual, conceptual, procedural and meta-cognitive techniques. The key features of knowledge repositories include communication forums. A knowledge repository can take many forms to "contain" the knowledge it holds. A customer database is a knowledge repository of customer information and insights – or electronic explicit knowledge. A Library is a knowledge repository of books – physical explicit knowledge. A community of experts is a knowledge repository of tacit knowledge or experience. The nature of the repository only changes to contain/manage the type of knowledge it holds. A repository (as opposed to an archive) is designed to get knowledge out. It should therefore have some rules of structure, classification, taxonomy, record management, etc., to facilitate user engagement.

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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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  Jais (language model)

  Jais is an open-source large language model launched in August 2023. Developed as a collaboration between Emirati AI company G42, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), and US-based Cerebras Systems, Jais was designed to produce high-quality Arabic text and was also trained on English data. The model's creation was motivated by the underrepresentation of the Arabic language in the field of generative artificial intelligence. It aims to provide a more culturally and linguistically accurate model for the world's 400 million Arabic speakers. Its name is a reference to Jebel Jais, the highest mountain in the UAE. == Background and development == Jais was developed in response to the limited availability of advanced generative artificial intelligence models for the Arabic language, despite it being spoken by over 400 million people. Existing models were often trained on limited or low-quality Arabic web content, resulting in poor performance. The project represents a significant investment by the United Arab Emirates in the field of AI as part of its national strategy. The model was created through a partnership between Inception (now Core42), a subsidiary of the Abu Dhabi-based AI company G42; the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI); and Cerebras Systems, a US company specializing in AI hardware. The model is named after Jebel Jais, the highest peak in the UAE. == Training == The initial version of Jais released in August 2023 had 13 billion parameters. In November 2023, Core42 released Jais 30B, an improved version with 30 billion parameters. Both models were trained on a subset of the Cerebras Condor Galaxy 1 supercomputer. The training dataset consisted of a mix of Arabic, English, and computer code. According to Timothy Baldwin, a professor of natural language processing at MBZUAI, training the model on a diverse Arabic dataset allows it to switch between dialects. == Features == Jais is designed to generate text in both English and Arabic. The project has also released instruction-tuned "Chat" variants for both the 13B and 30B models, which are specifically optimized for conversational applications. Additional functionality for working with images, graphs, and tabular data is planned for future releases.

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  Cypherpunks (book)

  Cypherpunks: Freedom and the Future of the Internet is a 2012 book by Julian Assange, in discussion with Internet activists and cypherpunks Jacob Appelbaum, Andy Müller-Maguhn and Jérémie Zimmermann. Its primary topic is society's relationship with information security. In the book, the authors warn that the Internet has become a tool of the police state, and that the world is inadvertently heading toward a form of totalitarianism. They promote the use of cryptography to protect against state surveillance. In the introduction, Assange says that the book is "not a manifesto [...] [but] a warning". He told Guardian journalist Decca Aitkenhead: A well-defined mathematical algorithm can encrypt something quickly, but to decrypt it would take billions of years – or trillions of dollars' worth of electricity to drive the computer. So cryptography is the essential building block of independence for organisations on the Internet, just like armies are the essential building blocks of states, because otherwise one state just takes over another. There is no other way for our intellectual life to gain proper independence from the security guards of the world, the people who control physical reality. Assange later wrote in The Guardian: "Strong cryptography is a vital tool in fighting state oppression." saying that was the message of his book, Cypherpunks. Cypherpunks is published by OR Books. It is primarily a transcript of World Tomorrow episode eight, a two-part interview between Assange, Jacob Appelbaum, Andy Müller-Maguhn, and Jérémie Zimmermann. In the foreword, Assange said, "the Internet, our greatest tool for emancipation, has been transformed into the most dangerous facilitator of totalitarianism we have ever seen".

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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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  Data integration

  Data integration is the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories). The decision to integrate data tends to arise when the volume, complexity (that is, big data) and need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. The data being integrated must be received from a heterogeneous database system and transformed to a single coherent data store that provides synchronous data across a network of files for clients. A common use of data integration is in data mining when analyzing and extracting information from existing databases that can be useful for Business information. == History == Issues with combining heterogeneous data sources, often referred to as information silos, under a single query interface have existed for some time. In the early 1980s, computer scientists began designing systems for interoperability of heterogeneous databases. The first data integration system driven by structured metadata was designed in 1991 at the University of Minnesota for the Integrated Public Use Microdata Series (IPUMS). IPUMS used a data warehousing approach, which extracts, transforms, and loads data from heterogeneous sources into a unique view schema so data from different sources become compatible. By making thousands of population databases interoperable, IPUMS demonstrated the feasibility of large-scale data integration. The data warehouse approach offers a tightly coupled architecture because the data are already physically reconciled in a single queryable repository, so it usually takes little time to resolve queries. The data warehouse approach is less feasible for data sets that are frequently updated, requiring the extract, transform, load (ETL) process to be continuously re-executed for synchronization. Difficulties also arise in constructing data warehouses when one has only a query interface to summary data sources and no access to the full data. This problem frequently emerges when integrating several commercial query services like travel or classified advertisement web applications. A trend began in 2009 favoring the loose coupling of data and providing a unified query-interface to access real time data over a mediated schema (see Figure 2), which allows information to be retrieved directly from original databases. This is consistent with the SOA approach popular in that era. This approach relies on mappings between the mediated schema and the schema of original sources, and translating a query into decomposed queries to match the schema of the original databases. Such mappings can be specified in two ways: as a mapping from entities in the mediated schema to entities in the original sources (the "Global-as-View" (GAV) approach), or as a mapping from entities in the original sources to the mediated schema (the "Local-as-View" (LAV) approach). The latter approach requires more sophisticated inferences to resolve a query on the mediated schema, but makes it easier to add new data sources to a (stable) mediated schema. As of 2010, some of the work in data integration research concerns the semantic integration problem. This problem addresses not the structuring of the architecture of the integration, but how to resolve semantic conflicts between heterogeneous data sources. For example, if two companies merge their databases, certain concepts and definitions in their respective schemas like "earnings" inevitably have different meanings. In one database it may mean profits in dollars (a floating-point number), while in the other it might represent the number of sales (an integer). A common strategy for the resolution of such problems involves the use of ontologies which explicitly define schema terms and thus help to resolve semantic conflicts. This approach represents ontology-based data integration. On the other hand, the problem of combining research results from different bioinformatics repositories requires bench-marking of the similarities, computed from different data sources, on a single criterion such as positive predictive value. This enables the data sources to be directly comparable and can be integrated even when the natures of experiments are distinct. As of 2011, it was determined that current data modeling methods were imparting data isolation into every data architecture in the form of islands of disparate data and information silos. This data isolation is an unintended artifact of the data modeling methodology that results in the development of disparate data models. Disparate data models, when instantiated as databases, form disparate databases. Enhanced data model methodologies have been developed to eliminate the data isolation artifact and to promote the development of integrated data models. One enhanced data modeling method recasts data models by augmenting them with structural metadata in the form of standardized data entities. As a result of recasting multiple data models, the set of recast data models will now share one or more commonality relationships that relate the structural metadata now common to these data models. Commonality relationships are a peer-to-peer type of entity relationships that relate the standardized data entities of multiple data models. Multiple data models that contain the same standard data entity may participate in the same commonality relationship. When integrated data models are instantiated as databases and are properly populated from a common set of master data, then these databases are integrated. Since 2011, data hub approaches have been of greater interest than fully structured (typically relational) Enterprise Data Warehouses. Since 2013, data lake approaches have risen to the level of Data Hubs. (See all three search terms popularity on Google Trends.) These approaches combine unstructured or varied data into one location, but do not necessarily require an (often complex) master relational schema to structure and define all data in the Hub. In recent times, as the number of applications being used have increased many fold and application to application integration have become critical and this has given rise to [Unified APIs] that help application developers integrate their apps with other apps and more recently with [MCP - Model Context Protocol] taking it a step further for AI Agents. Data integration plays a big role in business regarding data collection used for studying the market. Converting the raw data retrieved from consumers into coherent data is something businesses try to do when considering what steps they should take next. Organizations are more frequently using data mining for collecting information and patterns from their databases, and this process helps them develop new business strategies to increase business performance and perform economic analyses more efficiently. Compiling the large amount of data they collect to be stored in their system is a form of data integration adapted for Business intelligence to improve their chances of success. == Example == Consider a web application where a user can query a variety of information about cities (such as crime statistics, weather, hotels, demographics, etc.). Traditionally, the information must be stored in a single database with a single schema. But any single enterprise would find information of this breadth somewhat difficult and expensive to collect. Even if the resources exist to gather the data, it would likely duplicate data in existing crime databases, weather websites, and census data. A data-integration solution may address this problem by considering these external resources as materialized views over a virtual mediated schema, resulting in "virtual data integration". This means application-developers construct a virtual schema—the mediated schema—to best model the kinds of answers their users want. Next, they design "wrappers" or adapters for each data source, such as the crime database and weather website. These adapters simply transform the local query results (those returned by the respective websites or databases) into an easily processed form for the data integration solution (see figure 2). When an application-user queries the mediated schema, the data-integration solution transforms this query into appropriate queries over the respective data sources. Finally, the virtual database combines the results of these queries into the answer to the user's query. This solution offers the convenience of adding new sources by simply constructing an adapter or an application software blade for them. It contrasts with ETL systems or with a si

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  Live Transcribe

  Live Transcribe is a mobile app for real-time captioning, developed by Google for the Android operating system. Development on the application began in partnership with Gallaudet University. It was publicly released as a free beta for Android 5.0+ on the Google Play Store on February 4, 2019. As of early 2023 it had been downloaded over 500 million times. == Development == Researchers Dimitri Kanevsky, Sagar Savla and Chet Gnegy at Google developed the app in collaboration with researchers at Gallaudet University, an American university for the education of the deaf and hard of hearing. The app uses machine learning to generate captions, similar to YouTube's auto-generated captions. In August 2019, Google made Live Transcribe an open-source project. == Features == The app uses speech recognition to generate live captions in over 80 languages with varying accuracy. The app, which requires connection to the Internet to function, is available to download on the Google Play Store. A later update to the app displayed information on sounds such as clapping, laughter, music, applause, and whistling. In May 2020, the app started supporting transcription in Albanian, Burmese, Estonian, Macedonian, Mongolian, Punjabi, and Uzbek, supporting 70 languages. In March 2022, the app was updated with support to transcribe offline, without Internet connection, so long as the appropriate language pack has been installed. The offline mode is only available for devices with 6GB of RAM and certain Google Pixel devices.

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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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  Contrast set learning

  Contrast set learning is a form of association rule learning that seeks to identify meaningful differences between separate groups by reverse-engineering the key predictors that identify for each particular group. For example, given a set of attributes for a pool of students (labeled by degree type), a contrast set learner would identify the contrasting features between students seeking bachelor's degrees and those working toward PhD degrees. == Overview == A common practice in data mining is to classify, to look at the attributes of an object or situation and make a guess at what category the observed item belongs to. As new evidence is examined (typically by feeding a training set to a learning algorithm), these guesses are refined and improved. Contrast set learning works in the opposite direction. While classifiers read a collection of data and collect information that is used to place new data into a series of discrete categories, contrast set learning takes the category that an item belongs to and attempts to reverse engineer the statistical evidence that identifies an item as a member of a class. That is, contrast set learners seek rules associating attribute values with changes to the class distribution. They seek to identify the key predictors that contrast one classification from another. For example, an aerospace engineer might record data on test launches of a new rocket. Measurements would be taken at regular intervals throughout the launch, noting factors such as the trajectory of the rocket, operating temperatures, external pressures, and so on. If the rocket launch fails after a number of successful tests, the engineer could use contrast set learning to distinguish between the successful and failed tests. A contrast set learner will produce a set of association rules that, when applied, will indicate the key predictors of each failed tests versus the successful ones (the temperature was too high, the wind pressure was too high, etc.). Contrast set learning is a form of association rule learning. Association rule learners typically offer rules linking attributes commonly occurring together in a training set (for instance, people who are enrolled in four-year programs and take a full course load tend to also live near campus). Instead of finding rules that describe the current situation, contrast set learners seek rules that differ meaningfully in their distribution across groups (and thus, can be used as predictors for those groups). For example, a contrast set learner could ask, “What are the key identifiers of a person with a bachelor's degree or a person with a PhD, and how do people with PhD's and bachelor’s degrees differ?” Standard classifier algorithms, such as C4.5, have no concept of class importance (that is, they do not know if a class is "good" or "bad"). Such learners cannot bias or filter their predictions towards certain desired classes. As the goal of contrast set learning is to discover meaningful differences between groups, it is useful to be able to target the learned rules towards certain classifications. Several contrast set learners, such as MINWAL or the family of TAR algorithms, assign weights to each class in order to focus the learned theories toward outcomes that are of interest to a particular audience. Thus, contrast set learning can be thought of as a form of weighted class learning. === Example: Supermarket Purchases === The differences between standard classification, association rule learning, and contrast set learning can be illustrated with a simple supermarket metaphor. In the following small dataset, each row is a supermarket transaction and each "1" indicates that the item was purchased (a "0" indicates that the item was not purchased): Given this data, Association rule learning may discover that customers that buy onions and potatoes together are likely to also purchase hamburger meat. Classification may discover that customers that bought onions, potatoes, and hamburger meats were purchasing items for a cookout. Contrast set learning may discover that the major difference between customers shopping for a cookout and those shopping for an anniversary dinner are that customers acquiring items for a cookout purchase onions, potatoes, and hamburger meat (and do not purchase foie gras or champagne). == Treatment learning == Treatment learning is a form of weighted contrast-set learning that takes a single desirable group and contrasts it against the remaining undesirable groups (the level of desirability is represented by weighted classes). The resulting "treatment" suggests a set of rules that, when applied, will lead to the desired outcome. Treatment learning differs from standard contrast set learning through the following constraints: Rather than seeking the differences between all groups, treatment learning specifies a particular group to focus on, applies a weight to this desired grouping, and lumps the remaining groups into one "undesired" category. Treatment learning has a stated focus on minimal theories. In practice, treatment are limited to a maximum of four constraints (i.e., rather than stating all of the reasons that a rocket differs from a skateboard, a treatment learner will state one to four major differences that predict for rockets at a high level of statistical significance). This focus on simplicity is an important goal for treatment learners. Treatment learning seeks the smallest change that has the greatest impact on the class distribution. Conceptually, treatment learners explore all possible subsets of the range of values for all attributes. Such a search is often infeasible in practice, so treatment learning often focuses instead on quickly pruning and ignoring attribute ranges that, when applied, lead to a class distribution where the desired class is in the minority. === Example: Boston housing data === The following example demonstrates the output of the treatment learner TAR3 on a dataset of housing data from the city of Boston (a nontrivial public dataset with over 500 examples). In this dataset, a number of factors are collected for each house, and each house is classified according to its quality (low, medium-low, medium-high, and high). The desired class is set to "high", and all other classes are lumped together as undesirable. The output of the treatment learner is as follows: Baseline class distribution: low: 29% medlow: 29% medhigh: 21% high: 21% Suggested Treatment: [PTRATIO=[12.6..16), RM=[6.7..9.78)] New class distribution: low: 0% medlow: 0% medhigh: 3% high: 97% With no applied treatments (rules), the desired class represents only 21% of the class distribution. However, if one filters the data set for houses with 6.7 to 9.78 rooms and a neighborhood parent-teacher ratio of 12.6 to 16, then 97% of the remaining examples fall into the desired class (high-quality houses). == Algorithms == There are a number of algorithms that perform contrast set learning. The following subsections describe two examples. === STUCCO === The STUCCO contrast set learner treats the task of learning from contrast sets as a tree search problem where the root node of the tree is an empty contrast set. Children are added by specializing the set with additional items picked through a canonical ordering of attributes (to avoid visiting the same nodes twice). Children are formed by appending terms that follow all existing terms in a given ordering. The formed tree is searched in a breadth-first manner. Given the nodes at each level, the dataset is scanned and the support is counted for each group. Each node is then examined to determine if it is significant and large, if it should be pruned, and if new children should be generated. After all significant contrast sets are located, a post-processor selects a subset to show to the user - the low order, simpler results are shown first, followed by the higher order results which are "surprising and significantly different." The support calculation comes from testing a null hypothesis that the contrast set support is equal across all groups (i.e., that contrast set support is independent of group membership). The support count for each group is a frequency value that can be analyzed in a contingency table where each row represents the truth value of the contrast set and each column variable indicates the group membership frequency. If there is a difference in proportions between the contrast set frequencies and those of the null hypothesis, the algorithm must then determine if the differences in proportions represent a relation between variables or if it can be attributed to random causes. This can be determined through a chi-square test comparing the observed frequency count to the expected count. Nodes are pruned from the tree when all specializations of the node can never lead to a significant and large contrast set. The decision to prune is based on: The minimum deviation size: The maximum difference between the support

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