AI Code Vulnerability Scanner

AI Code Vulnerability Scanner — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Photoanalysis

  Photoanalysis (or photo analysis) refers to the study of pictures to compile various types of data, for example, to measure the size distribution of virtually anything that can be captured by photo. Photoanalysis technology has changed the way mines and mills quantify fragmented material. Images are an effective way to document conditions before, after, and even during blasting activities. The technology is advancing at a high rate, and lenses, storage media memory, light sensitivity and resolution have been improving steadily. Today's digital cameras and camcorders include high-resolution optics, compact size, automatic time and date stamps, good battery life, shutters to freeze motion, and computers to autofocus and eliminate jitter using image stabilization. == Mining == Photoanalysis in mining operations can provide an automated system that forewarns a company of potential problems with materials, leading to economies and reduced damage caused from over-sized materials. It can also help determine the effectiveness of blasts. A company can use this technology to monitor materials moving on a conveyor belt in an underground environment, to measure piles left over from a blast, and even measure the amount of material being carried by dump trucks or vessels to a destination. Photoanalysis is being used on SAG mills worldwide to control the size of rock being crushed. Companies are using this technology to determine the size of particles being processed in the SAG Mill.[1] Archived 2009-05-23 at the Wayback Machine Having oversize material entering the SAG mill makes an operation less efficient, costing companies money in electrical and maintenance costs. Photoanalysis technology can eliminate unwanted material before it enters the mill, keeping rock crushing costs low. == Forestry == Wood chip size can affect the overall quality of a product. With automated photoanalysis systems, companies can remove any unwanted wrong-size particles without stopping their mill process. Photoanalysis can affect how efficiently forestry companies operate. In mills worldwide, photoanalysis technology is improving the use of lumber products, cutting back on the amount of trees being used to operate, and saving companies money through quality control optimization.[2] With the current downturn in the North American forestry industry, operators are looking at making their mills more efficient and effective when processing materials. Photoanalysis technology helps identify any weaknesses in the process by continuously monitoring different sections of an operation. == Agriculture == Agricultural companies can, using photoanalysis, monitor conveyor belts of food without contaminating the product by touching it. Other benefits of photoanalysis systems include: Automated removal of any unwanted material on food conveyor Improved quality control for the most important parts of the agricultural process Pinpoint accuracy that helps the efficiency and effectiveness of product handling techniques The importance of photoanalysis technology is being noticed by the agricultural industry as it identifies any unwanted materials going through the process. In an example, if a mouse is on a conveyor of corn, photoanalysis technology would be able to identify the unwanted object and remove it before it contaminates the whole process. == Origins of photoanalysis technology == Photoanalysis technology was created by using the Waterloo Image Enhancement Process in the 1980s. After further development of the imaging process with explosives producer DuPont, engineers Tom Palangio and Takis Katsabanis began selling photoanalysis software commercially. They later renamed the process WipFrag, standing for Waterloo Image Process Fragmentation Today, photoanalysis technology has evolved into stabilized and portable systems that can automatically capture and analyze results instantly. Thousands of these products are currently being used around the world to measure fragmented material. == Photoanalysis equipment photos == == Fragmentation analysis == Fragmentation analysis is becoming a popular term in mining, agricultural and forestry industries. With the majority of money in these industries directed towards the proper sizing of materials, companies are using fragmentation analysis to determine various factors within an operation.[3] The two main ways a company keeps track of fragmented material are through manual and automated sieving procedures. Manual sieving involves extracting a sample of material to analyze the size distribution. The results can be tabulated within two days. Automated sieving is an advanced way of sieving materials running through a process. Without having to extract the material, photoanalysis can take place, allowing for immediate results with pinpoint accuracy. == Blast Fragmentation Software == Operators are using fragmentation analysis to determine the effectiveness of various blasts. With automated sieving technology, workers can track the success of these blasts and receive instant results. Companies are using these results to determine what blasting method yielded the best results for their specific operation. The common variables associated with blast optimization are the provided Particle Size Distribution (PSD) from a shovel fragmentation system, geology including rock type and fracturing, and energy factor. By using photoanalysis the fragmented materials can be monitored, offering pinpoint accuracy and allowing mine operators to make adjustments to future blasting procedures. See Optical Granulometry to view the automated sieving process. == Pre-crushing analysis == Maintenance costs can be significantly reduced if an operation focuses on the fragmentation of the particles passing through their process. Automated sieving systems can detect and help remove any oversize material before it enters the crusher and causes maintenance problems. It also helps determine the effectiveness of the mining process prior to crushing; the sizing of material is always a critical part of operations in the mining, forestry and agricultural industries. Having an analysis taking place at every major point in an operation allows for the proper tracking of material being processed. Engineers can then determine what part of the process needs improving based solely on the size of material. == Post-crushing analysis == Measuring how effective industrial crushers are, can help save a company millions of dollars in energy costs on an annual basis. There are two components that affect a typical crusher: the size of the material inputted, and the speed at which the crusher is moving. If the user can find a perfect balance between these two components, the materials will be crushed to the right size in the shortest time possible. Meeting the material standards set by governments and large companies can be hard. Having a post-crushing analysis taking place ensures that no oversize material gets shipped; eliminating the chance of getting fined for not meeting industry specifications.

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  Corpus linguistics

  Corpus linguistics is an empirical method for the study of language by text corpus (plural corpora). Corpora are balanced, often stratified collections of authentic, "real world", text of speech or writing that aim to represent a given linguistic variety. Today, corpora are generally machine-readable data collections. Corpus linguistics proposes that a reliable analysis of a language is more feasible with corpora collected in the field—the natural context ("realia") of that language—with minimal experimental interference. Large collections of text, though corpora may also be small in terms of running words, allow linguists to run quantitative analyses on linguistic concepts that may be difficult to test in a qualitative manner. The text-corpus method uses the body of texts in any natural language to derive the set of abstract rules which govern that language. Those results can be used to explore the relationships between that subject language and other languages which have undergone a similar analysis. The first such corpora were manually derived from source texts, but now that work is automated. Corpora have not only been used for linguistics research, they have been increasingly used to compile dictionaries (starting with The American Heritage Dictionary of the English Language in 1969) and reference grammars, with A Comprehensive Grammar of the English Language, published in 1985, as a first. Experts in the field have differing views about the annotation of a corpus. These views range from John McHardy Sinclair, who advocates minimal annotation so texts speak for themselves, to the Survey of English Usage team (University College, London), who advocate annotation as allowing greater linguistic understanding through rigorous recording. == History == Some of the earliest efforts at grammatical description were based at least in part on corpora of particular religious or cultural significance. For example, Prātiśākhya literature described the sound patterns of Sanskrit as found in the Vedas, and Pāṇini's grammar of classical Sanskrit was based at least in part on analysis of that same corpus. Similarly, the early Arabic grammarians paid particular attention to the language of the Quran. In the Western European tradition, scholars prepared concordances to allow detailed study of the language of the Bible and other canonical texts. === English corpora === A landmark in modern corpus linguistics was the publication of Computational Analysis of Present-Day American English in 1967. Written by Henry Kučera and W. Nelson Francis, the work was based on an analysis of the Brown Corpus, which is a structured and balanced corpus of one million words of American English from the year 1961. The corpus comprises 2000 text samples, from a variety of genres. The Brown Corpus was the first computerized corpus designed for linguistic research. Kučera and Francis subjected the Brown Corpus to a variety of computational analyses and then combined elements of linguistics, language teaching, psychology, statistics, and sociology to create a rich and variegated opus. A further key publication was Randolph Quirk's "Towards a description of English Usage" in 1960 in which he introduced the Survey of English Usage. Quirk's corpus was the first modern corpus to be built with the purpose of representing the whole language. Shortly thereafter, Boston publisher Houghton-Mifflin approached Kučera to supply a million-word, three-line citation base for its new American Heritage Dictionary, the first dictionary compiled using corpus linguistics. The AHD took the innovative step of combining prescriptive elements (how language should be used) with descriptive information (how it actually is used). Other publishers followed suit. The British publisher Collins' COBUILD monolingual learner's dictionary, designed for users learning English as a foreign language, was compiled using the Bank of English. The Survey of English Usage Corpus was used in the development of one of the most important Corpus-based Grammars, which was written by Quirk et al. and published in 1985 as A Comprehensive Grammar of the English Language. The Brown Corpus has also spawned a number of similarly structured corpora: the LOB Corpus (1960s British English), Kolhapur (Indian English), Wellington (New Zealand English), Australian Corpus of English (Australian English), the Frown Corpus (early 1990s American English), and the FLOB Corpus (1990s British English). Other corpora represent many languages, varieties and modes, and include the International Corpus of English, and the British National Corpus, a 100 million word collection of a range of spoken and written texts, created in the 1990s by a consortium of publishers, universities (Oxford and Lancaster) and the British Library. For contemporary American English, work has stalled on the American National Corpus, but the 400+ million word Corpus of Contemporary American English (1990–present) is now available through a web interface. The first computerized corpus of transcribed spoken language was constructed in 1971 by the Montreal French Project, containing one million words, which inspired Shana Poplack's much larger corpus of spoken French in the Ottawa-Hull area. === Multilingual corpora === In the 1990s, many of the notable early successes on statistical methods in natural-language programming (NLP) occurred in the field of machine translation, due especially to work at IBM Research. These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. There are corpora in non-European languages as well. For example, the National Institute for Japanese Language and Linguistics in Japan has built a number of corpora of spoken and written Japanese. Sign language corpora have also been created using video data. === Ancient languages corpora === Besides these corpora of living languages, computerized corpora have also been made of collections of texts in ancient languages. An example is the Andersen-Forbes database of the Hebrew Bible, developed since the 1970s, in which every clause is parsed using graphs representing up to seven levels of syntax, and every segment tagged with seven fields of information. The Quranic Arabic Corpus is an annotated corpus for the Classical Arabic language of the Quran. This is a recent project with multiple layers of annotation including morphological segmentation, part-of-speech tagging, and syntactic analysis using dependency grammar. The Digital Corpus of Sanskrit (DCS) is a "Sandhi-split corpus of Sanskrit texts with full morphological and lexical analysis... designed for text-historical research in Sanskrit linguistics and philology." === Corpora from specific fields === Besides pure linguistic inquiry, researchers had begun to apply corpus linguistics to other academic and professional fields, such as the emerging sub-discipline of Law and Corpus Linguistics, which seeks to understand legal texts using corpus data and tools. The DBLP Discovery Dataset concentrates on computer science, containing relevant computer science publications with sentient metadata such as author affiliations, citations, or study fields. A more focused dataset was introduced by NLP Scholar, a combination of papers of the ACL Anthology and Google Scholar metadata. Corpora can also aid in translation efforts or in teaching foreign languages. == Methods == Corpus linguistics has generated a number of research methods, which attempt to trace a path from data to theory. Wallis and Nelson (2001) first introduced what they called the 3A perspective: Annotation, Abstraction and Analysis. Annotation consists of the application of a scheme to texts. Annotations may include structural markup, part-of-speech tagging, parsing, and numerous other representations. Abstraction consists of the translation (mapping) of terms in the scheme to terms in a theoretically motivated model or dataset. Abstraction typically includes linguist-directed search but may include e.g., rule-learning for parsers. Analysis consists of statistically probing, manipulating and generalising from the dataset. Analysis might include statistical evaluations, optimisation of rule-bases or knowledge discovery methods. Most lexical corpora today are part-of-speech-tagged (POS-tagged). However even corpus linguists who work with 'unannotated plain text' inevitably apply some method to isolate salient terms. In such situations annotation and abstraction are combined in a lexical search. The advantage of publishing an annotated corpus is that other users can then perform experiments on the corpus (through corpus managers). Linguists with other interests and differing perspectives than the originators' can exploit this work. By sharing data

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  Tree transducer

  In theoretical computer science and formal language theory, a tree transducer (TT) is an abstract machine taking as input a tree, and generating output – generally other trees, but models producing words or other structures exist. Roughly speaking, tree transducers extend tree automata in the same way that word transducers extend word automata. Manipulating tree structures instead of words enable TT to model syntax-directed transformations of formal or natural languages. However, TT are not as well-behaved as their word counterparts in terms of algorithmic complexity, closure properties, etcetera. In particular, most of the main classes are not closed under composition. The main classes of tree transducers are: == Top-Down Tree Transducers (TOP) == A TOP T is a tuple (Q, Σ, Γ, I, δ) such that: Q is a finite set, the set of states; Σ is a finite ranked alphabet, called the input alphabet; Γ is a finite ranked alphabet, called the output alphabet; I is a subset of Q, the set of initial states; and δ is a set of rules of the form q ( f ( x 1 , … , x n ) ) → u {\displaystyle q(f(x_{1},\dots ,x_{n}))\to u} , where f is a symbol of Σ, n is the arity of f, q is a state, and u is a tree on Γ and Q × 1.. n {\displaystyle Q\times 1..n} , such pairs being nullary. === Examples of rules and intuitions on semantics === For instance, q ( f ( x 1 , … , x 3 ) ) → g ( a , q ′ ( x 1 ) , h ( q ″ ( x 3 ) ) ) {\displaystyle q(f(x_{1},\dots ,x_{3}))\to g(a,q'(x_{1}),h(q''(x_{3})))} is a rule – one customarily writes q ( x i ) {\displaystyle q(x_{i})} instead of the pair ( q , x i ) {\displaystyle (q,x_{i})} – and its intuitive semantics is that, under the action of q, a tree with f at the root and three children is transformed into g ( a , q ′ ( x 1 ) , h ( q ″ ( x 3 ) ) ) {\displaystyle g(a,q'(x_{1}),h(q''(x_{3})))} where, recursively, q ′ ( x 1 ) {\displaystyle q'(x_{1})} and q ″ ( x 3 ) {\displaystyle q''(x_{3})} are replaced, respectively, with the application of q ′ {\displaystyle q'} on the first child and with the application of q ″ {\displaystyle q''} on the third. === Semantics as term rewriting === The semantics of each state of the transducer T, and of T itself, is a binary relation between input trees (on Σ) and output trees (on Γ). A way of defining the semantics formally is to see δ {\displaystyle \delta } as a term rewriting system, provided that in the right-hand sides the calls are written in the form q ( x i ) {\displaystyle q(x_{i})} , where states q are unary symbols. Then the semantics [ [ q ] ] {\displaystyle [\![q]\!]} of a state q is given by [ [ q ] ] = { u ↦ v ∣ u is a tree on Σ , v is a tree on Γ , and q ( u ) → δ ∗ v } . {\displaystyle [\![q]\!]=\{u\mapsto v\mid u{\text{ is a tree on }}\Sigma ,\ v{\text{ is a tree on }}\Gamma {\text{, and }}q(u)\to _{\delta }^{}v\}.} The semantics of T is then defined as the union of the semantics of its initial states: [ [ T ] ] = ⋃ q ∈ I [ [ q ] ] . {\displaystyle [\![T]\!]=\bigcup _{q\in I}[\![q]\!].} === Determinism and domain === As with tree automata, a TOP is said to be deterministic (abbreviated DTOP) if no two rules of δ share the same left-hand side, and there is at most one initial state. In that case, the semantics of the DTOP is a partial function from input trees (on Σ) to output trees (on Γ), as are the semantics of each of the DTOP's states. The domain of a transducer is the domain of its semantics. Likewise, the image of a transducer is the image of its semantics. === Properties of DTOP === DTOP are not closed under union: this is already the case for deterministic word transducers. The domain of a DTOP is a regular tree language. Furthermore, the domain is recognisable by a deterministic top-down tree automaton (DTTA) of size at most exponential in that of the initial DTOP. That the domain is DTTA-recognizable is not surprising, considering that the left-hand sides of DTOP rules are the same as for DTTA. As for the reason for the exponential explosion in the worst case (that does not exist in the word case), consider the rule q ( f ( x 1 , x 2 ) ) → g ( p 1 ( x 1 ) , p 2 ( x 1 ) , p 3 ( x 2 ) ) {\displaystyle q(f(x_{1},x_{2}))\to g(p_{1}(x_{1}),p_{2}(x_{1}),p_{3}(x_{2}))} . In order for the computation to succeed, it must succeed for both children. That means that the right child must be in the domain of p 3 {\displaystyle p_{3}} . As for the left child, it must be in the domain of both p 1 {\displaystyle p_{1}} and p 2 {\displaystyle p_{2}} . Generally, since subtrees can be copied, a single subtree can be evaluated by multiple states during a run, despite the determinism, and unlike DTTA. Thus the construction of the DTTA recognising the domain of a DTOP must account for sets of states and compute the intersections of their domains, hence the exponential. In the special case of linear DTOP, that is to say DTOP where each x i {\displaystyle x_{i}} appears at most once in the right-hand side of each rule, the construction is linear in time and space. The image of a DTOP is not a regular tree language. Consider the transducer coding the transformation f ( x ) → g ( x , x ) {\displaystyle f(x)\to g(x,x)} ; that is, duplicate the child of the input. This is easily done by a rule q ( f ( x 1 ) ) → g ( p ( x 1 ) , p ( x 1 ) ) {\displaystyle q(f(x_{1}))\to g(p(x_{1}),p(x_{1}))} , where p encodes the identity. Then, absent any restrictions on the first child of the input, the image is a classical non-regular tree language. However, the domain of a DTOP cannot be restricted to a regular tree language. That is to say, given a DTOP T and a language L, one cannot in general build a DTOP T ′ {\displaystyle T'} such that the semantics of T ′ {\displaystyle T'} is that of T, restricted to L. This property is linked to the reason deterministic top-down tree automata are less expressive than bottom-up automata: once you go down a given path, information from other paths is inaccessible. Consider the transducer coding the transformation f ( x , y ) → y {\displaystyle f(x,y)\to y} ; that is, output the right child of the input. This is easily done by a rule q ( f ( x 1 , x 2 ) ) → p ( x 2 ) {\displaystyle q(f(x_{1},x_{2}))\to p(x_{2})} , where p encodes the identity. Now let's say we want to restrict this transducer to the finite (and thus, in particular, regular) domain { f ( c , a ) , f ( c , b ) } {\displaystyle \{f(c,a),\ f(c,b)\}} . We must use the rules q ( f ( x 1 , x 2 ) ) → p ( x 2 ) , p ( a ) → a , p ( b ) → b {\displaystyle q(f(x_{1},x_{2}))\to p(x_{2}),\ p(a)\to a,\ p(b)\to b} . But in the first rule, x 1 {\displaystyle x_{1}} does not appear at all, since nothing is produced from the left child. Thus, it is not possible to test that the left child is c. In contrast, since we produce from the right child, we can test that it is a or b. In general, the criterion is that DTOP cannot test properties of subtrees from which they do not produce output. DTOP are not closed under composition. However this problem can be solved by the addition of a lookahead: a tree automaton, coupled to the transducer, that can perform tests on the domain which the transducer is incapable of. This follows from the point about domain restriction: composing the DTOP encoding identity on { f ( c , a ) , f ( c , b ) } {\displaystyle \{f(c,a),\ f(c,b)\}} with the one encoding f ( x , y ) → y {\displaystyle f(x,y)\to y} must yield a transducer with the semantics { f ( c , a ) ↦ a , f ( c , b ) ↦ b } {\displaystyle \{f(c,a)\mapsto a,\ f(c,b)\mapsto b\}} , which we know is not expressible by a DTOP. The typechecking problem—testing whether the image of a regular tree language is included in another regular tree language—is decidable. The equivalence problem—testing whether two DTOP define the same functions—is decidable. == Bottom-Up Tree Transducers (BOT) == As in the simpler case of tree automata, bottom-up tree transducers are defined similarly to their top-down counterparts, but proceed from the leaves of the tree to the root, instead of from the root to the leaves. Thus the main difference is in the form of the rules, which are of the form f ( q 1 ( x 1 ) , … , q n ( x n ) ) → q ( u ) {\displaystyle f(q_{1}(x_{1}),\dots ,q_{n}(x_{n}))\to q(u)} .

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  METEO System

  The METEO System is a machine translation system specifically designed for the translation of the weather forecasts issued daily by Environment Canada. The system was used from 1981 to 30 September 2001 by Environment Canada to translate forecasts issued in French in the province of Quebec into English and those issued in English in other Canadian provinces into French. Since then, a competitor program has replaced METEO System after an open governmental bid. The system was developed by John Chandioux and was often mentioned as one of the few success stories in the field of machine translation. == History == The METEO System was in operational use at Environment Canada from 1982 to 2001. It stems from a prototype developed in 1975–76 by the TAUM Group, known as TAUM-METEO. The initial motivation to develop that prototype was that a junior translator came to TAUM to ask for help in translating weather bulletins at Environment Canada. Since all official communications emanating from the Canadian government must be available in French and English, because of the Official Languages Act of 1969, and weather bulletins represent a large amount of translation in real time, junior translators had to spend several months producing first draft translations, which were then revised by seniors. That was a difficult and tedious job, because of the specificities of the English and French sublanguages used, and not very rewarding, as the lifetime of a bulletin is only 4 hours. TAUM proposed to build a prototype MT system, and Environment Canada agreed to fund the project. A prototype was ready after a few months, with basic integration in the workflow of translation (source and target bulletins travelled over telex lines at the time and MT happened on a mainframe computer). The first version of the system (METEO 1) went into operation on a Control Data CDC 7600 supercomputer in March 1977. Chandioux then left the TAUM group to manage its operation and improve it, while the TAUM group embarked on a different project (TAUM-aviation, 1977–81). Benoit Thouin made improvements to the initial prototype over the subsequent year, and turned it into an operational system. After three years, METEO 1 had demonstrated the feasibility of microcomputer-based machine translation to the satisfaction of the Canadian government's Translation Bureau of Public Works and Government Services Canada. METEO 1 was formally adopted in 1981, replacing the junior translators in the workflow. Because of the need for high-quality translation, the revision step, done by senior translators, was maintained. The quality, measured as the percentage of edit operations (inserting or deleting a word counts as 1, replacing as 2) on the MT results, reached 85% in 1985. Until that time, the MT part was still implemented as a sequence of Q-systems. The Q-systems formalism is a rule-based SLLP (Specialized Language for Linguistic Programming) invented by Alain Colmerauer in 1967 as he was a postdoc coopérant at the TAUM group. He later invented the Prolog language in 1972 after returning to France and becoming a university professor in Marseille-Luminy. As the engine of the Q-systems is highly non-deterministic, and the manipulated data structures are in some ways too simple, without any types such as string or number, Chandioux encountered limitations in his efforts to raise translation quality and lower computation time to the point he could run it on microcomputers. In 1981, Chandioux created a new SLLP, or metalanguage for linguistic applications, based on the same basic algorithmic ideas as the Q-systems, but more deterministic, and offering typed labels on tree nodes. Following the advice of Bernard Vauquois and Colmerauer, he created GramR, and developed it for microcomputers. In 1982, he could start developing in GramR a new system for translating the weather bulletins on a high-end Cromemco microcomputer. METEO 2 went into operation in 1983. The software then ran in 48Kb of central memory with a 5Mb hard disk for paging. METEO 2 was the first MT application to run on a microcomputer. In 1985, the system had nothing left of the initial prototype, and was officially renamed METEO. It translated about 20 million words per year from English into French, and 10 million words from French into English, with a quality of 97%. Typically, it took 4 minutes for a bulletin in English to be sent from Winnipeg and come back in French after MT and human revision. In 1996, Chandioux developed a special version of his system (METEO 96) which was used to translate the weather forecasts (different kinds of bulletins) issued by the US National Weather Service during the 1996 Summer Olympics in Atlanta. The last known version of the system, METEO 5, dates from 1997 and ran on an IBM PC network under Windows NT. It translated 10 pages per second, but was able to fit into a 1.44Mb floppy disk.

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  CamScanner

  CamScanner is a Chinese mobile app first released in 2010 that allows iOS and Android devices to be used as image scanners. It allows users to 'scan' documents (by taking a photo with the device's camera) and share the photo as either a JPEG or PDF. This app is available free of charge on the Google Play Store and the Apple App Store. The app is based on freemium model, with ad-supported free version and a premium version with additional functions. == History == On August 27, 2019, Russian cyber security company Kaspersky Lab discovered that recent versions of the Android app distributed an advertising library containing a Trojan Dropper, which was also included in some apps preinstalled on several Chinese mobiles. The advertising library decrypts a Zip archive which subsequently downloads additional files from servers controlled by hackers, allowing the hackers to control the device, including by showing intrusive advertising or charging paid subscriptions. Google took the app down after Kaspersky reported its findings. An updated version of the app with the advertising library removed was made available on the Google Play Store as of September 5, 2019. Kaspersky later acknowledged "We appreciate the willingness to cooperate that we've seen from CamScanner representatives, as well as the responsible attitude to user safety they demonstrated while eliminating the threat…The malicious modules were removed from the app immediately upon Kaspersky's warning, and Google Play has restored the app." In June 2020, as tensions along the Line of Actual Control between China and India continued, the Government of India decided to ban 118 Chinese apps, including TikTok and CamScanner citing data and privacy issues. On January 5, 2021, US President Donald Trump signed Executive Order 13971 banning Alipay, Tencent's QQ, QQ Wallet, WeChat Pay, CamScanner, Shareit, VMate and WPS Office to conduct US transactions. The Trump administration explained this act by saying that this move helps prevent personal information such as text, phone calls and photos collected from rivals. However, the Biden administration did not meet the February 2021 deadline for implementing the executive order, allowing these apps to operate in the US and revoked the previous executive order Executive Order 14034 of June 9, 2021.

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  The Best Free AI Video Editor for Beginners

  Comparing the best AI video editor? An AI video editor is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI video editor slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  Lin-Shan Lee

  Lin-Shan Lee (Chinese: 李琳山; born 23 September 1952) is a Taiwanese computer scientist. == Education and career == Lee earned a bachelor's degree in electrical engineering from National Taiwan University in 1974, and pursued a doctorate in the same subject at Stanford University, graduating in 1977. He subsequently returned to Taiwan and joined the NTU faculty in 1982. Lee is a 1993 fellow of the Institute of Electrical and Electronics Engineers, recognized "[f]or contributions to computer voice input/output techniques for Mandarin Chinese and to engineering education." The International Speech Communication Association elevated him to fellow status in 2010 "[f]or his contributions to Chinese spoken language processing and speech information retrieval, and his service to the speech language community." In 2016, Lee was elected a member of Academia Sinica.

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  Boris Katz

  Boris Gershevich Katz (Russian: Борис Гершевич Кац; born October 5, 1947) is a principal American research scientist (computer scientist) at the MIT Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology in Cambridge and head of the Laboratory's InfoLab Group. His research interests include natural language processing and understanding, machine learning and intelligent information access. His brother Victor Kac is a mathematician at MIT. He was able to get out of the USSR with the help of U.S. Senator Ted Kennedy, before the end of the Cold War. Over the last several decades, Boris Katz has been developing the START natural language system that allows the user to access various types of information using English. == Biography == Boris Katz was born on October 5, 1947, in Chișinău in the family of Hersh Katz (died 1976) and Hayki (Klara) Landman (born 1921, Lipcani, Briceni District - died 2006, Cambridge, Middlesex County), who moved from Lipcani, a town located in the northern Bessarabian, to Chișinău before the war. He graduated from Moscow State University and in November 1978, he left for the United States thanks to the personal intervention of Senator Edward M. Kennedy. He defended his thesis as a candidate of physical and mathematical sciences in 1975 under the supervision of Evgenii M. Landis. He currently lives in Boston and heads the InfoLabresearch team at the Laboratory of Informatics and Artificial Intelligence at the Massachusetts Institute of Technology. Boris Katz is the creator of the START information processing system (since 1993 - on the Internet), the author of several works in the field of processing, generation and perception of natural languages, machine learning, and accelerated access to multimedia information. == Family == Brothers - Victor Gershevich Katz, American mathematician, professor at the Massachusetts Institute of Technology; Mikhail Gershevich Katz, Israeli mathematician, graduate of Harvard and Columbia (Ph.D., 1984) universities, professor at Bar-Ilan University, author of the monograph "Systolic Geometry and Topology" (Mathematical Surveys and Monographs, vol. 137. American Mathematical Society: Providence, 2007). Daughter - Luba Katz, a bioinformatics scientist (her husband is Alan Jasanoff, a neuroimaging scientist, a professor at MIT, the son of Harvard University professors Jay Jasanoff and Sheila Jasanoff). == Past works == A Knowledge Entry System for Subject Matter Experts: The goal of SHAKEN project is to enable subject matter experts, without any assistance from AI technologists, to assemble the models of processes and mechanisms so that questions about them can be answered by declarative inference and simulation. Exploiting lexical regularities in designing natural language systems Word sense disambiguation for information retrieval HIKE (HPKB integrated knowledge environment)- a query interface and integrated knowledge environment for HPKB Quantitative evaluation of passage retrieval algorithms for question answering Sticky notes for the semantic web Question answering from the web using knowledge annotation and knowledge mining techniques The role of context in question answering systems

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  Drush

  Drush (DRUpal SHell) is a computer software shell-based application used to control, manipulate, and administer Drupal websites. == Details == Drush was originally developed by Arto Bendiken for Drupal 4.7. In May 2007, it was partly rewritten and redesigned for Drupal 5 by Franz Heinzmann. Drush is maintained by Moshe Weitzman with the support of Owen Barton, greg.1.anderson, jonhattan, Mark Sonnabaum, Jonathan Hedstrom and Christopher Gervais.

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  Klaus-Robert Müller

  Klaus-Robert Müller (born 1964 in Karlsruhe, West Germany) is a German computer scientist and physicist, most noted for his work in machine learning and brain–computer interfaces. == Career == Klaus-Robert Müller received his Diplom in mathematical physics and PhD in theoretical computer science from the University of Karlsruhe. Following his Ph.D. he went to Berlin as a postdoctoral fellow at GMD (German National Research Center for Computer Science) Berlin (now part of Fraunhofer Institute for Open Communication Systems), where he started building up the Intelligent Data Analysis (IDA) group. From 1994 to 1995 he was a research fellow at Shun'ichi Amari's lab at the University of Tokyo. 1999 Müller became an associate professor for neuroinformatics at the University of Potsdam, transitioning to the full professorship for Neural Networks and Time Series Analysis in 2003. Since 2006 he holds the chair for Machine Learning at Technische Universität Berlin. Since 2012 he holds a distinguished professorship at Korea University in Seoul. He co-founded and is co-director of the Berlin Big Data Center (BBDC) of TU Berlin. As of 2017, 29 former doctoral or postdoctoral researchers of Klaus-Robert Müller have become full professors themselves. Bernhard Schölkopf and Alexander J. Smola were supervised by him as members of his research group. Since 2020 he is director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), a German National AI Competence Center, and director of the European Laboratory for Learning and Intelligent Systems (ELLIS) unit Berlin. In 2020/2021 he spent his sabbatical at Google Brain as a principal scientist. == Research == Müller has contributed extensively to several major interests of machine learning, including support vector machines (SVMs) and kernel methods, and artificial neural networks. He pioneered applying new methods of pattern recognition in domains like brain–computer interfaces, using them for patients with Locked-in syndrome. He is one of the leading computer scientists affiliated with Germany. His current research interests include: Statistical learning theory (Support Vector Machines, Deep Neural Networks, Boosting) Learning of non-stationarity data Fusion of structured heterogeneous multi-modal data, co-adaptation Applications: MEG, EEG, NIRS, ECoG, EMG, Brain Computer Interfaces, computational neuroscience, computer vision, genomic data analysis, computational chemistry and atomistic simulations, digital pathology == Honours and awards == Klaus-Robert Müller was elected a fellow of the German National Academy of Sciences Leopoldina in 2012. In 2017 he was elected member of the Berlin-Brandenburg Academy of Sciences and Humanities and also external scientific member of the Max Planck Society. In 2021 he was elected member of the German Academy of Science and Engineering. His work was honoured with several awards, including: 2026 Gottfried Wilhelm Leibniz Prize 2025 IEEE Neural Network Pioneer Award 2024 Feynman Prize in Nanotechnology 2023 Hector Fellow 2025, 2024, 2023, 2022, 2021, 2020, and 2019 Clarivate Highly Cited Researcher 2017 Vodafone Innovations Award 2017 2014 Science Prize of Berlin 2014 by the Governing Mayor of Berlin 2014 European Research Council Panel Consolidator Grants 2009 Best Paper award by IEEE Engineering in Medicine and Biology Society EMBS 2006 SEL-ALCATEL Research Prize for Technical Communication 1999 Olympus Award for Pattern Recognition == Books == with Holzinger, Andreas; et al., eds. (2022). xxAI – Beyond Explainable Artificial Intelligence. Lecture Notes in Computer Science. Vol. 13200. Springer Cham. doi:10.1007/978-3-031-04083-2. ISBN 978-3-031-04082-5. with Schütt, Kristof T.; et al., eds. (2020). Machine Learning Meets Quantum Physics. Lecture Notes in Physics. Vol. 968. Springer Cham. doi:10.1007/978-3-030-40245-7. ISBN 978-3-030-40244-0. S2CID 242406994. with Samek, Wojciech; et al., eds. (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science. Vol. 11700. Springer Cham. doi:10.1007/978-3-030-28954-6. ISBN 978-3-030-28953-9. with Montavon, Grégoire; et al., eds. (2012). Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science. Vol. 7700 (2nd ed.). Springer Berlin, Heidelberg. doi:10.1007/978-3-642-35289-8. ISBN 978-3-642-35288-1. S2CID 39578794.

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  Myhill–Nerode theorem

  In the theory of formal languages, the Myhill–Nerode theorem provides a necessary and sufficient condition for a language to be regular. The theorem is named for John Myhill and Anil Nerode, who proved it at the University of Chicago in 1957 (Nerode & Sauer 1957, p. ii). == Statement == Given a language L {\displaystyle L} , and a pair of strings x {\displaystyle x} and y {\displaystyle y} , define a distinguishing extension to be a string z {\displaystyle z} such that exactly one of the two strings x z {\displaystyle xz} and y z {\displaystyle yz} belongs to L {\displaystyle L} . Define a relation ∼ L {\displaystyle \sim _{L}} on strings as x ∼ L y {\displaystyle x\;\sim _{L}\ y} if there is no distinguishing extension for x {\displaystyle x} and y {\displaystyle y} . It is easy to show that ∼ L {\displaystyle \sim _{L}} is an equivalence relation on strings, and thus it divides the set of all strings into equivalence classes. The Myhill–Nerode theorem states that a language L {\displaystyle L} is regular if and only if ∼ L {\displaystyle \sim _{L}} has a finite number of equivalence classes, and moreover, that this number is equal to the number of states in the minimal deterministic finite automaton (DFA) accepting L {\displaystyle L} . Furthermore, every minimal DFA for the language is isomorphic to the canonical one (Hopcroft & Ullman 1979). Generally, for any language, the constructed automaton is a state automaton acceptor. However, it does not necessarily have finitely many states. The Myhill–Nerode theorem shows that finiteness is necessary and sufficient for language regularity. Some authors refer to the ∼ L {\displaystyle \sim _{L}} relation as Nerode congruence, in honor of Anil Nerode. == Use and consequences == The Myhill–Nerode theorem may be used to show that a language L {\displaystyle L} is regular by proving that the number of equivalence classes of ∼ L {\displaystyle \sim _{L}} is finite. This may be done by an exhaustive case analysis in which, beginning from the empty string, distinguishing extensions are used to find additional equivalence classes until no more can be found. For example, the language consisting of binary representations of numbers that can be divided by 3 is regular. Given two binary strings x , y {\displaystyle x,y} , extending them by one digit gives 2 x + b , 2 y + b {\displaystyle 2x+b,2y+b} , so 2 x + b ≡ 2 y + b mod 3 {\displaystyle 2x+b\equiv 2y+b\mod 3} iff x ≡ y mod 3 {\displaystyle x\equiv y\mod 3} . Thus, 00 {\displaystyle 00} (or 11 {\displaystyle 11} ), 01 {\displaystyle 01} , and 10 {\displaystyle 10} are the only distinguishing extensions, resulting in the 3 classes. The minimal automaton accepting our language would have three states corresponding to these three equivalence classes. Another immediate corollary of the theorem is that if for a language L {\displaystyle L} the relation ∼ L {\displaystyle \sim _{L}} has infinitely many equivalence classes, it is not regular. It is this corollary that is frequently used to prove that a language is not regular. == Generalizations == The Myhill–Nerode theorem can be generalized to tree automata.

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  Best AI Analytics Tools in 2026

  Curious about the best AI analytics tool? An AI analytics tool is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI analytics tool slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  Apache Hama

  Apache Hama is a distributed computing framework based on bulk synchronous parallel computing techniques for massive scientific computations e.g., matrix, graph and network algorithms. Originally a sub-project of Hadoop, it became an Apache Software Foundation top level project in 2012. It was created by Edward J. Yoon, who named it (short for "Hadoop Matrix Algebra"), and Hama also means hippopotamus in Yoon's native Korean language (하마), following the trend of naming Apache projects after animals and zoology (such as Apache Pig). Hama was inspired by Google's Pregel large-scale graph computing framework described in 2010. When executing graph algorithms, Hama showed a fifty-fold performance increase relative to Hadoop. Retired in April 2020, project resources are made available as part of the Apache Attic. Yoon cited issues of installation, scalability, and a difficult programming model for its lack of adoption. == Architecture == Hama consists of three major components: BSPMaster, GroomServers and Zookeeper. === BSPMaster === BSPMaster is responsible for: Maintaining groom server status Controlling super steps in a cluster Maintaining job progress information Scheduling jobs and assigning tasks to groom servers Disseminating execution class across groom servers Controlling fault Providing users with the cluster control interface. A BSP Master and multiple grooms are started by the script. Then, the bsp master starts up with a RPC server for groom servers. Groom servers starts up with a BSPPeer instance and a RPC proxy to contact the bsp master. After started, each groom periodically sends a heartbeat message that encloses its groom server status, including maximum task capacity, unused memory, and so on. Each time the BSP master receives a heartbeat message, it brings the groom server status up-to-date. The bsp master makes use of groom servers' status in order to assign tasks to idle groom servers - and returns a heartbeat response containing assigned tasks and others actions for a groom server to do. Currently BSP master has a FIFO job scheduler and simple task assignment algorithms. === GroomServer === A groom server (shortly referred to as groom) is a process that performs BSP tasks assigned by BSPMaster. Each groom contacts the BSPMaster, and it takes assigned tasks and reports its status by means of periodical piggybacks with BSPMaster. Each groom is designed to run with HDFS or other distributed storages. Basically, a groom server and a data node should be run on one physical node. === Zookeeper === A Zookeeper is used to manage the efficient barrier synchronisation of the BSPPeers.

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  Corpus linguistics

  Corpus linguistics is an empirical method for the study of language by text corpus (plural corpora). Corpora are balanced, often stratified collections of authentic, "real world", text of speech or writing that aim to represent a given linguistic variety. Today, corpora are generally machine-readable data collections. Corpus linguistics proposes that a reliable analysis of a language is more feasible with corpora collected in the field—the natural context ("realia") of that language—with minimal experimental interference. Large collections of text, though corpora may also be small in terms of running words, allow linguists to run quantitative analyses on linguistic concepts that may be difficult to test in a qualitative manner. The text-corpus method uses the body of texts in any natural language to derive the set of abstract rules which govern that language. Those results can be used to explore the relationships between that subject language and other languages which have undergone a similar analysis. The first such corpora were manually derived from source texts, but now that work is automated. Corpora have not only been used for linguistics research, they have been increasingly used to compile dictionaries (starting with The American Heritage Dictionary of the English Language in 1969) and reference grammars, with A Comprehensive Grammar of the English Language, published in 1985, as a first. Experts in the field have differing views about the annotation of a corpus. These views range from John McHardy Sinclair, who advocates minimal annotation so texts speak for themselves, to the Survey of English Usage team (University College, London), who advocate annotation as allowing greater linguistic understanding through rigorous recording. == History == Some of the earliest efforts at grammatical description were based at least in part on corpora of particular religious or cultural significance. For example, Prātiśākhya literature described the sound patterns of Sanskrit as found in the Vedas, and Pāṇini's grammar of classical Sanskrit was based at least in part on analysis of that same corpus. Similarly, the early Arabic grammarians paid particular attention to the language of the Quran. In the Western European tradition, scholars prepared concordances to allow detailed study of the language of the Bible and other canonical texts. === English corpora === A landmark in modern corpus linguistics was the publication of Computational Analysis of Present-Day American English in 1967. Written by Henry Kučera and W. Nelson Francis, the work was based on an analysis of the Brown Corpus, which is a structured and balanced corpus of one million words of American English from the year 1961. The corpus comprises 2000 text samples, from a variety of genres. The Brown Corpus was the first computerized corpus designed for linguistic research. Kučera and Francis subjected the Brown Corpus to a variety of computational analyses and then combined elements of linguistics, language teaching, psychology, statistics, and sociology to create a rich and variegated opus. A further key publication was Randolph Quirk's "Towards a description of English Usage" in 1960 in which he introduced the Survey of English Usage. Quirk's corpus was the first modern corpus to be built with the purpose of representing the whole language. Shortly thereafter, Boston publisher Houghton-Mifflin approached Kučera to supply a million-word, three-line citation base for its new American Heritage Dictionary, the first dictionary compiled using corpus linguistics. The AHD took the innovative step of combining prescriptive elements (how language should be used) with descriptive information (how it actually is used). Other publishers followed suit. The British publisher Collins' COBUILD monolingual learner's dictionary, designed for users learning English as a foreign language, was compiled using the Bank of English. The Survey of English Usage Corpus was used in the development of one of the most important Corpus-based Grammars, which was written by Quirk et al. and published in 1985 as A Comprehensive Grammar of the English Language. The Brown Corpus has also spawned a number of similarly structured corpora: the LOB Corpus (1960s British English), Kolhapur (Indian English), Wellington (New Zealand English), Australian Corpus of English (Australian English), the Frown Corpus (early 1990s American English), and the FLOB Corpus (1990s British English). Other corpora represent many languages, varieties and modes, and include the International Corpus of English, and the British National Corpus, a 100 million word collection of a range of spoken and written texts, created in the 1990s by a consortium of publishers, universities (Oxford and Lancaster) and the British Library. For contemporary American English, work has stalled on the American National Corpus, but the 400+ million word Corpus of Contemporary American English (1990–present) is now available through a web interface. The first computerized corpus of transcribed spoken language was constructed in 1971 by the Montreal French Project, containing one million words, which inspired Shana Poplack's much larger corpus of spoken French in the Ottawa-Hull area. === Multilingual corpora === In the 1990s, many of the notable early successes on statistical methods in natural-language programming (NLP) occurred in the field of machine translation, due especially to work at IBM Research. These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. There are corpora in non-European languages as well. For example, the National Institute for Japanese Language and Linguistics in Japan has built a number of corpora of spoken and written Japanese. Sign language corpora have also been created using video data. === Ancient languages corpora === Besides these corpora of living languages, computerized corpora have also been made of collections of texts in ancient languages. An example is the Andersen-Forbes database of the Hebrew Bible, developed since the 1970s, in which every clause is parsed using graphs representing up to seven levels of syntax, and every segment tagged with seven fields of information. The Quranic Arabic Corpus is an annotated corpus for the Classical Arabic language of the Quran. This is a recent project with multiple layers of annotation including morphological segmentation, part-of-speech tagging, and syntactic analysis using dependency grammar. The Digital Corpus of Sanskrit (DCS) is a "Sandhi-split corpus of Sanskrit texts with full morphological and lexical analysis... designed for text-historical research in Sanskrit linguistics and philology." === Corpora from specific fields === Besides pure linguistic inquiry, researchers had begun to apply corpus linguistics to other academic and professional fields, such as the emerging sub-discipline of Law and Corpus Linguistics, which seeks to understand legal texts using corpus data and tools. The DBLP Discovery Dataset concentrates on computer science, containing relevant computer science publications with sentient metadata such as author affiliations, citations, or study fields. A more focused dataset was introduced by NLP Scholar, a combination of papers of the ACL Anthology and Google Scholar metadata. Corpora can also aid in translation efforts or in teaching foreign languages. == Methods == Corpus linguistics has generated a number of research methods, which attempt to trace a path from data to theory. Wallis and Nelson (2001) first introduced what they called the 3A perspective: Annotation, Abstraction and Analysis. Annotation consists of the application of a scheme to texts. Annotations may include structural markup, part-of-speech tagging, parsing, and numerous other representations. Abstraction consists of the translation (mapping) of terms in the scheme to terms in a theoretically motivated model or dataset. Abstraction typically includes linguist-directed search but may include e.g., rule-learning for parsers. Analysis consists of statistically probing, manipulating and generalising from the dataset. Analysis might include statistical evaluations, optimisation of rule-bases or knowledge discovery methods. Most lexical corpora today are part-of-speech-tagged (POS-tagged). However even corpus linguists who work with 'unannotated plain text' inevitably apply some method to isolate salient terms. In such situations annotation and abstraction are combined in a lexical search. The advantage of publishing an annotated corpus is that other users can then perform experiments on the corpus (through corpus managers). Linguists with other interests and differing perspectives than the originators' can exploit this work. By sharing data

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  Cortana (virtual assistant)

  Cortana is a discontinued virtual assistant developed by Microsoft that used the Bing search engine to perform tasks such as setting reminders and answering questions for users. Cortana was available in English, Portuguese, French, German, Italian, Spanish, Chinese, and Japanese language editions, depending on the software platform and region in which it was used. In 2019, Microsoft began reducing the prevalence of Cortana and converting it from an assistant into different software integrations. It was split from the Windows 10 search bar in April 2019. In January 2020, the Cortana mobile app was removed from certain markets, and on March 31, 2021, the Cortana mobile app was shut down globally. On June 2, 2023, Microsoft announced that support for the Cortana standalone app on Microsoft Windows would end in late 2023 and would be replaced by Microsoft Copilot, an AI chatbot. Support for Cortana in the Microsoft Outlook and Microsoft 365 mobile apps was discontinued in fall of 2023. == History == === Beginnings (2009–2014) === The development of Cortana started in 2009 in the Microsoft Speech products team with general manager Zig Serafin and Chief Scientist Larry Heck. Heck and Serafin established the vision, mission, and long-range plan for Microsoft's digital personal assistant and they built a team with the expertise to create the initial prototypes for Cortana. Some of the key researchers in these early efforts included Microsoft Research researchers Dilek Hakkani-Tür, Gokhan Tur, Andreas Stolcke, and Malcolm Slaney, research software developer Madhu Chinthakunta, and user experience designer Lisa Stifelman. To develop the Cortana digital assistant, the team interviewed human personal assistants. The interviews inspired a number of unique features in Cortana, including the assistant's "notebook" feature. Originally, Cortana was meant to be only a codename, but a petition on Windows Phone's UserVoice site proved to be popular and made the codename official. Cortana was demonstrated for the first time at the Microsoft Build developer conference in San Francisco in April 2014. It was launched as a key ingredient of Microsoft's planned "makeover" of future operating systems for Windows Phone and Windows. It was named after Cortana, a synthetic intelligence character in Microsoft's Halo video game franchise originating in Bungie folklore, with Jen Taylor, the character's voice actress, returning to voice the personal assistant's US-specific version. === Expansion (2015–2018) === In January 2015, Microsoft announced the availability of Cortana for Windows 10 desktops and mobile devices as part of merging Windows Phone into the operating system at large. On May 26, 2015, Microsoft announced that Cortana would also be available on other mobile platforms. An Android release was set for July 2015, but the Android APK file containing Cortana was leaked ahead of its release. It was officially released, along with an iOS version, in December 2015. During E3 2015, Microsoft announced that Cortana would come to the Xbox One as part of a universally designed Windows 10 update for the console. Microsoft integrated Cortana into numerous products such as Microsoft Edge. Microsoft's Cortana assistant was deeply integrated into the browser. Cortana was able to find opening hours when on restaurant sites, show retail coupons for websites, or show weather information in the address bar. At the Worldwide Partners Conference 2015 Microsoft demonstrated Cortana integration with products such as GigJam. Conversely, Microsoft announced in late April 2016 that it would block anything other than Bing and Edge from being used to complete Cortana searches, again raising questions of anti-competitive practices by the company. Microsoft's "Windows in the car" concept included Cortana. The concept makes it possible for drivers to make restaurant reservations and see places before they go there. At Microsoft Build 2016, Microsoft announced plans to integrate Cortana into Skype (Microsoft's video-conferencing and instant messaging service) as a bot to allow users to order food, book trips, transcribe video messages and make calendar appointments through Cortana in addition to other bots. As of 2016, Cortana was able to underline certain words and phrases in Skype conversations that relate to contacts and corporations. A writer from Engadget has criticised the Cortana integration in Skype for responding only to very specific keywords, feeling as if she was "chatting with a search engine" due to the impersonal way the bots replied to certain words such as "Hello" causing the Bing Music bot to bring up Adele's song of that name. Microsoft also announced at Microsoft Build 2016 that Cortana would be able to cloud-synchronise notifications between Windows 10 Mobile's and Windows 10's Action Center, as well as notifications from Android devices. In December 2016, Microsoft announced the preview of Calendar.help, a service that enabled people to delegate the scheduling of meetings to Cortana. Users interact with Cortana by including her in email conversations. Cortana would then check people's availability in Outlook Calendar or Google Calendar, and work with others Cc'd on the email to schedule the meeting. The service relied on automation and human-based computation. In May 2017, Microsoft announced INVOKE, a voice-activated speaker featuring Cortana, in collaboration with Harman Kardon. The premium speaker has a cylindrical design and offers 360-degree sound, the ability to make and receive calls with Skype, and all of the other features currently available with Cortana. In 2017, Microsoft partnered with Amazon to integrate Echo and Cortana with each other, allowing users of each smart assistant to summon the other via a command. This feature preview was released in August 2018. Windows 10 users were able to just say "Hey Cortana, open Alexa" and Echo users were able to say "Alexa, open Cortana" to summon the other assistant. === Decreasing focus and discontinuation (2019–2024) === In January 2019, Microsoft CEO Satya Nadella stated that he no longer saw Cortana as a direct competitor against Alexa and Siri. Shortly thereafter, Microsoft began reducing the prevalence of Cortana and converting it from an assistant into different software integrations. It was split from the Windows 10 search bar in April 2019. In January 2020, the Cortana mobile app was removed from certain markets, and then, on July 24, 2020, Cortana was removed from the Xbox dashboard as part of a redesign. On January 31, 2021, Microsoft removed the Cortana mobile application in many markets, including the UK, Australia, Germany, Mexico, China, Spain, Canada, and India. On March 31, 2021, Microsoft shut down the Cortana apps globally for iOS and Android and removed the apps entirely from their corresponding app stores. To access previously recorded content, users had to use Cortana on Windows 10 or other specialized Microsoft applications. Microsoft also reduced emphasis on Cortana in Windows with the 2021 release of Windows 11. Cortana was not used during the device setup process or pinned to the taskbar by default. On June 2, 2023, Microsoft announced the Cortana standalone app on Windows 10 and Windows 11 which would shut down later in the year. In its support article, Microsoft listed several alternatives, most of which have since been rebranded as Microsoft Copilot. They also added that the change would not impact Cortana in Office 365 and Teams environments. On August 11, 2023, Microsoft updated the Cortana standalone app in Windows, informing that it was deprecated and can no longer be used. Microsoft's support article announcing the deprecation of Cortana was updated to reflect this change. Along with the deprecation of the standalone app, it was announced that Cortana support in Teams mobile, Microsoft Teams displays, and Teams rooms would end in late 2023. The support article states that Cortana in the “Play my emails” feature of the Microsoft Outlook mobile app would continue to be available. Later in June 2024, the support article was updated, stating that Cortana in the voice search and the "Play my emails" feature is now removed from the Microsoft Outlook mobile app, officially marking the discontinuation of Cortana across all Microsoft products. On May 22, 2024, Microsoft announced the Windows 11 24H2 update, which removed Cortana, Tips, and WordPad from systems. == Functionality == Cortana was able to set reminders, recognize natural voice without the requirement for keyboard input, and answer questions using information from the Bing search engine. Searches using Windows 10 are made only with the Microsoft Bing search engine, and all links will open with Microsoft Edge, except when a screen reader such as Narrator was being used, where the links will open in Internet Explorer. Windows Phone 8.1's universal Bing SmartSearch features were incorporated into Cortana, which replaced the

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