Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.
Computers & Graphics
Computers & Graphics is a peer-reviewed scientific journal that covers computer graphics and related subjects such as data visualization, human-computer interaction, virtual reality, and augmented reality. It was established in 1975 and originally published by Pergamon Press. It is now published by Elsevier, which acquired Pergamon Press in 1991. From 2018 to 2022 Graphics and Visual Computing was an open access sister journal sharing the same editorial team and double-blind peer-review policies. It has since merged into GMOD, the International Journal of Graphical Models. == History == The journal was established in 1975 by founding editor-in-chief Robert Schiffman (University of Colorado, Boulder), as Computers & Graphics-UK. Schiffman, who co-organized the first SIGGRAPH conference in 1974, had the conference proceedings published as the first issue of the journal. He was succeeded in 1978 by Larry Feeser (Rensselaer Polytechnic Institute). In 1983 José Luis Encarnação (Technische Hochschule Darmstadt) took over. Joaquim Jorge (University of Lisbon) has been Editor-in-Chief since 2007. == Replicability == The journal is working with the Graphics Replicability Stamp Initiative to promote replicable results in publication. == Abstracting and indexing == The journal is abstracted and indexed in: Current Contents/Engineering, Computing & Technology EBSCO databases Ei Compendex Inspec ProQuest databases Science Citation Index Expanded Scopus Chinese Computer Federation/Recommended List of International Conferences and Journals on CAD & Graphics and Multimedia. According to the Journal Citation Reports, the journal has a 2022 impact factor of 2.5.
Glushkov's construction algorithm
In computer science theory – particularly formal language theory – Glushkov's construction algorithm, invented by Victor Mikhailovich Glushkov, transforms a given regular expression into an equivalent nondeterministic finite automaton (NFA). Thus, it forms a bridge between regular expressions and nondeterministic finite automata: two abstract representations of the same class of formal languages. A regular expression may be used to conveniently describe an advanced search pattern in a "find and replace"–like operation of a text processing utility. Glushkov's algorithm can be used to transform it into an NFA, which furthermore is small by nature, as the number of its states equals the number of symbols of the regular expression, plus one. Subsequently, the NFA can be made deterministic by the powerset construction and then be minimized to get an optimal automaton corresponding to the given regular expression. The latter format is best suited for execution on a computer. From another, more theoretical point of view, Glushkov's algorithm is a part of the proof that NFA and regular expressions both accept exactly the same languages; that is, the regular languages. The converse of Glushkov's algorithm is Kleene's algorithm, which transforms a finite automaton into a regular expression. The automaton obtained by Glushkov's construction is the same as the one obtained by Thompson's construction algorithm, once its ε-transitions are removed. Glushkov's construction algorithm is also called The algorithm of Berry-Sethi, named after Gérard Berry and Ravi Sethi who worked on this construction. == Construction == Given a regular expression e, the Glushkov Construction Algorithm creates a non-deterministic automaton that accepts the language L ( e ) {\displaystyle L(e)} accepted by e. The construction uses four steps: === Step 1 === Linearisation of the expression. Each letter of the alphabet appearing in the expression e is renamed, so that each letter occurs at most once in the new expression e ′ {\displaystyle e'} . Glushkov's construction essentially relies on the fact that e ′ {\displaystyle e'} represents a local language L ( e ′ ) {\displaystyle L(e')} . Let A be the old alphabet and let B be the new one. === Step 2a === Computation of the sets P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , and F ( e ′ ) {\displaystyle F(e')} . The first, P ( e ′ ) {\displaystyle P(e')} , is the set of letters which occurs as first letter of a word of L ( e ′ ) {\displaystyle L(e')} . The second, D ( e ′ ) {\displaystyle D(e')} , is the set of letters that can end a word of L ( e ′ ) {\displaystyle L(e')} . The last one, F ( e ′ ) {\displaystyle F(e')} , is the set of letter pairs that can occur in words of L ( e ′ ) {\displaystyle L(e')} , i.e. it is the set of factors of length two of the words of L ( e ′ ) {\displaystyle L(e')} . Those sets are mathematically defined by P ( e ′ ) = { x ∈ B ∣ x B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle P(e')=\{x\in B\mid xB^{}\cap L(e')\neq \emptyset \}} , D ( e ′ ) = { y ∈ B ∣ B ∗ y ∩ L ( e ′ ) ≠ ∅ } {\displaystyle D(e')=\{y\in B\mid B^{}y\cap L(e')\neq \emptyset \}} , F ( e ′ ) = { u ∈ B 2 ∣ B ∗ u B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle F(e')=\{u\in B^{2}\mid B^{}uB^{}\cap L(e')\neq \emptyset \}} . They are computed by induction over the structure of the expression, as explained below. === Step 2b === Computation of the set Λ ( e ′ ) {\displaystyle \Lambda (e')} which contains the empty word ε {\displaystyle \varepsilon } if this word belongs to L ( e ′ ) {\displaystyle L(e')} , and is the empty set otherwise. Formally, this is Λ ( e ′ ) = { ε } ∩ L ( e ′ ) {\displaystyle \Lambda (e')=\{\varepsilon \}\cap L(e')} . === Step 3 === Computation of automaton recognizing the local language, as defined by P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , F ( e ′ ) {\displaystyle F(e')} , and Λ ( e ′ ) {\displaystyle \Lambda (e')} . By definition, the local language defined by the sets P, D, and F is the set of words which begin with a letter of P, end by a letter of D, and whose factors of length 2 belong to F, optionally also including the empty word; that is, it is the language: L ′ = ( P B ∗ ∩ B ∗ D ) ∖ B ∗ ( B 2 ∖ F ) B ∗ ∪ Λ ( e ′ ) {\displaystyle L'=(PB^{}\cap B^{}D)\setminus B^{}(B^{2}\setminus F)B^{}\cup \Lambda (e')} . Strictly speaking, it is the computation of the automaton for the local language denoted by this linearised expression that is Glushkov's construction. === Step 4 === Remove the linearisation, replacing each indexed letter B by the original letter of A. == Example == Consider the regular expression e = ( a ( a b ) ∗ ) ∗ + ( b a ) ∗ {\displaystyle e=(a(ab)^{})^{}+(ba)^{}} . == Computation of the set of letters == The computation of the sets P, D, F, and Λ is done inductively over the regular expression e ′ {\displaystyle e'} . One must give the values for ∅, ε (the symbols for the empty language and the singleton language containing the empty word), the letters, and the results of the operations + , ⋅ , ∗ {\displaystyle +,\cdot ,^{}} . The most costly operations are the cartesian products of sets for the computation of F. == Properties == The obtained automaton is non-deterministic, and it has as many states as the number of letters of the regular expression, plus one. It has been proven that every Thompson's automaton can be transformed into Glushkov's automaton via a ε-transitions elimination method. == Applications and deterministic expressions == The computation of the automaton by the expression occurs often; it has been systematically used in search functions, in particular by the Unix grep command. Similarly, XML's specification also uses such constructions; for more efficiency, regular expressions of a certain kind, called deterministic expressions, have been studied.
Glottochronology
Glottochronology (from Attic Greek γλῶττα 'tongue, language' and χρόνος 'time') is the part of lexicostatistics which involves comparative linguistics and deals with the chronological relationship between languages. The idea was developed by Morris Swadesh in the 1950s in his article on Salish internal relationships. He developed the idea under two assumptions: there indeed exists a relatively stable basic vocabulary (referred to as Swadesh lists) in all languages of the world; and, any replacements happen in a way analogous to radioactive decay in a constant percentage per time elapsed. Using mathematics and statistics, Swadesh developed an equation to determine when languages separated and give an approximate time of when the separation occurred. His methods aimed to aid linguistic anthropologists by giving them a definitive way to determine a separation date between two languages. The formula provides an approximate number of centuries since two languages were supposed to have separated from a singular common ancestor. His methods also purported to provide information on when ancient languages may have existed. Despite multiple studies and literature containing the information of glottochronology, it is not widely used today and is surrounded with controversy. Glottochronology tracks language separation from thousands of years ago but many linguists are skeptical of the concept because it is more of a 'probability' rather than a 'certainty.' On the other hand, some linguists may say that glottochronology is gaining traction because of its relatedness to archaeological dates. Glottochronology is not as accurate as archaeological data, but some linguists still believe that it can provide a solid estimate. Over time many different extensions of the Swadesh method evolved; however, Swadesh's original method is so well known that 'glottochronology' is usually associated with him. == Methodology == The original method of glottochronology presumed that the core vocabulary of a language is replaced at a constant (or constant average) rate across all languages and cultures and so can be used to measure the passage of time. The process makes use of a list of lexical terms and morphemes which are similar to multiple languages. Lists were compiled by Morris Swadesh and assumed to be resistant against borrowing (originally designed in 1952 as a list of 200 items, but the refined 100-word list in Swadesh (1955) is much more common among modern day linguists). The core vocabulary was designed to encompass concepts common to every human language such as personal pronouns, body parts, heavenly bodies and living beings, verbs of basic actions, numerals, basic adjectives, kin terms, and natural occurrences and events. Through a basic word list, one eliminates concepts that are specific to a particular culture or time period. It has been found through differentiating word lists that the ideal is really impossible and that the meaning set may need to be tailored to the languages being compared. Word lists are not homogenous throughout studies and they are often changed and designed to suit both languages being studied. Linguists find that it is difficult to find a word list where all words used are culturally unbiased. Many alternative word lists have been compiled by other linguists and often use fewer meaning slots. The percentage of cognates (words with a common origin) in the word lists is then measured. The larger the percentage of cognates, the more recently the two languages being compared are presumed to have separated. === Glottochronologic constant === Determining word lists rely on morpheme decay or change in vocabulary. Morpheme decay must stay at a constant rate for glottochronology to be applied to a language. This leads to a critique of the glottochronologic formula because some linguists argue that the morpheme decay rate is not guaranteed to stay the same throughout history. American Linguist Robert Lees obtained a value for the "glottochronological constant" (r) of words by considering the known changes in 13 pairs of languages using the 200 word list. He obtained a value of 0.8048 ± 0.0176 with 90% confidence. For his 100-word list Swadesh obtained a value of 0.86, the higher value reflecting the elimination of semantically unstable words. === Divergence time === The basic formula of glottochronology proposed by Morris Swadesh is: t = − ln ( c ) 2 ln ( r ) {\displaystyle t=-{\frac {\ln(c)}{2\ln(r)}}} t = a given period of time from one stage of the language to another (measured in millennia), c = proportion of wordlist items retained at the end of that period and r = rate of replacement for that word list. By testing historically verifiable cases in which t is known by nonlinguistic data (such as the approximate distance from Classical Latin to modern Romance languages), Swadesh arrived at the empirical value of approximately 0.14 for L, (c?) which means that the rate of replacement constitutes around 14 words from the 100-wordlist per millennium. This is represented in the table below. === Results === Glottochronology was applied to a range of language families, including Salishan, Indo-European, Japonic, Afro-Asiatic, Chinese and Mayan and other American languages. For Amerind, correlations have been obtained with radiocarbon dating and blood groups as well as archaeology. === Example Wordlist === Below is an example of a basic word list composed of basic Turkish words and their English translations. == Discussion == The concept of language change is old, and its history is reviewed in Hymes (1973) and Wells (1973). In some sense, glottochronology is a reconstruction of history and can often be closely related to archaeology. Many linguistic studies find the success of glottochronology to be found alongside archaeological data. Glottochronology itself dates back to the mid-20th century. An introduction to the subject is given in Embleton (1986) and in McMahon and McMahon (2005). Glottochronology has been controversial ever since, partly because of issues of accuracy but also because of the question of whether its basis is sound (for example, Bergsland 1958; Bergsland and Vogt 1962; Fodor 1961; Chrétien 1962; Guy 1980). The concerns have been addressed by Dobson et al. (1972), Dyen (1973) and Kruskal, Dyen and Black (1973). The assumption of a single-word replacement rate can distort the divergence-time estimate when borrowed words are included (Thomason and Kaufman 1988). The presentations vary from "Why linguists don't do dates" to the one by Starostin discussed below. Since its original inception, glottochronology has been rejected by many linguists, mostly Indo-Europeanists of the school of the traditional comparative method. Criticisms have been answered in particular around three points of discussion: Criticism levelled against the higher stability of lexemes in Swadesh lists alone (Haarmann 1990) misses the point because a certain amount of losses only enables the computations (Sankoff 1970). The non-homogeneity of word lists often leads to lack of understanding between linguists. Linguists also have difficulties finding a completely unbiased list of basic cultural words. it can take a long time for linguists to find a viable word list which can take several test lists to find a usable list. Traditional glottochronology presumes that language changes at a stable rate. Thus, in Bergsland & Vogt (1962), the authors make an impressive demonstration, on the basis of actual language data verifiable by extralinguistic sources, that the "rate of change" for Icelandic constituted around 4% per millennium, but for closely connected Riksmal (Literary Norwegian), it would amount to as much as 20% (Swadesh's proposed "constant rate" was supposed to be around 14% per millennium). That and several other similar examples effectively proved that Swadesh's formula would not work on all available material, which is a serious accusation since evidence that can be used to "calibrate" the meaning of L (language history recorded during prolonged periods of time) is not overwhelmingly large in the first place. It is highly likely that the chance of replacement is different for every word or feature ("each word has its own history", among hundreds of other sources:). That global assumption has been modified and downgraded to single words, even in single languages, in many newer attempts (see below). There is a lack of understanding of Swadesh's mathematical/statistical methods. Some linguists reject the methods in full because the statistics lead to 'probabilities' when linguists trust 'certainties' more. A serious argument is that language change arises from socio-historical events that are, of course, unforeseeable and, therefore, uncomputable. == Modifications == Somewhere in between the original concept of Swadesh and the rejection of glottochronology in its entirety lies the idea that glottochronology as a formal method of linguistic
Anil K. Jain (computer scientist, born 1948)
Anil Kumar Jain (born 1948) is an Indian-American computer scientist and University Distinguished Professor in the Department of Computer Science and Engineering at Michigan State University. He is one of the most highly cited researchers in computer science, and is internationally recognized for his foundational contributions to pattern recognition, computer vision, and biometric recognition, particularly in fingerprint recognition and face recognition. Jain is a member of the United States National Academy of Engineering, a Foreign Member of the Chinese Academy of Sciences, and a Foreign Fellow of the Indian National Academy of Engineering. He is a Fellow of the ACM, IEEE, AAAS, IAPR, and SPIE. His research has shaped the field of biometrics and has been applied in systems used worldwide for identity verification, law enforcement, and border security. In 2024, he was awarded the BBVA Foundation Frontiers of Knowledge Award in the category of Information and Communication Technologies. == Early life and education == Born in Basti, India, Jain received his Bachelor of Technology in electrical engineering from the Indian Institute of Technology, Kanpur in 1969. He then moved to the United States, where he earned his M.S. in 1970 and Ph.D. in 1973 from Ohio State University. His doctoral dissertation, titled Some Aspects of Dimensionality and Sample Size Problems in Statistical Pattern Recognition, was supervised by Robert B. McGhee and laid the groundwork for his subsequent research in pattern recognition. == Career == Jain began his academic career at Wayne State University, where he taught from 1972 to 1974. In 1974, he joined the faculty of Michigan State University, where he has remained for over five decades and currently holds the position of University Distinguished Professor. Throughout his career, Jain has conducted pioneering research in data clustering, fingerprint recognition, and face recognition. His work has been published in leading scientific journals including Scientific American, Nature, IEEE Spectrum, and MIT Technology Review. He served as Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence from 1991 to 1994. Jain has also contributed to national security and policy through his service on several advisory bodies. He served as a member of the U.S. National Academies panels on Information Technology, Whither Biometrics, and Improvised Explosive Devices (IED). He has also served on the Defense Science Board, the Forensic Science Standards Board, and the AAAS Latent Fingerprint Working Group. In 2014, Jain was named Innovator of the Year at Michigan State University for transferring several technologies on face and fingerprint recognition to major players in the biometrics industry. He holds eight U.S. and Korean patents related to biometric technologies. == Research contributions == Jain's research spans pattern recognition, computer vision, machine learning, and biometric recognition. His contributions have been particularly influential in several areas: === Biometric recognition === Jain is considered one of the foremost authorities on biometric recognition systems. His research group at Michigan State University has developed algorithms and systems for fingerprint, face, and iris recognition that have been widely adopted in both academic research and commercial applications. His work on fingerprint matching algorithms has been instrumental in establishing standards for automated fingerprint identification systems (AFIS) used by law enforcement agencies worldwide. In recent years, Jain and his research team have made significant advances in child fingerprint recognition, demonstrating that digital scans of a young child's fingerprint can be correctly recognized one year later with over 99 percent accuracy for children as young as six months old. This research has important implications for child identification in developing countries, where it can be used to track immunization records and provide access to medical care. === Data clustering === Jain's survey article "Data clustering: a review" (1999), co-authored with M. N. Murty and P. J. Flynn, is one of the most highly cited papers in computer science. His 2010 paper "Data Clustering: 50 Years Beyond K-Means" provided a comprehensive overview of the evolution of clustering methods and remains an essential reference in the field. === Statistical pattern recognition === Jain's work on statistical pattern recognition, including his influential survey "Statistical pattern recognition: A review" (2000) with R. P. W. Duin and Jianchang Mao, has shaped the theoretical foundations of the field. == Citation metrics and academic impact == Jain is among the most highly cited researchers in computer science. Based on his Google Scholar profile, he had an h-index of 200 in 2020, which was the highest among computer scientists identified in a survey published by UCLA at the time. As of August 2023, his h-index on Google Scholar is 211. He has since been surpassed by Yoshua Bengio, a researcher of similar subjects (neural networks and deep learning for artificial intelligence), who had an h-index of 224 as of August 2023. Another source reported that as of December 2022, he had the highest discipline h-index (D-index) in computer science. == Honors and awards == Jain has received numerous awards and honors recognizing his contributions to computer science and engineering: === Academy memberships === Member, United States National Academy of Engineering (2016) — elected "for contributions to the engineering and practice of biometrics" Foreign Fellow, Indian National Academy of Engineering (2016) Foreign Member, Chinese Academy of Sciences (2019) Member, The World Academy of Sciences (2019) Fellow, National Academy of Inventors === Professional society fellowships === Fellow, ACM Fellow, IEEE (1988) — for contributions to image processing Fellow, AAAS Fellow, International Association for Pattern Recognition Fellow, SPIE === Major awards === BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies (2024) IAPR King-Sun Fu Prize (2008) IEEE W. Wallace McDowell Award (2007) — the highest technical honor awarded by the IEEE Computer Society, for pioneering contributions to theory, technique, and practice of pattern recognition, computer vision, and biometric recognition systems IEEE Computer Society Technical Achievement Award (2003) IAPR Pierre Devijver Award (2002) Humboldt Research Award (2002) Guggenheim Fellowship (2001) Fulbright Fellowship (1998) IEEE ICDM Research Contribution Award (2008) === Best paper awards === IEEE Transactions on Neural Networks (1996) Pattern Recognition journal (1987, 1991, 2005) === Honorary doctorates === Universidad Autónoma de Madrid (2018) Hong Kong University of Science and Technology (2021) == Legacy and endowments == Two endowed funds have been established in Jain's honor at Michigan State University, recognizing his lasting impact on the field and the university. In 2015, a former visiting scholar from Jain's laboratory made an anonymous $400,000 gift to create the Anil K. Jain Endowed Graduate Fellowship, which supports doctoral-level research in pattern recognition, computer vision, and biometric recognition. In 2022, the Anil K. and Nandita K. Jain Endowed Professorship was established through $1 million in contributions from multiple donors, including a substantial gift from the Jain family, to support faculty recruitment and retention in the Department of Computer Science and Engineering. == Selected publications == === Books === 1988. Algorithms For Clustering Data. With Richard C. Dubes. Prentice Hall. 1993. Markov Random Fields: Theory and Applications. With Rama Chellappa eds. Academic Press. 1999. Biometrics: Personal Identification in Networked Society. With Ruud M. Bolle and Sharath Pankanti eds. Springer. 2003. Handbook of Fingerprint Recognition. (2nd edition 2009). With D. Maio, D. Maltoni, S. Prabhakar. Springer. 2005. Handbook of Face Recognition. (2nd edition 2011). With S. Z. Li ed. Springer. 2006. Handbook of Multibiometrics. With A. Ross and K. Nandakumar. Springer. 2007. Handbook of Biometrics. With P. Flynn and A. Ross eds. Springer. 2011. Introduction to Biometrics. With A. Ross and K. Nandakumar. Springer. 2015. Encyclopedia of Biometrics (Second Edition). With Stan Li. Springer. === Research articles === Cross, George R. and Anil K. Jain. "Markov random field texture models". IEEE Transactions on Pattern Analysis and Machine Intelligence (1983): 25–39. Jain, Anil K., and Farshid Farrokhnia. "Unsupervised texture segmentation using Gabor filters". Pattern Recognition 24.12 (1991): 1167–1186. Jain, Anil K., and Douglas Zongker. "Feature selection: Evaluation, application, and small sample performance". IEEE Transactions on Pattern Analysis and Machine Intelligence, 19.2 (1997): 153–158. Jain, Anil K., L. Hong, S. Pankanti, R. Bolle. "An Identity-A
Microsoft Teams
Microsoft Teams is a team collaboration platform developed by Microsoft as part of the Microsoft 365 suite. It offers features such as workspace chat, video conferencing, file storage, and integration with both Microsoft and third-party applications and services. Teams gradually replaced earlier Microsoft messaging and collaboration platforms, including Skype for Business, Skype, Flip, and Microsoft Classroom. The platform saw significant growth during the COVID-19 pandemic, alongside competitors such as Zoom, Slack, and Google Meet, as organizations shifted to remote work and virtual meetings. As of January 2023, Microsoft reported approximately 280 million monthly active users. == History == On August 29, 2007, Microsoft acquired Parlano, the developer of the persistent group chat tool MindAlign. Years later, on March 4, 2016, Microsoft considered acquiring Slack for $8 billion. However, the proposal was reportedly opposed by Bill Gates, who advocated for focusing on enhancing Skype for Business instead. Lu Qi, then executive vice president of Applications and Services, had led the initiative to pursue the Slack acquisition. Following Lu's departure later that year, Microsoft announced Microsoft Teams on November 2, 2016, at an event in New York City, positioning it as a direct competitor to Slack. Teams launched worldwide on March 14, 2017. The service was initially led by corporate vice president Brian MacDonald. In response to the launch, Slack published a full-page advertisement in The New York Times welcoming the competition and outlining its product philosophy. Although Slack was used by 28 companies in the Fortune 100, The Verge wrote that executives would question paying for the service if Teams provides a similar function in their company's existing Office 365 subscription. However, ZDNET noted that the platforms initially served different markets, as Teams did not support external users, making it less appealing to small businesses and freelancers, a limitation Microsoft later addressed. In response to Teams' announcement, Slack deepened in-product integration with Google services. In May 2017, Microsoft announced that Teams would replace Microsoft Classroom in Office 365 Education. A free version of Teams was released on July 12, 2018, offering most core features at no cost, albeit with limits on users and storage. In January 2019, Microsoft introduced updates targeting "Firstline Workers" to improve Teams’ performance across shared or limited-access devices. In September 2019, Microsoft announced the retirement of Skype for Business in favor of Teams, which took effect on July 31, 2021. In early 2020, Microsoft introduced a push-to-talk "Walkie Talkie" feature aimed at firstline workers using smartphones and tablets over Wi-Fi or cellular networks. The COVID-19 pandemic significantly boosted usage of Teams. On March 19, 2020, Microsoft reported 44 million daily active users. In April, the platform logged 4.1 billion meeting minutes in a single day. A public preview of Microsoft Teams for Linux was released in December 2019, but the Linux client was discontinued in 2022. In July 2020, Microsoft shut down its video game livestreaming platform Mixer, and announced that some of its technologies would be repurposed for use in Teams. On February 28, 2025, Microsoft announced that Skype would be fully retired on May 5, 2025, with users given options to export their data or transition to Microsoft Teams. In October 2025, together with other Microsoft 365 suite apps, Teams had its logo updated. == Usage == == Underlying software == Microsoft Teams, as part of the Microsoft 365 suite, utilizes SharePoint and Exchange Online. Each Team, Shared Channel, and Private Channel has its own Microsoft 365 Group and SharePoint Site used for file storage. Messages are stored in Cosmos DB and are journaled to Exchange Online mailboxes. Private messages, including messages in Private Channels, are journaled to the sender and recipients' mailboxes. Public Channel messages are journaled to their corresponding Team's group mailbox, whereas, messages from Shared Channels are journaled to their own mailboxes. Contacts and voicemail are stored in Exchange Online. Microsoft Teams client is a web-based desktop app, originally developed on top of the Electron framework which combines the Chromium rendering engine and the Node.js JavaScript platform. Version 2.0 client was rebuilt using the Evergreen version of Microsoft Edge WebView2 in place of Electron. == Features == === Chats === Teams allows users to communicate in two-way persistent chats with one or multiple participants. Participants can message using text, emojis, stickers and gifs, as well as sharing links and files. In August 2022, the chat feature was updated for "chat with yourself"; allowing for the organization of files, notes, comments, images, and videos within a private chat tab. === Teams === Teams allows communities, groups, or teams to contribute in a shared workspace where messages and digital content on a specific topic are shared. Team members can join through an invitation sent by a team administrator or owner or sharing of a specific URL. Teams for Education allows admins and teachers to set up groups for classes, professional learning communities (PLCs), staff members, and everyone. === Channels === Channels allow team members to communicate without the use of email or group SMS (texting). Users can reply to posts with text, images, GIFs, and image macros. Direct messages send private messages to designated users rather than the entire channel. Connectors can be used within a channel to submit information contacted through a third-party service. Connectors include Mailchimp, Facebook Pages, Twitter, Power BI and Bing News. === Group conversations === Ad-hoc groups can be created to share instant messaging, audio calls (VoIP), and video calls inside the client software. === Telephone replacement === A feature on one of the higher cost licencing tiers allows connectivity to the public switched telephone network (PSTN) telephone system. This allows users to use Teams as if it were a telephone, making and receiving calls over the PSTN, including the ability to host "conference calls" with multiple participants. === Meeting === Meetings can be scheduled with multiple participants able to share audio, video, chat and presented content with all participants. Multiple users can connect via a meeting link. Automated minutes are possible using the recording and transcript features. Teams has a plugin for Microsoft Outlook to schedule a Teams Meeting in Outlook for a specific date and time and invite others to attend. If a meeting is scheduled within a channel, users visiting the channel are able to see if a meeting is in progress. ==== Teams Live Events ==== Teams Live Events replaces Skype Meeting Broadcast for users to broadcast to 10,000 participants on Teams, Yammer, or Microsoft Stream. ==== Breakout Rooms ==== Breakout rooms split a meeting into small groups. This is often utilized for collaboration during trainings or any environment where having all participants speak at once could be disruptive or unfeasible. Breakout rooms can be set by the hosts to a certain length of time, after which all participants will automatically rejoin the main meeting room. ==== Front Row ==== Front Row adjusts the layout of the viewer's screen, placing the speaker or content in the center of the gallery with other meeting participant's video feeds reduced in size and located below the speaker. === Education === Microsoft Teams for Education allows teachers to distribute, provide feedback, and grade student assignments turned in via Teams using the Assignments tab through Office 365 for Education subscribers. Quizzes can also be assigned to students through an integration with Office Forms. === Protocols === Microsoft Teams is based on a number of Microsoft-specific protocols. Video conferences are realized over the protocol MNP24, known from the Skype consumer version. VoIP and video conference clients based on SIP and H.323 need special gateways to connect to Microsoft Teams servers. With the help of Interactive Connectivity Establishment (ICE), clients behind Network address translation routers and restrictive firewalls are also able to connect, if peer-to-peer is not possible. === Integrations === Microsoft Teams has integrations through Microsoft AppSource, its integration marketplace. In 2020, Microsoft partnered with KUDO, a cloud-based solution with language interpretation, to allow integrated language meeting controls. In June 2022, an update was released using AI to improve call audio through the elimination of background feedback loops and cancelling non-vocal audio. == Anti-trust controversy == In July 2023, the European Commission opened an anti-trust investigation into the possibility that Microsoft unfairly used its office suite market power to increase sales of Teams and hurt
General Internet Corpus of Russian
General Internet Corpus of Russian (GICR) is a corpus of Russian internet texts that has been accessible on request through an online query interface since 2013. The corpus includes rich text materials from the blogosphere, social networks, major news sources and literary magazines. == Goals of the project == The project has the status of an educational and scientific one, and many tasks of computational linguistics are solved by independent researchers and research groups with the materials obtained by GICR. While other corpus projects of Russian are focused on fiction and edited texts, General Internet Corpus provides linguists timely opportunity to learn the language as it is, with all the slang and regional peculiarities. Corpus gives the opportunity to carry out research in Linguistic research of a wide range: dialectological research, study of word distribution, study of the language of the social networks, study of the influence of gender, age and other factors on the language, frequency of words, fixed expressions and different constructions, stylistic features of texts of different segments of the Internet, etc. Social media analysis Corpus-based machine learning for evaluating automatic tagging At various times, student papers and independent researches were carried out on the project material by students, graduates and employees of MSU, MIPT, Russian State Humanitarian University, Novosibirsk State University, Higher School of Economics, Russian Academy of Sciences, SFU, CSU, SGMP, IAAS of MSU. Scientific project leaders: Belikov V. - RSUH, Moscow, Russia Selegey V. - RSUH, ABBYY, Moscow, Russia Sharoff S. - RSUH, Moscow, Russia; University of Leeds, UK The organizations involved in support of GICR: Russian State University of Humanities ABBYY Company Moscow Institute of Physics and Technology Skolkovo Institute of Science and Technology == Size and content of the corpus == Corpus size for the summer 2016 is 19.8 billion tokens, of which 49% are from VKontakte, 40% are from LiveJournal, another 4% - from Mail.ru Blogs and News, and 2% - from Russian Magazine Hall. The sources collected in news segment are: RIA Novosti, Regnum, Lenta.ru, Rosbalt. Texts are provided with metamarkup (by date of creation of the text, sex, place and year of birth of the author, Internet genre, etc.); all texts are provided with automatic morphological tagging and lemmatization. Most of the texts collected are of 2013–2014 years of creation, although in some segments, such as in Russian Magazine Hall, there are some texts collected since 1994. GICR is one of the few mega-corpora projects nowadays, which means its available size is reaching several billion of words. == Access == Currently the interface of GICR is in beta stage, so access to the search in the corpora is provided and is free, but is available for researchers on request.