In artificial intelligence and cognitive science, the structure mapping engine (SME) is an implementation in software of an algorithm for analogical matching based on the psychological theory of Dedre Gentner. The basis of Gentner's structure-mapping idea is that an analogy is a mapping of knowledge from one domain (the base) into another (the target). The structure-mapping engine is a computer simulation of the analogy and similarity comparisons. The theory is useful because it ignores surface features and finds matches between potentially very different things if they have the same representational structure. For example, SME could determine that a pen is like a sponge because both are involved in dispensing liquid, even though they do this very differently. == Structure mapping theory == Structure mapping theory is based on the systematicity principle, which states that connected knowledge is preferred over independent facts. Therefore, the structure mapping engine should ignore isolated source-target mappings unless they are part of a bigger structure. The SME, the theory goes, should map objects that are related to knowledge that has already been mapped. The theory also requires that mappings be done one-to-one, which means that no part of the source description can map to more than one item in the target and no part of the target description can be mapped to more than one part of the source. The theory also requires that if a match maps subject to target, the arguments of subject and target must also be mapped. If both these conditions are met, the mapping is said to be "structurally consistent." == Concepts in SME == SME maps knowledge from a source into a target. SME calls each description a dgroup. Dgroups contain a list of entities and predicates. Entities represent the objects or concepts in a description — such as an input gear or a switch. Predicates are one of three types and are a general way to express knowledge for SME. Relation predicates contain multiple arguments, which can be other predicates or entities. An example relation is: (transmit (what from to)). This relation has a functor transmit and takes three arguments: what, from, and to. Attribute predicates are the properties of an entity. An example of an attribute is (red gear) which means that gear has the attribute red. Function predicates map an entity into another entity or constant. An example of a function is (joules power source) which maps the entity power source onto the numerical quantity joules. Functions and attributes have different meanings, and consequently SME processes them differently. For example, in SME's true analogy rule set, attributes differ from functions because they cannot match unless there is a higher-order match between them. The difference between attributes and functions will be explained further in this section's examples. All predicates have four parameters. They have (1) a functor, which identifies it, and (2) a type, which is either relation, attribute, or function. The other two parameters (3 and 4) are for determining how to process the arguments in the SME algorithm. If the arguments have to be matched in order, commutative is false. If the predicate can take any number of arguments, N-ary is false. An example of a predicate definition is: (sme:defPredicate behavior-set (predicate) relation :n-ary? t :commutative? t) The predicate's functor is “behavior-set,” its type is “relation,” and its n-ary and commutative parameters are both set to true. The “(predicate)” part of the definition specifies that there will be one or more predicates inside an instantiation of behavior-set. == Algorithm details == The algorithm has several steps. The first step of the algorithm is to create a set of match hypotheses between source and target dgroups. A match hypothesis represents a possible mapping between any part of the source and the target. This mapping is controlled by a set of match rules. By changing the match rules, one can change the type of reasoning SME does. For example, one set of match rules may perform a kind of analogy called literal similarity, and another performs a kind of analogy called true-analogy. These rules are not the place where domain-dependent information is added, but rather where the analogy process is tweaked, depending on the type of cognitive function the user is trying to emulate. For a given match rule, there are two types of rules that further define how it will be applied: filter rules and intern rules. Intern rules use only the arguments of the expressions in the match hypotheses that the filter rules identify. This limitation makes the processing more efficient by constraining the number of match hypotheses that are generated. At the same time, it also helps to build the structural consistencies that are needed later on in the algorithm. An example of a filter rule from the true-analogy rule set creates match hypotheses between predicates that have the same functor. The true-analogy rule set has an intern rule that iterates over the arguments of any match hypothesis, creating more match hypotheses if the arguments are entities or functions, or if the arguments are attributes and have the same functor. In order to illustrate how the match rules produce match hypotheses consider these two predicates: transmit torque inputgear secondgear (p1) transmit signal switch div10 (p2) Here we use true analogy for the type of reasoning. The filter match rule generates a match between p1 and p2 because they share the same functor, transmit. The intern rules then produce three more match hypotheses: torque to signal, inputgear to switch, and secondgear to div10. The intern rules created these match hypotheses because all the arguments were entities. If the arguments were functions or attributes instead of entities, the predicates would be expressed as: transmit torque (inputgear gear) (secondgear gear) (p3) transmit signal (switch circuit) (div10 circuit) (p4) These additional predicates make inputgear, secondgear, switch, and div10 functions or attributes depending on the value defined in the language input file. The representation also contains additional entities for gear and circuit. Depending on what type inputgear, secondgear, switch, and div10 are, their meanings change. As attributes, each one is a property of the gear or circuit. For example, the gear has two attributes, inputgear and secondgear. The circuit has two attributes, switch and circuit. As functions inputgear, secondgear, switch, and div10 become quantities of the gear and circuit. In this example, the functions inputgear and secondgear now map to the numerical quantities “torque from inputgear” and “torque from secondgear,” For the circuit the quantities map to logical quantity “switch engaged” and the numerical quantity “current count on the divide by 10 counter.” SME processes these differently. It does not allow attributes to match unless they are part of a higher-order relation, but it does allow functions to match, even if they are not part of such a relation. It allows functions to match because they indirectly refer to entities and thus should be treated like relations that involve no entities. However, as next section shows, the intern rules assign lower weights to matches between functions than to matches between relations. The reason SME does not match attributes is because it is trying to create connected knowledge based on relationships and thus satisfy the systematicity principle. For example, if both a clock and a car have inputgear attributes, SME will not mark them as similar. If it did, it would be making a match between the clock and car based on their appearance — not on the relationships between them. When the additional predicates in p3 and p4 are functions, the results from matching p3 and p4 are similar to the results from p1 and p2 except there is an additional match between gear and circuit and the values for the match hypotheses between (inputgear gear) and (switch circuit), and (secondgear gear) and (div10 circuit), are lower. The next section describes the reason for this in more detail. If the inputgear, secondgear, switch, and div10 are attributes instead of entities, SME does not find matches between any of the attributes. It finds matches only between the transmit predicates and between torque and signal. Additionally, the structural-evaluation scores for the remaining two matches decrease. In order to get the two predicates to match, p3 would need to be replaced by p5, which is demonstrated below. transmit torque (inputgear gear) (div10 gear) (p5) Since the true-analogy rule set identifies that the div10 attributes are the same between p5 and p4 and because the div10 attributes are both part of the higher-relation match between torque and signal, SME makes a match between (div10 gear) and (div10 circuit) — which leads to a match between gear and circuit. Being part of a higher-order match is a requiremen
Interlacing (bitmaps)
In computing, interlacing (also known as interleaving) is a method of encoding a bitmap image such that a person who has partially received it sees a degraded copy of the entire image. When communicating over a slow communications link, this is often preferable to seeing a perfectly clear copy of one part of the image, as it helps the viewer decide more quickly whether to abort or continue the transmission. Interlacing is supported by the following formats, where it is optional: GIF interlacing stores the lines in the order 0 , 8 , 16 , … , ( 8 n ) , 4 , 12 , … , ( 8 n + 4 ) , 2 , 6 , 10 , 14 , … , ( 4 n + 2 ) , 1 , 3 , 5 , 7 , 9 , … , ( 2 n + 1 ) . {\displaystyle 0,8,16,\dots ,(8n),\ 4,12,\dots ,(8n+4),\ 2,6,10,14,\dots ,(4n+2),\ 1,3,5,7,9,\dots ,(2n+1).} PNG uses the Adam7 algorithm, which interlaces in both the vertical and horizontal direction. TGA uses two optional interlacing algorithms: Two-way: 0 , 2 , 4 , … , ( 2 n ) , 1 , 3 , … , ( 2 n + 1 ) , {\displaystyle 0,2,4,\dots ,(2n),\ 1,3,\dots ,(2n+1),} And four-way: 0 , 4 , 8 , … , ( 4 n ) , 1 , 5 , … , ( 4 n + 1 ) , 2 , 6 , … , ( 4 n + 2 ) , 3 , 7 , … , ( 4 n + 3 ) . {\displaystyle 0,4,8,\dots ,(4n),\ 1,5,\dots ,(4n+1),\ 2,6,\dots ,\ (4n+2),3,7,\dots ,(4n+3).} JPEG, JPEG 2000, and JPEG XR (actually using a frequency decomposition hierarchy rather than interlacing of pixel values) PGF (also using a frequency decomposition) Interlacing is a form of incremental decoding, because the image can be loaded incrementally. Another form of incremental decoding is progressive scan. In progressive scan the loaded image is decoded line for line, so instead of becoming incrementally clearer it becomes incrementally larger. The main difference between the interlace concept in bitmaps and in video is that even progressive bitmaps can be loaded over multiple frames. For example: Interlaced GIF is a GIF image that seems to arrive on your display like an image coming through a slowly opening Venetian blind. A fuzzy outline of an image is gradually replaced by seven successive waves of bit streams that fill in the missing lines until the image arrives at its full resolution. Interlaced graphics were once widely used in web design and before that in the distribution of graphics files over bulletin board systems and other low-speed communications methods. The practice is much less common today, as common broadband internet connections allow most images to be downloaded to the user's screen nearly instantaneously, and interlacing is usually an inefficient method of encoding images. Interlacing has been criticized because it may not be clear to viewers when the image has finished rendering, unlike non-interlaced rendering, where progress is apparent (remaining data appears as blank). Also, the benefits of interlacing to those on low-speed connections may be outweighed by having to download a larger file, as interlaced images typically do not compress as well.
Mixvoip
Mixvoip S.A. is a Luxembourg-based telecommunications service provider founded in 2008. The company offers IP telephony, high-speed Internet connectivity, and IT solutions to businesses and individuals. == Company history == In November 2017, Mixvoip expanded its operations to Belgium and Germany. At the beginning of 2019, the company acquired the telecommunications provider Voipgate. In December 2019, Mixvoip was named Telecom Company of the Year at the Luxembourg ICT Awards 2019 organized by Farvest and IT One. A 2024 article in Duke described the company's transition during the 2010s from traditional telephony services to cloud-based communication platforms. In the end of 2024, the ILR published the statistics about electronic communications in Luxembourg, including Mixvoip in the fix telephony section. In July 2025, Mixvoip acquired Crossing Telecom. In 2026, Mixvoip acquired Nomado's portfolio.
User-generated content
User-generated content (UGC), alternatively known as user-created content (UCC), is content generated by users of the Internet such as images, videos, audio, text, testimonials, software, and user interactions. Online content aggregation platforms such as social media, discussion forums and wikis by their interactive and social nature, no longer produce multimedia content but provide tools to produce, collaborate, and share a variety of content, which can affect the attitudes and behaviors of the audience in various aspects. This transforms the role of consumers from passive spectators to active participants. User-generated content is used for a wide range of applications, including problem processing, news, entertainment, customer engagement, advertising, gossip, research and more. It is an example of the democratization of content production and the flattening of traditional media hierarchies. The BBC adopted a user-generated content platform for its websites in 2005, and Time magazine named "You" as the Person of the Year in 2006, referring to the rise in the production of UGC on Web 2.0 platforms. CNN also developed a similar user-generated content platform, known as iReport. There are other examples of news channels implementing similar protocols, especially in the immediate aftermath of a catastrophe or terrorist attack. Social media users can provide key eyewitness content and information that may otherwise have been inaccessible. Since 2020, there has been an increasing number of businesses who are utilizing User Generated Content (UGC) to promote their products and services. Several factors significantly influence how UGC is received, including the quality of the content, the credibility of the creator, and viewer engagement. These elements can impact users' perceptions and trust towards the brand, as well as influence the buying intentions of potential customers. UGC has proven to be an effective method for brands to connect with consumers, drawing their attention through the sharing of experiences and information on social media platforms. Due to new media and technology affordances, such as low cost and low barriers to entry, the Internet is an easy platform to create and dispense user-generated content, allowing the dissemination of information at a rapid pace in the wake of an event. == Definition == The advent of user-generated content marked a shift among media organizations from creating online content to providing facilities for amateurs to publish their own content. User-generated content has also been characterized as citizen media as opposed to the "packaged goods media" of the past century. Citizen Media is audience-generated feedback and news coverage. People give their reviews and share stories in the form of user-generated and user-uploaded audio and user-generated video. The former is a two-way process in contrast to the one-way distribution of the latter. Conversational or two-way media is a key characteristic of so-called Web 2.0, which encourages the publishing of one's own content and commenting on other people's content. The role of the passive audience, therefore, has shifted since the birth of new media, and an ever-growing number of participatory users are taking advantage of these interactive opportunities, especially on the Internet, to create independent content. Grassroots experimentation then generated an innovation in sounds, artists, techniques, and associations with audiences, which then are being used in mainstream media. The active, participatory, and creative audience is prevailing today with relatively accessible media, tools, and applications, and its culture is in turn affecting mass media corporations and global audiences. The Organisation for Economic Co-operation and Development (OECD) has defined three core variables for UGC: Accessible Content: User-generated content (UGC) is publicly produced through platforms located on the Internet and is available to any individual browsing such a publicly accessible website or a public social media account. There are other contexts where users must remain in a community or closed group to access and publish on such platforms (for example, wikis). This is a way of differentiating that although the content is accessible to the audience, there are certain restrictions for the users who generates the content. Creative effort: Creative effort was put into creating the work or adapting existing works to construct a new one; i.e. users must add their own value to the work. UGC often also has a collaborative element to it, as is the case with websites that users can edit collaboratively. For example, merely copying a portion of a television show and posting it to an online video website (an activity frequently seen on the UGC sites) would not be considered UGC. However, uploading photographs, expressing one's thoughts in a blog post or creating a new music video could be considered UGC. Yet the minimum amount of creative effort is hard to define and depends on the context. Creation outside of professional routines and practices: User-generated content is generally created outside of professional routines and practices. It often does not have an institutional or a commercial market context. In extreme cases, UGC may be produced by non-professionals without the expectation of profit or remuneration. Motivating factors include connecting with peers, achieving a certain level of fame, notoriety, or prestige, and the desire to express oneself. == Media pluralism == According to Cisco, in 2016 an average of 96,000 petabytes was transferred monthly over the Internet, more than twice as many as in 2012. In 2016, the number of active websites surpassed 1 billion, up from approximately 700 million in 2012. Reaching 1.66 billion daily active users in Q4 2019, Facebook has emerged as the most popular social media platform globally. Other social media platforms are also dominant at the regional level such as: Twitter in Japan, Naver in the Republic of Korea, Instagram (owned by Facebook) and LinkedIn (owned by Microsoft) in Africa, VKontakte (VK) and Odnoklassniki (eng. Classmates) in Russia and other countries in Central and Eastern Europe, WeChat and QQ in China. However, a concentration phenomenon is occurring globally giving dominance to a few online platforms that become popular for some unique features they provide, most commonly for the added privacy they offer users through disappearing messages or end-to-end encryption (e.g. Jami, Signal, Snapchat, Telegram, Viber, and WhatsApp), but they have tended to occupy niches and to facilitate the exchanges of information that remain rather invisible to larger audiences. Production of freely accessible information has been increasing since 2012. In January 2017, Wikipedia had more than 43 million articles, almost twice as many as in January 2012. This corresponded to a progressive diversification of content and an increase in contributions in languages other than English. In 2017, less than 12 percent of Wikipedia content was in English, down from 18 percent in 2012. Graham, Straumann, and Hogan say that the increase in the availability and diversity of content has not radically changed the structures and processes for the production of knowledge. For example, while content on Africa has dramatically increased, a significant portion of this content has continued to be produced by contributors operating from North America and Europe, rather than from Africa itself. == History == The massive, multi-volume Oxford English Dictionary was exclusively composed of user-generated content. In 1857, Richard Chenevix Trench of the London Philological Society sought public contributions throughout the English-speaking world for the creation of the first edition of the OED. As Simon Winchester recounts: So what we're going to do, if I have your agreement that we're going to produce such a dictionary, is that we're going to send out invitations, were going to send these invitations to every library, every school, every university, every book shop that we can identify throughout the English-speaking world... everywhere where English is spoken or read with any degree of enthusiasm, people will be invited to contribute words. And the point is, the way they do it, the way they will be asked and instructed to do it, is to read voraciously and whenever they see a word, whether it's a preposition or a sesquipedalian monster, they are to... if it interests them and if where they read it, they see it in a sentence that illustrates the way that that word is used, offers the meaning of the day to that word, then they are to write it on a slip of paper... the top left-hand side you write the word, the chosen word, the catchword, which in this case is 'twilight'. Then the quotation, the quotation illustrates the meaning of the word. And underneath it, the citation, where it came from, whether it was printed or whether it was in manuscri
Paperless society
A paperless society is a society in which paper communication (written documents, email, letters, etc.) is replaced by electronic communication and storage. The concept was first introduced by Frederick Wilfrid Lancaster in 1978. Furthermore, libraries would no longer be needed to handle printed documents. "Librarians will, in time, become information specialists in a deinstitutionalized setting". Lancaster also stated that both computers and libraries will not always give us the information that other people and living life will. == Literature == Brodman, E. (1979). Review of Toward Paperless Information Systems. Bulletin of the Medical Library Association, 67(4), 437–439. Buckland, M. K. (1980). Review of Toward Paperless Information Systems. Journal of Academic Librarianship, 5(6), 349. Grosch, A. (1979). Review of Toward Paperless Information Systems. College & Research Libraries, 40(1), 88–89. Kohl, D. F. (2004). From the editor . . . The paperless society . . . Not quite yet. Journal of Academic Librarianship, 30(3), 177–178. Lancaster, F. W. (1978a). Toward paperless information systems. New York: Academic Press. Lancaster, F. W. (1980b). The future of the librarian lies outside of the library. Catholic Library World, 51, 388–391. Lancaster, F. W. (1982a). Libraries and librarians in an age of electronics. Arlington, VA: Information Resources Press. Lancaster, F. W. (1982b). The evolving paperless society and its implications for libraries. International Forum on Information and Documentation, 7(4), 3–10. Lancaster, F. W. (1983). Future librarianship: Preparing for an unconventional career. Wilson Library Bulletin, 57, 747–753. Lancaster, F. W. (1985). The paperless society revisited. American Libraries, 16, 553–555. Lancaster, F. W. (1993). Libraries and the future: Essays on the library in the twenty-first century. New York: Haworth Press. Lancaster, F. W. (1999). Second thoughts on the paperless society. Library Journal, 124(15), 48– 50. Lancaster, F. W., & Smith, L. C. (1980c). On-Line systems in the communication process: Projections. Journal of the American Society for Information Science, 31(3), 193–200. Miall, D. S. (2001). The library versus the Internet: Literary studies under siege? Proceedings of the Modern Language Association, 116(5), 1405–1414. Salton, G. (1979). Review of Toward Paperless Information Systems. Journal of Documentation, 35(3), 250–252. Sellen, A. J., & Harper, R. H. R. (2003). The myth of the paperless office. Cambridge, MA: MIT Press. Stevens, N. D. (2006). The fully electronic academic library. College & Research Libraries, 67(1),5–14. Young, Arthur P. (2008).Aftermath of a Prediction: F. W. Lancaster and the Paperless Society LIBRARY TRENDS, 56(4),(“The Evaluation and Transformation of Information Systems: Essays Honoring the Legacy of F. W. Lancaster,” edited by Lorraine J. Haricombe and Keith Russell), pp. 843–858.
ViBe
ViBe is a background subtraction algorithm which has been presented at the IEEE ICASSP 2009 conference and was refined in later publications. More precisely, it is a software module for extracting background information from moving images. It has been developed by Oliver Barnich and Marc Van Droogenbroeck of the Montefiore Institute, University of Liège, Belgium. ViBe is patented: the patent covers various aspects such as stochastic replacement, spatial diffusion, and non-chronological handling. ViBe is written in the programming language C, and has been implemented on CPU, GPU and FPGA. == Technical description == Source: === Pixel model and classification process === Many advanced techniques are used to provide an estimate of the temporal probability density function (pdf) of a pixel x. ViBe's approach is different, as it imposes the influence of a value in the polychromatic space to be limited to the local neighborhood. In practice, ViBe does not estimate the pdf, but uses a set of previously observed sample values as a pixel model. To classify a value pt(x), it is compared to its closest values among the set of samples. === Model update: Sample values lifespan policy === ViBe ensures a smooth exponentially decaying lifespan for the sample values that constitute the pixel models. This makes ViBe able to successfully deal with concomitant events with a single model of a reasonable size for each pixel. This is achieved by choosing, randomly, which sample to replace when updating a pixel model. Once the sample to be discarded has been chosen, the new value replaces the discarded sample. The pixel model that would result from the update of a given pixel model with a given pixel sample cannot be predicted since the value to be discarded is chosen at random. === Model update: Spatial Consistency === To ensure the spatial consistency of the whole image model and handle practical situations such as small camera movements or slowly evolving background objects, ViBe uses a technique similar to that developed for the updating process in which it chooses at random and update a pixel model in the neighborhood of the current pixel. By denoting NG(x) and p(x) respectively the spatial neighborhood of a pixel x and its value, and assuming that it was decided to update the set of samples of x by inserting p(x), then ViBe also use this value p(x) to update the set of samples of one of the pixels in the neighborhood NG(x), chosen at random. As a result, ViBe is able to produce spatially coherent results directly without the use of any post-processing method. === Model initialization === Although the model could easily recover from any type of initialization, for example by choosing a set of random values, it is convenient to get an accurate background estimate as soon as possible. Ideally a segmentation algorithm would like to be able to segment the video sequences starting from the second frame, the first frame being used to initialize the model. Since no temporal information is available prior to the second frame, ViBe populates the pixel models with values found in the spatial neighborhood of each pixel; more precisely, it initializes the background model with values taken randomly in each pixel neighborhood of the first frame. The background estimate is therefore valid starting from the second frame of a video sequence.
Photonically Optimized Embedded Microprocessors
The Photonically Optimized Embedded Microprocessors (POEM) is DARPA program. It should demonstrate photonic technologies that can be integrated within embedded microprocessors and enable energy-efficient high-capacity communications between the microprocessor and DRAM. For realizing POEM technology CMOS and DRAM-compatible photonic links should operate at high bit-rates with very low power dissipation. == Current research == Currently research in this field is at University of Colorado, Berkley University, and Nanophotonic Systems Laboratory ( Ultra-Efficient CMOS-Compatible Grating Coupler Design).