AI Detector Similar To Turnitin

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  Cybernetic Serendipity

  Cybernetic Serendipity was an exhibition of cybernetic art curated by Jasia Reichardt, shown at the Institute of Contemporary Arts, London, England, from 2 August to 20 October 1968, and then toured across the United States. Two stops in the United States were the Corcoran Annex (Corcoran Gallery of Art), Washington, D.C., from 16 July to 31 August 1969, and the newly opened Exploratorium in San Francisco, from 1 November to 18 December 1969. == Content == One part of the exhibition was concerned with algorithms and devices for generating music. Some exhibits were pamphlets describing the algorithms, whilst others showed musical notation produced by computers. Devices made musical effects and played tapes of sounds made by computers. Peter Zinovieff lent part of his studio equipment - visitors could sing or whistle a tune into a microphone and his equipment would improvise a piece of music based on the tune. Another part described computer projects such as Gustav Metzger's self-destructive Five Screens With Computer, a design for a new hospital, a computer programmed structure, and dance choreography. The machines and installations were a very noticeable part of the exhibition. Gordon Pask produced a collection of large mobiles (Colloquy of Mobiles (1968)) with interacting parts that let the viewers join in the conversation. Many machines formed kinetic environments or displayed moving images. Bruce Lacey contributed his radio-controlled robots and a light-sensitive owl. Nam June Paik was represented by Robot K-456 and televisions with distorted images. Jean Tinguely provided two of his painting machines. Edward Ihnatowicz's biomorphic hydraulic ear (Sound Activated Mobile (SAM, 1968)) turned toward sounds and John Billingsley's Albert 1967 turned to face light. Wen-Ying Tsai presented his interactive cybernetic sculptures of vibrating stainless-steel rods, stroboscopic light, and audio feedback control. Several artists exhibited machines that drew patterns that the visitor could take away, or involved visitors in games. Cartoonist Rowland Emett designed the mechanical computer Forget-me-not, which was commissioned by Honeywell. Another section explored the computer's ability to produce text - both essays and poetry. Different programs produced Haiku, children's stories, and essays. One of the first computer-generated poems, by Alison Knowles and James Tenney, was included in the exhibition and catalogue. Computer-generated movies were represented by John Whitney's Permutations and a Bell Labs movie on their technology for producing movies. Some samples included images of tesseracts rotating in four dimensions, a satellite orbiting the Earth, and an animated data structure. Computer graphics were also represented, including pictures produced on cathode ray oscilloscopes and digital plotters. There was a variety of posters and graphics demonstrating the power of computers to do complex (and apparently random) calculations. Other graphics showed a simulated Mondrian and the iconic decreasing squares spiral that appeared on the exhibition's poster and book. The Boeing Company exhibited their use of wireframe graphics. The innovative computer-generated sculpture, Quad 1, was displayed at the Cybernetic Serendipity exhibit. Created by the American abstract expressionist sculptor, Robert Mallary, in 1968, Quad 1 is widely believed to be the world's first Computer Aided Design sculpture. Keith Albarn & Partners contributed to the design of the exhibition. Reflecting the prominence of music in the show, a ten-track album Cybernetic Serendipity Music was released by the ICA to accompany the show. Artists featured included Iannis Xenakis, John Cage, and Peter Zinovieff, a detail of whose graphic score for 'Four Sacred April Rounds’ (1968) was used as the cover artwork. == Attendance == Time magazine noted that there had been 40,000 visitors to the London exhibition. Other reports suggested visitor numbers were as high as 44,000 to 60,000. However, the ICA did not accurately count visitors. == After-effects == The exhibition provided the energy for the formation of British Computer Arts Society which continued to explore the interaction between science, technology and art, and put on exhibitions (for example Event One at the Royal College of Art). Several pieces were purchased by the Exploratorium in 1971, some of which are on display to this day. In 2014 the ICA held a retrospective exhibition Cybernetic Serendipity: A Documentation which included documents, installation photographs, press reviews and publications and a series of discussions in one of which Peter Zinovieff took part. To coincide with the exhibition, Cybernetic Serendipity Music was re-released as a limited-edition vinyl LP by The Vinyl Factory. The Victoria and Albert Museum marked the 50th anniversary with an exhibition in 2018 entitled "Chance and Control: Art in the Age of Computers". The V&A exhibition included many works by artists who featured in the original ICA show, plus related ephemera. "Chance and Control" subsequently toured to Chester Visual Arts and Firstsite, Colchester. In 2020, The Centre Pompidou exhibited the replica of Gordon Pask's 1968 Colloquy of Mobiles, reproduced by Paul Pangaro and TJ McLeish in 2018. In 2022 the Australian National University's School of Cybernetics launched the school by presenting an exhibition Australian Cybernetic: a point through time. The exhibition included works from Cybernetic Serendipity (1968), Australia ‘75: Festival of Creative Arts and Science (1975), and contemporary pieces curated by the School of Cybernetics. In describing Reichardt's Cybernetic Serendipity exhibition the school stated that it "represented points of expanding the cybernetic imagination" and was a "ground-breaking" "glimpse of a future in which computers were entangled with people and cultures, and through this she fashioned a blueprint for the future of computing that has since inspired generations".

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  Gorn address

  A Gorn address (Gorn, 1967) is a method of identifying and addressing any node within a tree data structure. This notation is often used for identifying nodes in a parse tree defined by phrase structure rules. The Gorn address is a sequence of zero or more integers conventionally separated by dots, e.g., 0 or 1.0.1. The root which Gorn calls can be regarded as the empty sequence. And the j {\displaystyle j} -th child of the i {\displaystyle i} -th child has an address i . j {\displaystyle i.j} , counting from 0. It is named after American computer scientist Saul Gorn.

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  INDECT

  INDECT is a research project in the area of intelligent security systems performed by several European universities since 2009 and funded by the European Union. The purpose of the project is to involve European scientists and researchers in the development of solutions to and tools for automatic threat detection through e.g. processing of CCTV camera data streams, standardization of video sequence quality for user applications, threat detection in computer networks as well as data and privacy protection. The area of research, applied methods, and techniques are described in the public deliverables which are available to the public on the project's website. Practically, all information related to the research is public. Only documents that comprise information related to financial data or information that could negatively influence the competitiveness and law enforcement capabilities of parties involved in the project are not published. This follows regulations and practices applied in EU research projects. == Application and target users == The main end-user of INDECT solutions are police forces and security services. The principle of operation of the project is detecting threats and identifying sources of threats, without monitoring and searching for particular citizens or groups of citizens. Then, the system operator (i.e. police officer) decides whether an intervention of services responsible for public security are required or not. Further investigation eventually leading to persons related to threats is performed, preserving the presumption of innocence, based on existing procedures already used by police services and prosecutors. As it can be found in the project deliverables, INDECT does not involve storage of personal data (such as names, addresses, identity document numbers, etc.). A similar, behavior-based surveillance program was SAMURAI (Suspicious and Abnormal behavior Monitoring Using a netwoRk of cAmeras & sensors for sItuation awareness enhancement). == Expected results == The main expected results of the INDECT project are: Trial of intelligent analysis of video and audio data for threat detection in urban environments Creation of tools and technology for privacy and data protection during storage and transmission of information using quantum cryptography and new methods of digital watermarking Performing computer-aided detection of threats and targeted crimes in Internet resources with privacy-protecting solutions Construction of a search engine for rapid semantic search based on watermarking of content related to child pornography and human organ trafficking Implementation of a distributed computer system that is capable of effective intelligent processing == Controversy == Some media and other sources accuse INDECT of privacy abuse, collecting personal data, and keeping information from the public. Consequently, these issues have been commented and discussed by some Members of the European Parliament. As seen in the project's documentation, INDECT does not involve mobile phone tracking or call interception. The rumors about testing INDECT during 2012 UEFA European Football Championship also turned out to be false. The mid-term review of the Seventh Framework Programme to the European Parliament strongly urges the European Commission to immediately make all documents available and to define a clear and strict mandate for the research goal, the application, and the end users of INDECT, and stresses a thorough investigation of the possible impact on fundamental rights. Nevertheless, according to Mr. Paweł Kowal, MEP, the project had the ethical review on 15 March 2011 in Brussels with the participation of ethics experts from Austria, France, Netherlands, Germany and Great Britain.

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  Integrated writing environment

  An integrated writing environment (IWE) is software that provides comprehensive writing and knowledge management functionality for writers and information workers. IWEs enable writers and information workers to perform a variety of tasks related to the document in the IWE in a single environment. This provides a distraction-free workspace and streamlined writing experience. IWEs provide similar efficiency and functionality benefits to writers and information professionals that integrated development environments (IDEs) provide to software developers. == Overview == IWEs are designed to maximize productivity and help improve the quality of written work by integrating together tools that allow users to work effectively in a single application. The IWE features may include integrated content search, reversion management, outlining, note management, and reference management, as may be suitable for the target field of use. == List of IWEs == Celtx This IWE is intended for screenplay writers and has screenplay writing and management tools. Celtex provides tools for the pre-production work phase, story development, storyboarding, script breakdowns, production scheduling, and reports. Scrivener This IWE targets novel, research paper, and script writing. Scrivener provides tools to organize notes and research documents for easy access and referencing. After completing the writing, Scrivener allows the user to export the document to formats supported by common word processors, such as Microsoft Word. TeXstudio This IWE targets LaTeX documents and provides interactive spelling checker, code folding, and syntax highlighting.

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  Color

  Color (or colour in Commonwealth English) is the visual perception produced by the activation of the different types of cone cells in the eye caused by light. Though color is not an inherent property of matter, color perception is related to an object's light absorption, emission, reflection and transmission. For most humans, visible wavelengths of light are the ones perceived in the visible light spectrum, with three types of cone cells (trichromacy). Other animals may have a different number of cone cell types or have eyes sensitive to different wavelengths, such as bees that can distinguish ultraviolet, and thus have a different color sensitivity range. Animal perception of color originates from different light wavelength or spectral sensitivity in cone cell types, which is then processed by the brain. Colors have perceived properties such as hue, colorfulness, and lightness. Colors can also be additively mixed (mixing light) or subtractively mixed (mixing pigments). If one color is mixed in the right proportions, because of metamerism, they may look the same as another stimulus with a different reflection or emission spectrum. For convenience, colors can be organized in a color space, which when being abstracted as a mathematical color model can assign each region of color with a corresponding set of numbers. Thus, color spaces are an essential tool for color reproduction in print, photography, computer monitors, and television. Some of the most well-known color models and color spaces are RGB, CMYK, HSL/HSV, CIE Lab, and YCbCr/YUV. Because the perception of color is an important aspect of human life, different colors have been associated with emotions, activity, and nationality. Names of color regions in different cultures can have different, sometimes overlapping areas. In visual arts, color theory is used to govern the use of colors in an aesthetically pleasing and harmonious way. The theory of color includes the color complements; color balance; and classification of primary colors, secondary colors, and tertiary colors. The study of colors in general is called color science. == Physical properties == Electromagnetic radiation is characterized by its wavelength (or frequency) and its intensity. When the wavelength is within the visible spectrum (the range of wavelengths humans can perceive, approximately from 390 nm to 700 nm), it is known as "visible light". Most light sources emit light at many different wavelengths; a source's spectrum is a distribution giving its intensity at each wavelength. Although the spectrum of light arriving at the eye from a given direction determines the color sensation in that direction, there are many more possible spectral combinations than color sensations. In fact, one may formally define a color as a class of spectra that give rise to the same color sensation, although such classes would vary widely among different animal species, and to a lesser extent among individuals within the same species. In each such class, the members are called metamers of the color in question. This effect can be visualized by comparing the light sources' spectral power distributions and the resulting colors. === Spectral colors === The familiar colors of the rainbow in the spectrum—named using the Latin word for appearance or apparition by Isaac Newton in 1671—include all those colors that can be produced by visible light of a single wavelength only, the pure spectral or monochromatic colors. The spectrum above shows approximate wavelengths (in nm) for spectral colors in the visible range. Spectral colors have 100% purity, and are fully saturated. A complex mixture of spectral colors can be used to describe any color, which is the definition of a light power spectrum. The spectral colors form a continuous spectrum, and how it is divided into distinct colors linguistically is a matter of culture and historical contingency. Despite the ubiquitous ROYGBIV mnemonic used to remember the spectral colors in English, the inclusion or exclusion of colors is contentious, with disagreement often focused on indigo and cyan. Even if the subset of color terms is agreed, their wavelength ranges and borders between them may not be. The intensity of a spectral color, relative to the context in which it is viewed, may alter its perception considerably. For example, a low-intensity orange-yellow is brown, and a low-intensity yellow-green is olive green. Additionally, hue shifts towards yellow or blue happen if the intensity of a spectral light is increased; this is called Bezold–Brücke shift. In color models capable of representing spectral colors, such as CIELUV, a spectral color has the maximal saturation. In Helmholtz coordinates, this is described as 100% purity. === Color of objects === The physical color of an object depends on how it absorbs and scatters light. Most objects scatter light to some degree and do not reflect or transmit light specularly like glasses or mirrors. A transparent object allows almost all light to transmit or pass through, thus transparent objects are perceived as colorless. Conversely, an opaque object does not allow light to transmit through and instead absorbs or reflects the light it receives. Like transparent objects, translucent objects allow light to transmit through, but translucent objects are seen colored because they scatter or absorb certain wavelengths of light via internal scattering. The absorbed light is often dissipated as heat. == Color vision == === Development of theories of color vision === Although Aristotle and other ancient scientists had already written on the nature of light and color vision, it was not until Isaac Newton that light was identified as the source of the color sensation. In 1810, Johann Wolfgang von Goethe published his comprehensive Theory of Colors in which he provided a rational description of color experience, which "tells us how it originates, not what it is". In 1801, Thomas Young proposed his trichromatic theory, to explain how a wide spectrum of different wavelengths could be detected by the human eye. It would be unreasonable to suppose that the human eye contained hundreds of different receptors each responding to the presence of a specific wavelength. Instead, he suggested that the human experience of color derives from a complex interaction and mixing from the output three receptors. This theory was later confirmed by James Clerk Maxwell and refined by Hermann von Helmholtz. Maxwell experimentally demonstrated that any color could be matched with a combination of three lights. As Helmholtz puts it, "the principles of Newton's law of mixture were experimentally confirmed by Maxwell in 1856. Young's theory of color sensations, like so much else that this marvelous investigator achieved in advance of his time, remained unnoticed until Maxwell directed attention to it." At the same time as Helmholtz, Ewald Hering developed the opponent process theory of color, noting that color blindness and afterimages typically come in opponent pairs (red-green, blue-orange, yellow-violet, and black-white). Ultimately these two theories were synthesized in 1957 by Hurvich and Jameson, who showed that retinal processing corresponds to the trichromatic theory, while processing at the level of the lateral geniculate nucleus corresponds to the opponent theory. In 1931, the International Commission on Illumination (CIE), an international group of experts, developed a mathematical color model which mapped out the space of observable colors, allowing every individual color able to be specified with a set of three numbers. === Color in the eye === The ability of the human eye to distinguish colors is based upon the varying sensitivity of different cells in the retina to light of different wavelengths. Humans are trichromatic—the retina contains three types of color receptor cells, or cones. One type, relatively distinct from the other two, is most responsive to light that is perceived as blue or blue-violet, with wavelengths around 450 nm; cones of this type are sometimes called short-wavelength cones or S cones (or misleadingly, blue cones). The other two types are closely related genetically and chemically: middle-wavelength cones, M cones, or green cones are most sensitive to light perceived as green, with wavelengths around 540 nm, while the long-wavelength cones, L cones, or red cones, are most sensitive to light that is perceived as greenish yellow, with wavelengths around 570 nm. Light, no matter how complex its composition of wavelengths, is reduced to three color components by the eye. Each cone type adheres to the principle of univariance, which is that each cone's output is determined by the amount of light that falls on it over all wavelengths. For each location in the visual field, the three types of cones yield three signals based on the extent to which each is stimulated. These amounts of stimulation are sometimes called tristimulus values. The response cu

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  Brave Leo

  Brave Leo is a large language model-based chatbot developed by Brave Software and included with the Brave browser. == History == In November 2023, the company said versions for iOS and Android would be available "in the coming months". == Features == Since January 2024, Leo has used the open-source Mixtral 8x7B from Mistral AI as its default large language model, in addition to LLaMA 2 from Meta Platforms and Claude from Anthropic, both of which have been used previously. Leo can suggest follow-up questions, and summarize webpages, PDFs, and videos. Leo has a $15 (US) per month premium version that enables more requests and uses larger LLMs. == Privacy == The answers given by Leo are not saved. Brave uses the slogan Love Privacy to emphasize its focus on user privacy and data protection. The phrase has been featured in Brave's official marketing campaigns and has been cited in media coverage of the browser's privacy-first approach. == Controversies == In 2023, PC World reported that Leo evades questions about US elections.

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  Randomized Hough transform

  Hough transforms are techniques for object detection, a critical step in many implementations of computer vision, or data mining from images. Specifically, the Randomized Hough transform is a probabilistic variant to the classical Hough transform, and is commonly used to detect curves (straight line, circle, ellipse, etc.) The basic idea of Hough transform (HT) is to implement a voting procedure for all potential curves in the image, and at the termination of the algorithm, curves that do exist in the image will have relatively high voting scores. Randomized Hough transform (RHT) is different from HT in that it tries to avoid conducting the computationally expensive voting process for every nonzero pixel in the image by taking advantage of the geometric properties of analytical curves, and thus improve the time efficiency and reduce the storage requirement of the original algorithm. == Motivation == Although Hough transform (HT) has been widely used in curve detection, it has two major drawbacks: First, for each nonzero pixel in the image, the parameters for the existing curve and redundant ones are both accumulated during the voting procedure. Second, the accumulator array (or Hough space) is predefined in a heuristic way. The more accuracy needed, the higher parameter resolution should be defined. These two needs usually result in a large storage requirement and low speed for real applications. Therefore, RHT was brought up to tackle this problem. == Implementation == In comparison with HT, RHT takes advantage of the fact that some analytical curves can be fully determined by a certain number of points on the curve. For example, a straight line can be determined by two points, and an ellipse (or a circle) can be determined by three points. The case of ellipse detection can be used to illustrate the basic idea of RHT. The whole process generally consists of three steps: Fit ellipses with randomly selected points. Update the accumulator array and corresponding scores. Output the ellipses with scores higher than some predefined threshold. === Ellipse fitting === One general equation for defining ellipses is: a ( x − p ) 2 + 2 b ( x − p ) ( y − q ) + c ( y − q ) 2 = 1 {\displaystyle a(x-p)^{2}+2b(x-p)(y-q)+c(y-q)^{2}=1} with restriction: a c − b 2 > 0 {\displaystyle ac-b^{2}>0} However, an ellipse can be fully determined if one knows three points on it and the tangents in these points. RHT starts by randomly selecting three points on the ellipse. Let them be X 1 {\displaystyle X_{1}} , X 2 {\displaystyle X_{2}} and X 3 {\displaystyle X_{3}} . The first step is to find the tangents of these three points. They can be found by fitting a straight line using least squares technique for a small window of neighboring pixels. The next step is to find the intersection points of the tangent lines. This can be easily done by solving the line equations found in the previous step. Then let the intersection points be T 12 {\displaystyle T_{12}} and T 23 {\displaystyle T_{23}} , the midpoints of line segments X 1 X 2 {\displaystyle X_{1}X_{2}} and X 2 X 3 {\displaystyle X_{2}X_{3}} be M 12 {\displaystyle M_{12}} and M 23 {\displaystyle M_{23}} . Then the center of the ellipse will lie in the intersection of T 12 M 12 {\displaystyle T_{12}M_{12}} and T 23 M 23 {\displaystyle T_{23}M_{23}} . Again, the coordinates of the intersected point can be determined by solving line equations and the detailed process is skipped here for conciseness. Let the coordinates of ellipse center found in previous step be ( x 0 , y 0 ) {\displaystyle (x_{0},y_{0})} . Then the center can be translated to the origin with x ′ = x − x 0 {\displaystyle x'=x-x_{0}} and y ′ = y − y 0 {\displaystyle y'=y-y_{0}} so that the ellipse equation can be simplified to: a x ′ 2 + 2 b x ′ y ′ + c y ′ 2 = 1 {\displaystyle ax'^{2}+2bx'y'+cy'^{2}=1} Now we can solve for the rest of ellipse parameters: a {\displaystyle a} , b {\displaystyle b} and c {\displaystyle c} by substituting the coordinates of X 1 {\displaystyle X_{1}} , X 2 {\displaystyle X_{2}} and X 3 {\displaystyle X_{3}} into the equation above. === Accumulating === With the ellipse parameters determined from previous stage, the accumulator array can be updated correspondingly. Different from classical Hough transform, RHT does not keep "grid of buckets" as the accumulator array. Rather, it first calculates the similarities between the newly detected ellipse and the ones already stored in accumulator array. Different metrics can be used to calculate the similarity. As long as the similarity exceeds some predefined threshold, replace the one in the accumulator with the average of both ellipses and add 1 to its score. Otherwise, initialize this ellipse to an empty position in the accumulator and assign a score of 1. === Termination === Once the score of one candidate ellipse exceeds the threshold, it is determined as existing in the image (in other words, this ellipse is detected), and should be removed from the image and accumulator array so that the algorithm can detect other potential ellipses faster. The algorithm terminates when the number of iterations reaches a maximum limit or all the ellipses have been detected. Pseudo code for RHT: while (we find ellipses AND not reached the maximum epoch) { for (a fixed number of iterations) { Find a potential ellipse. if (the ellipse is similar to an ellipse in the accumulator) then Replace the one in the accumulator with the average of two ellipses and add 1 to the score; else Insert the ellipse into an empty position in the accumulator with a score of 1; } Select the ellipse with the best score and save it in a best ellipse table; Eliminate the pixels of the best ellipse from the image; Empty the accumulator; }

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  Uncertain data

  In computer science, uncertain data is data that contains noise that makes it deviate from the correct, intended or original values. In the age of big data, uncertainty or data veracity is one of the defining characteristics of data. Data is constantly growing in volume, variety, velocity and uncertainty (1/veracity). Uncertain data is found in abundance today on the web, in sensor networks, within enterprises both in their structured and unstructured sources. For example, there may be uncertainty regarding the address of a customer in an enterprise dataset, or the temperature readings captured by a sensor due to aging of the sensor. In 2012 IBM called out managing uncertain data at scale in its global technology outlook report that presents a comprehensive analysis looking three to ten years into the future seeking to identify significant, disruptive technologies that will change the world. In order to make confident business decisions based on real-world data, analyses must necessarily account for many different kinds of uncertainty present in very large amounts of data. Analyses based on uncertain data will have an effect on the quality of subsequent decisions, so the degree and types of inaccuracies in this uncertain data cannot be ignored. Uncertain data is found in the area of sensor networks; text where noisy text is found in abundance on social media, web and within enterprises where the structured and unstructured data may be old, outdated, or plain incorrect; in modeling where the mathematical model may only be an approximation of the actual process. When representing such data in a database, an appropriate uncertain database model needs to be selected. == Example data model for uncertain data == One way to represent uncertain data is through probability distributions. Let us take the example of a relational database. There are three main ways to do represent uncertainty as probability distributions in such a database model. In attribute uncertainty, each uncertain attribute in a tuple is subject to its own independent probability distribution. For example, if readings are taken of temperature and wind speed, each would be described by its own probability distribution, as knowing the reading for one measurement would not provide any information about the other. In correlated uncertainty, multiple attributes may be described by a joint probability distribution. For example, if readings are taken of the position of an object, and the x- and y-coordinates stored, the probability of different values may depend on the distance from the recorded coordinates. As distance depends on both coordinates, it may be appropriate to use a joint distribution for these coordinates, as they are not independent. In tuple uncertainty, all the attributes of a tuple are subject to a joint probability distribution. This covers the case of correlated uncertainty, but also includes the case where there is a probability of a tuple not belonging in the relevant relation, which is indicated by all the probabilities not summing to one. For example, assume we have the following tuple from a probabilistic database: Then, the tuple has 10% chance of not existing in the database.

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  Pandorabots

  Pandorabots, Inc. is an artificial intelligence company that runs a web service for building and deploying chatbots. Pandorabots implements and supports development of the Artificial Intelligence Markup Language and makes portions of its code accessible for free. The Pandorabots Platform is "one of the oldest and largest chatbot hosting services in the world", allowing creation of virtual agents to hold human-like text or voice chats with consumers. The platform is written in Allegro Common LISP. == Use Cases == Common use cases include advertising, virtual assistance, e-learning, entertainment and education. The platform has also been used by academics and universities use the platform for teaching and research.

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  Inception (deep learning architecture)

  Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern CNN. == Version history == === Inception v1 === In 2014, a team at Google developed the GoogLeNet architecture, an instance of which won the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The name came from the LeNet of 1998, since both LeNet and GoogLeNet are CNNs. They also called it "Inception" after a "we need to go deeper" internet meme, a phrase from Inception (2010) the film. Because later, more versions were released, the original Inception architecture was renamed again as "Inception v1". The models and the code were released under Apache 2.0 license on GitHub. The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules". The original paper stated that Inception modules are a "logical culmination" of Network in Network and (Arora et al, 2014). Since Inception v1 is deep, it suffered from the vanishing gradient problem. The team solved it by using two "auxiliary classifiers", which are linear-softmax classifiers inserted at 1/3-deep and 2/3-deep within the network, and the loss function is a weighted sum of all three: L = 0.3 L a u x , 1 + 0.3 L a u x , 2 + L r e a l {\displaystyle L=0.3L_{aux,1}+0.3L_{aux,2}+L_{real}} These were removed after training was complete. This was later solved by the ResNet architecture. The architecture consists of three parts stacked on top of one another: The stem (data ingestion): The first few convolutional layers perform data preprocessing to downscale images to a smaller size. The body (data processing): The next many Inception modules perform the bulk of data processing. The head (prediction): The final fully-connected layer and softmax produces a probability distribution for image classification. This structure is used in most modern CNN architectures. === Inception v2 === Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. It had 13.6 million parameters. It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used. === Inception v3 === Inception v3 was released in 2016. It improves on Inception v2 by using factorized convolutions. As an example, a single 5×5 convolution can be factored into 3×3 stacked on top of another 3×3. Both has a receptive field of size 5×5. The 5×5 convolution kernel has 25 parameters, compared to just 18 in the factorized version. Thus, the 5×5 convolution is strictly more powerful than the factorized version. However, this power is not necessarily needed. Empirically, the research team found that factorized convolutions help. It also uses a form of dimension-reduction by concatenating the output from a convolutional layer and a pooling layer. As an example, a tensor of size 35 × 35 × 320 {\displaystyle 35\times 35\times 320} can be downscaled by a convolution with stride 2 to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} , and by maxpooling with pool size 2 × 2 {\displaystyle 2\times 2} to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} . These are then concatenated to 17 × 17 × 640 {\displaystyle 17\times 17\times 640} . Other than this, it also removed the lowest auxiliary classifier during training. They found that the auxiliary head worked as a form of regularization. They also proposed label-smoothing regularization in classification. For an image with label c {\displaystyle c} , instead of making the model to predict the probability distribution δ c = ( 0 , 0 , … , 0 , 1 ⏟ c -th entry , 0 , … , 0 ) {\displaystyle \delta _{c}=(0,0,\dots ,0,\underbrace {1} _{c{\text{-th entry}}},0,\dots ,0)} , they made the model predict the smoothed distribution ( 1 − ϵ ) δ c + ϵ / K {\displaystyle (1-\epsilon )\delta _{c}+\epsilon /K} where K {\displaystyle K} is the total number of classes. === Inception v4 === In 2017, the team released Inception v4, Inception ResNet v1, and Inception ResNet v2. Inception v4 is an incremental update with even more factorized convolutions, and other complications that were empirically found to improve benchmarks. Inception ResNet v1 and v2 are both modifications of Inception v4, where residual connections are added to each Inception module, inspired by the ResNet architecture. === Xception === Xception ("Extreme Inception") was published in 2017. It is a linear stack of depthwise separable convolution layers with residual connections. The design was proposed on the hypothesis that in a CNN, the cross-channels correlations and spatial correlations in the feature maps can be entirely decoupled. Training each network took 3 days on 60 K80 GPUs, or approximately 0.5 petaFLOP-days.

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  Stereo cameras

  The stereo cameras approach is a method of distilling a noisy video signal into a coherent data set that a computer can begin to process into actionable symbolic objects, or abstractions. Stereo cameras is one of many approaches used in the broader fields of computer vision and machine vision. == Calculation == In this approach, two cameras with a known physical relationship (i.e. a common field of view the cameras can see, and how far apart their focal points sit in physical space) are correlated via software. By finding mappings of common pixel values, and calculating how far apart these common areas reside in pixel space, a rough depth map can be created. This is very similar to how the human brain uses stereoscopic information from the eyes to gain depth cue information, i.e. how far apart any given object in the scene is from the viewer. The camera attributes must be known, focal length and distance apart etc., and a calibration done. Once this is completed, the systems can be used to sense the distances of objects by triangulation. Finding the same singular physical point in the two left and right images is known as the correspondence problem. Correctly locating the point gives the computer the capability to calculate the distance that the robot or camera is from the object. On the BH2 Lunar Rover the cameras use five steps: a bayer array filter, photometric consistency dense matching algorithm, a Laplace of Gaussian (LoG) edge detection algorithm, a stereo matching algorithm and finally uniqueness constraint. == Uses == This type of stereoscopic image processing technique is used in applications such as 3D reconstruction, robotic control and sensing, crowd dynamics monitoring and off-planet terrestrial rovers; for example, in mobile robot navigation, tracking, gesture recognition, targeting, 3D surface visualization, immersive and interactive gaming. Although the Xbox Kinect sensor is also able to create a depth map of an image, it uses an infrared camera for this purpose, and does not use the dual-camera technique. Other approaches to stereoscopic sensing include time of flight sensors and ultrasound.

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  Deep learning in photoacoustic imaging

  Photoacoustic imaging (PA) is based on the photoacoustic effect, in which optical absorption causes a rise in temperature, which causes a subsequent rise in pressure via thermo-elastic expansion. This pressure rise propagates through the tissue and is sensed via ultrasonic transducers. Due to the proportionality between the optical absorption, the rise in temperature, and the rise in pressure, the ultrasound pressure wave signal can be used to quantify the original optical energy deposition within the tissue. Photoacoustic imaging has applications of deep learning in both photoacoustic computed tomography (PACT) and photoacoustic microscopy (PAM). PACT utilizes wide-field optical excitation and an array of unfocused ultrasound transducers. Similar to other computed tomography methods, the sample is imaged at multiple view angles, which are then used to perform an inverse reconstruction algorithm based on the detection geometry (typically through universal backprojection, modified delay-and-sum, or time reversal ) to elicit the initial pressure distribution within the tissue. PAM on the other hand uses focused ultrasound detection combined with weakly focused optical excitation (acoustic resolution PAM or AR-PAM) or tightly focused optical excitation (optical resolution PAM or OR-PAM). PAM typically captures images point-by-point via a mechanical raster scanning pattern. At each scanned point, the acoustic time-of-flight provides axial resolution while the acoustic focusing yields lateral resolution. == Applications of deep learning in PACT == The first application of deep learning in PACT was by Reiter et al. in which a deep neural network was trained to learn spatial impulse responses and locate photoacoustic point sources. The resulting mean axial and lateral point location errors on 2,412 of their randomly selected test images were 0.28 mm and 0.37 mm respectively. After this initial implementation, the applications of deep learning in PACT have branched out primarily into removing artifacts from acoustic reflections, sparse sampling, limited-view, and limited-bandwidth. There has also been some recent work in PACT toward using deep learning for wavefront localization. There have been networks based on fusion of information from two different reconstructions to improve the reconstruction using deep learning fusion based networks. === Using deep learning to locate photoacoustic point sources === Traditional photoacoustic beamforming techniques modeled photoacoustic wave propagation by using detector array geometry and the time-of-flight to account for differences in the PA signal arrival time. However, this technique failed to account for reverberant acoustic signals caused by acoustic reflection, resulting in acoustic reflection artifacts that corrupt the true photoacoustic point source location information. In Reiter et al., a convolutional neural network (similar to a simple VGG-16 style architecture) was used that took pre-beamformed photoacoustic data as input and outputted a classification result specifying the 2-D point source location. ==== Deep learning for PA wavefront localization ==== Johnstonbaugh et al. was able to localize the source of photoacoustic wavefronts with a deep neural network. The network used was an encoder-decoder style convolutional neural network. The encoder-decoder network was made of residual convolution, upsampling, and high field-of-view convolution modules. A Nyquist convolution layer and differentiable spatial-to-numerical transform layer were also used within the architecture. Simulated PA wavefronts served as the input for training the model. To create the wavefronts, the forward simulation of light propagation was done with the NIRFast toolbox and the light-diffusion approximation, while the forward simulation of sound propagation was done with the K-Wave toolbox. The simulated wavefronts were subjected to different scattering mediums and Gaussian noise. The output for the network was an artifact free heat map of the targets axial and lateral position. The network had a mean error rate of less than 30 microns when localizing target below 40 mm and had a mean error rate of 1.06 mm for localizing targets between 40 mm and 60 mm. With a slight modification to the network, the model was able to accommodate multi target localization. A validation experiment was performed in which pencil lead was submerged into an intralipid solution at a depth of 32 mm. The network was able to localize the lead's position when the solution had a reduced scattering coefficient of 0, 5, 10, and 15 cm−1. The results of the network show improvements over standard delay-and-sum or frequency-domain beamforming algorithms and Johnstonbaugh proposes that this technology could be used for optical wavefront shaping, circulating melanoma cell detection, and real-time vascular surgeries. === Removing acoustic reflection artifacts (in the presence of multiple sources and channel noise) === Building on the work of Reiter et al., Allman et al. utilized a full VGG-16 architecture to locate point sources and remove reflection artifacts within raw photoacoustic channel data (in the presence of multiple sources and channel noise). This utilization of deep learning trained on simulated data produced in the MATLAB k-wave library, and then later reaffirmed their results on experimental data. === Ill-posed PACT reconstruction === In PACT, tomographic reconstruction is performed, in which the projections from multiple solid angles are combined to form an image. When reconstruction methods like filtered backprojection or time reversal, are ill-posed inverse problems due to sampling under the Nyquist-Shannon's sampling requirement or with limited-bandwidth/view, the resulting reconstruction contains image artifacts. Traditionally these artifacts were removed with slow iterative methods like total variation minimization, but the advent of deep learning approaches has opened a new avenue that utilizes a priori knowledge from network training to remove artifacts. In the deep learning methods that seek to remove these sparse sampling, limited-bandwidth, and limited-view artifacts, the typical workflow involves first performing the ill-posed reconstruction technique to transform the pre-beamformed data into a 2-D representation of the initial pressure distribution that contains artifacts. Then, a convolutional neural network (CNN) is trained to remove the artifacts, in order to produce an artifact-free representation of the ground truth initial pressure distribution. ==== Using deep learning to remove sparse sampling artifacts ==== When the density of uniform tomographic view angles is under what is prescribed by the Nyquist-Shannon's sampling theorem, it is said that the imaging system is performing sparse sampling. Sparse sampling typically occurs as a way of keeping production costs low and improving image acquisition speed. The typical network architectures used to remove these sparse sampling artifacts are U-net and Fully Dense (FD) U-net. Both of these architectures contain a compression and decompression phase. The compression phase learns to compress the image to a latent representation that lacks the imaging artifacts and other details. The decompression phase then combines with information passed by the residual connections in order to add back image details without adding in the details associated with the artifacts. FD U-net modifies the original U-net architecture by including dense blocks that allow layers to utilize information learned by previous layers within the dense block. Another technique was proposed using a simple CNN based architecture for removal of artifacts and improving the k-wave image reconstruction. ==== Removing limited-view artifacts with deep learning ==== When a region of partial solid angles are not captured, generally due to geometric limitations, the image acquisition is said to have limited-view. As illustrated by the experiments of Davoudi et al., limited-view corruptions can be directly observed as missing information in the frequency domain of the reconstructed image. Limited-view, similar to sparse sampling, makes the initial reconstruction algorithm ill-posed. Prior to deep learning, the limited-view problem was addressed with complex hardware such as acoustic deflectors and full ring-shaped transducer arrays, as well as solutions like compressed sensing, weighted factor, and iterative filtered backprojection. The result of this ill-posed reconstruction is imaging artifacts that can be removed by CNNs. The deep learning algorithms used to remove limited-view artifacts include U-net and FD U-net, as well as generative adversarial networks (GANs) and volumetric versions of U-net. One GAN implementation of note improved upon U-net by using U-net as a generator and VGG as a discriminator, with the Wasserstein metric and gradient penalty to stabilize training (WGAN-GP). ==== Pixel-wise interpolation

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  Mix automation

  In music recording, mix automation allows the mixing console to remember the mixing engineer's dynamic adjustment of faders during a musical piece in the post-production editing process. A timecode is necessary for the synchronization of automation. Modern mixing consoles and digital audio workstations use comprehensive mix automation. The need for automated mixing originated from the late 1970s transition form 8-track to 16-track and then 24-track multitrack recording, as mixing could be laborious and require multiple people and hands, and the results could be almost impossible to reproduce. With 48-track recording - synchronized twin 24-track recorders (for a net 46 audio tracks, with one on each machine for SMPTE timecode) - came larger recording and mixing consoles with even more channel faders to manage during mixdown. Manufacturers, such as Neve Electronics (now AMS Neve) and Solid State Logic (SSL), both English companies, developed systems that enabled one engineer to oversee every detail of a complex mix, although the computers required to power these desks remained a rarity into the late 1970s. According to record producer Roy Thomas Baker, Queen's 1975 single "Bohemian Rhapsody" was one of the first mixes to be done with automation. == Types == Voltage Controlled Automation fader levels are regulated by voltage-controlled amplifiers (VCA). VCAs control the audio level and not the actual fader. Moving Fader Automation a motor is attached to the fader, which then can be controlled by the console, digital audio workstation (DAW), or user. Software Controlled Automation the software can be internal to the console, or external as part of a DAW. The virtual fader can be adjusted in the software by the user. MIDI Automation the communications protocol MIDI can be used to send messages to the console to control automation. == Modes == Auto Write used the first time automation is created or when writing over existing automation Auto Touch writes automation data only while a fader is touched/faders return to any previously automated position after release Auto Latch starts writing automation data when a fader is touched/stays in position after release Auto Read digital Audio Workstation performs the written automation Auto Off automation is temporarily disabled All of these include the mute button. If mute is pressed during writing of automation, the audio track will be muted during playback of that automation. Depending on software, other parameters such as panning, sends, and plug-in controls can be automated as well. In some cases, automation can be written using a digital potentiometer instead of a fader.

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  Ayoba

  Ayoba is an African communication platform developed in South Africa. It is owned by Progressive Tech Holdings in Mauritius and managed by SIMFY Africa. Launched on May 4, 2019, as of April 2024, it has over 35 million active users. == History == Ayoba was first published on Google Play in February 2019. Its first marketing campaign and brand launch took place in Cameroon on May 4, 2019. In June 2019, the platform introduced its first eight channels. In November 2019, the platform reached one million active users, which increased to two million by June 2020. Subsequently, ayoba expanded its services, including the launch of games for Android in February 2020, Momo (Mobile Money) in Cameroon in May 2020, and MicroApps in May 2020. It also launched music and voice and video calling features in 12 territories in August 2020. The first version of ayoba for iOS was released in September 2020. In December of the same year, games and Messaging 2.0 were launched on the platform. In November 2020, it won Best Mobile Application at the African Digital Awards. In 2021, it won OTT Brand of the Year at the Marketing World Awards in Ghana. In December 2022, it received Top Innovative Technology and Telecom Product of the Year at the National Communications Awards in December 2022. In June 2023 ayoba partnered with BoomPlay and as of April 2024, it had 35 million monthly active users. Ayoba has partnered with Jumia Ghana to offer exclusive deals to users. Ayoba users can get a 10% discount on selected Jumia purchases through the app, with no data charges for MTN users. This partnership aims to make online shopping more affordable and accessible by integrating Jumia's offers into the ayoba app. Ayoba supports over 35 million users across Africa and provides services in 22 languages. To access the deals, users can download the ayoba app from the Google Play Store, iOS Store, or the official website. == Platform features == Chat, Call and Share: ayoba enables instant messaging, voice notes, picture sharing, and file sharing with contacts, even if they do not have the app installed. The app supports voice and video calls on both Android and iOS, as well as group chats, help channel and SMS continuity (non ayoba users receive messages as SMS, their responses appear in the ayoba app). Music: ayoba offers a free music player with daily updates on international and African music. Users can find playlists for different genres. Games: ayoba provides a selection of interactive games, including action, adventure, and children's games available on both Android and iOS. Mobile Money Transfers: In certain territories, ayoba supports mobile money transfers using MTN Mobile Money (MoMo) for transactions within the app. MicroApps: ayoba features individual MicroApps within the platform that offer content and services, including streaming channels, podcasts, and specialized apps. The availability of these apps may vary by country. == Operations == ayoba primarily focuses on the following territories: Nigeria, Cameroon, South Africa, Ghana, Côte d'Ivoire, Uganda, Republic of Congo, Benin, Zambia, Tanzania, Kenya, Senegal, Togo, Guinea Bissau, Guinea Conakry, Sudan, South Sudan, and Liberia. The company operates from its offices in Cape Town and Johannesburg, South Africa. David Gillaranz served as the CEO from 2019 to 2021, and Burak Akinci has been the CEO since 2021.

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  Ericom Connect

  Ericom Connect is a remote access/application publishing solution produced by Ericom Software that provides secure, centrally managed access to physical or hosted desktops and applications running on Microsoft Windows and Linux systems. == Product overview == Ericom Connect is desktop virtualization and application virtualization software that allows users to run applications remotely, without installing them on the local computer or device. The software is noted for its scalability, ease of deployment, and compatibility with any type of infrastructure, cloud or physical. Ericom Connect uses AccessPad (native client for desktops), AccessToGo (native client for mobile), or AccessNow, one of the first HTML5 RDP solutions to support clientless access to Windows desktops and applications from any device with an HTML5-compatible browser, including Macintosh computers, mobile devices, and Google Chromebooks. Other notable features include performance monitoring, built-in real-time analytics & BI, support for two-factor authentication (using RSA SecurID), multi-tenancy and multi-datacenter support via a single unified web interface, and a “Launch Simulation” feature that allows users to visualize and simulate actual step-by-step user processes directly from within the administration console. In addition to scalability, by distributing configurations, logs, etc., across multiple servers there is no single point of failure, as can be the case if all configuration information is stored on one server. == History == Ericom Connect was introduced in 2015. Ericom Connect is a successor to Ericom PowerTerm Web Connect. PowerTerm Web Connect used an architecture similar to what was then current with Citrix and VMWare, relying on a centralized SQL server, a connection broker, image management for different hypervisors, and a variety of clients. Ericom Connect uses a new grid architecture that provides more scalability, reliability, and flexibility than before.

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