AI Detector No Character Limit

AI Detector No Character Limit — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Huawei Member Center

  Huawei Member Center is a benefits app which runs using Huawei Mobile Services. Originally launched in China, Huawei Member Center is now being developed primarily around devices such as P40 Pro and the Nova 7. == Membership Levels == The Huawei Member Center provides rewards in two primary ways, 1) device-specific & promotions and 2) via frequent use of Huawei products and apps, using points to redeem additional benefits. In China, Huawei members are already classified into three levels, the highest being “elite”. Membership level determines the level of perks received, from priority access to the service hotline, new device events & proprietary early-access opportunities. Huawei ran a number of member events in 2019 called "Huawei Member Day" to promote the Member Center including providing tips for the Mate 30 Pro and offering a 50Gb cloud storage upgrade to users. == HMC in China == Huawei Member Center Has seen significant adoption in China and the east, the rewards for use on the app have ranged from free book coupons, discounted travel and exclusive gifts of new devices, such as the Huawei Enjoy Z.

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  Logogen model

  The logogen model of 1969 is a model of speech recognition that uses units called "logogens" to explain how humans comprehend spoken or written words. Logogens are a vast number of specialized recognition units, each able to recognize one specific word. This model provides for the effects of context on word recognition. == Overview == The word logogen can be traced back to the Greek-language word logos, which means "word", and genus, which means "birth". British scientist John Morton's logogen model was designed to explain word recognition using a new type of unit known as a logogen. A critical element of this theory is the involvement of lexicons, or specialized aspects of memory that include semantic and phonemic information about each item that is contained in memory. A given lexicon consists of many smaller, abstract items known as logogens. Logogens contain a variety of properties about given word such as their appearance, sound, and meaning. Logogens do not store words within themselves, but rather they store information that is specifically necessary for retrieval of whatever word is being searched for. A given logogen will become activated by psychological stimuli or contextual information (words) that is consistent with the properties of that specific logogen and when the logogen's activation level rises to or above its threshold level, the pronunciation of the given word is sent to the output system. Certain stimuli can affect the activation levels of more than one word at a time, usually involving words that are similar to one another. When this occurs, whichever of the words' activation levels reaches the threshold level, it is that word that is then sent to the output system with the subject remaining unaware of any partially excited logogens. This assumption was made by Marslen-Wilson and Welch (1978), who added to the model some assumptions of their own in order to account for their experimental results. They also assumed that the analysis of phonetic input can only become available to other parts of the system by process of how the input affects the logogen system. Finally, Marslen-Wilson and Welch assume that the first syllable of a given word will increase the activation level of a given logogen more than those of the latter syllables, which supported the data found at the time. == Analysis == The logogen model can be used to help linguists explain particular occurrences in the human language. The most-helpful application of the model is to show how one accesses words and their meanings in the lexicon. The word-frequency effect is best explained by the logogen model in that words (or logogens) that have a higher frequency (or are more common) have a lower threshold. This means that they require less perceptual power in the brain to be recognized and decoded from the lexicon and are recognized faster than those words that are less common. Also, with high-frequency words, the recovery from lowering the item's threshold is less fulfilled compared to low-frequency words so less sensory information is needed for that particular item's recognition. There are ways to lower thresholds, such as repetition and semantic priming. Also, each time a word is encountered through these methods, the threshold for that word is temporarily lowered partially because of its recovering ability. This model also conveys that specific concrete words are recalled better because they use images and logogens, whereas abstract words are not as easily recalled well because they only use logogens, hence showing the difference in thresholds between these two types of words. At the time of its conception, Morton's logogen model was one of the most influential models in springing up other parallel word access models and served as the essential basis for these subsequent models. Morton's model also strongly influenced other contemporary theories on lexical access. However, despite the advantages that the logogen theory presents, it also displays some negative facets. First and foremost, the logogen model does not explain all occurrences in language, such as the introduction of new words or non-words into a person's lexicon. Also, because of the distinctive model application, it may vary in its effectiveness in different languages. == Criticisms == While this model does a reasonable job of understanding the underlying semantics of many aspects in psycholinguistics, there are some flaws that have been pointed out in the logogen model. It has been argued that the prior stimulus patterns that have been seen in the logogen theory are not centrally localized in the logogen itself but are actually distributed throughout the different pathways over which the stimulus is being processed. What this directs at is that the notion and proliferation of logogens was due to modality. In essence, the logogen is unnecessary in the idea of attaining the title of being a recognition unit because of the variety of pathways that it is open to, not just logogens. Another criticism has been that this model essentially ignores larger and more critical structures in language and phonetics such as the different syntactic rules or grammatical construction that innately exists in language. Since this model overtly limits itself to the scope of lexical access then this model is seen as biased and misunderstood. To many psychologists, the logogen model does not meet the functional or representational adequacy that a theory should include to sufficiently comprehend language. Also, another criticism is that the logogen theory was supposed to predict that stimulus degradation should affect priming and word frequency in humans. However, many psychologists have conducted studies and researched the model to show that only priming and not word frequency is interacted with stimulus degradation. Priming is supposed to deteriorate a stimulus because it postulates that the semantic characteristics of previously known words are fed back into the detector of a person which in turn raises the threshold of related items. In word frequency, stimulus degradation is supposed to occur because it postulates that familiar words have lower thresholds than their low-frequency counterparts. However, in studies, priming is the only structure that does show observable and notable stimulus decadence. Even though the logogen theory has many unfilled holes, Morton was a revolutionary of his field whose speculation and research has opened up a remarkable era of psycholinguistics. == Other models to consider == cohort model – This model was proposed by Marslen-Wilson and was designed specifically to account for auditory word recognition. It works by breaking the word down and states that when a word is heard all words that begin with the first sound of the target word are activated. This set of words is considered the cohort. Once the first cohort has been activated, the other information, or sounds in the word narrow down the choices. The person recognizes the word when you are left with a single choice; this is considered the "recognition point". checking model – This model was developed by Norris in 1986. In this particular model, he took the approach that any word that partially matches the input is analyzed and checked to see if it fits with the context of the situation. interactive-activation model – This model is considered a connectionist model. Proposed by McClelland and Rumelhart in the 1981 to 1982 period, it is based around nodes, which are visual features, and positions of letters within a given word. They also act as word detectors which have inhibitory and excitatory connections between them. This model starts with first letter and suggests that all the words with that first letter are activated at first and then going through the word one can determine what the word is they are looking at. The main principle is that mental phenomena can be described by interconnected networks of simple units. verification model – The model was developed by Curtis Becker in 1970. The main idea is that a small number of candidates that are activated in parallel are subject to a serial-verification process. This model starts the word-recognition process with a basic representation of the stimulus. Then, sensory trace, consisting of line features is used to activate word detectors. When an acceptable number of detectors are activated these are used to generate a search set. These items are drawn from the lexicon on the basis of similarity to the sensory trace, which help with the identity of the stimulus. Then, in a serial process the candidates are compared to the representation of the sensory-trace input. == Related concepts == word frequency – This is the belief that the speed and accuracy with which a word is recognized is related to how frequently the word occurs in our language. Each logogen has a threshold (for identification) and words with higher frequencies have lower thresholds. Words with higher freq

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  Level-set method

  The Level-set method (LSM) is a conceptual framework for using level sets as a tool for numerical analysis of surfaces and shapes. LSM can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize these objects. LSM makes it easier to perform computations on shapes with sharp corners and shapes that change topology (such as by splitting in two or developing holes). These characteristics make LSM effective for modeling objects that vary in time, such as an airbag inflating or a drop of oil floating in water. == Overview == The figure on the right illustrates several ideas about LSM. In the upper left corner is a bounded region with a well-behaved boundary. Below it, the red surface is the graph of a level set function φ {\displaystyle \varphi } determining this shape, and the flat blue region represents the X-Y plane. The boundary of the shape is then the zero-level set of φ {\displaystyle \varphi } , while the shape itself is the set of points in the plane for which φ {\displaystyle \varphi } is positive (interior of the shape) or zero (at the boundary). In the top row, the shape's topology changes as it is split in two. It is challenging to describe this transformation numerically by parameterizing the boundary of the shape and following its evolution. An algorithm can be used to detect the moment the shape splits in two and then construct parameterizations for the two newly obtained curves. On the bottom row, however, the plane at which the level set function is sampled is translated upwards, on which the shape's change in topology is described. It is less challenging to work with a shape through its level-set function rather than with itself directly, in which a method would need to consider all the possible deformations the shape might undergo. Thus, in two dimensions, the level-set method amounts to representing a closed curve Γ {\displaystyle \Gamma } (such as the shape boundary in our example) using an auxiliary function φ {\displaystyle \varphi } , called the level-set function. The curve Γ {\displaystyle \Gamma } is represented as the zero-level set of φ {\displaystyle \varphi } by Γ = { ( x , y ) ∣ φ ( x , y ) = 0 } , {\displaystyle \Gamma =\{(x,y)\mid \varphi (x,y)=0\},} and the level-set method manipulates Γ {\displaystyle \Gamma } implicitly through the function φ {\displaystyle \varphi } . This function φ {\displaystyle \varphi } is assumed to take positive values inside the region delimited by the curve Γ {\displaystyle \Gamma } and negative values outside. == The level-set equation == If the curve Γ {\displaystyle \Gamma } moves in the normal direction with a speed v {\displaystyle v} , then by chain rule and implicit differentiation, it can be determined that the level-set function φ {\displaystyle \varphi } satisfies the level-set equation ∂ φ ∂ t = v | ∇ φ | . {\displaystyle {\frac {\partial \varphi }{\partial t}}=v|\nabla \varphi |.} Here, | ⋅ | {\displaystyle |\cdot |} is the Euclidean norm (denoted customarily by single bars in partial differential equations), and t {\displaystyle t} is time. This is a partial differential equation, in particular a Hamilton–Jacobi equation, and can be solved numerically, for example, by using finite differences on a Cartesian grid. However, the numerical solution of the level set equation may require advanced techniques. Simple finite difference methods fail quickly. Upwinding methods such as the Godunov method are considered better; however, the level set method does not guarantee preservation of the volume and shape of the set level in an advection field that maintains shape and size, for example, a uniform or rotational velocity field. Instead, the shape of the level set may become distorted, and the level set may disappear over a few time steps. Therefore, high-order finite difference schemes, such as high-order essentially non-oscillatory (ENO) schemes, are often required, and even then, the feasibility of long-term simulations is questionable. More advanced methods have been developed to overcome this; for example, combinations of the leveling method with tracking marker particles suggested by the velocity field. == Example == Consider a unit circle in R 2 {\textstyle \mathbb {R} ^{2}} , shrinking in on itself at a constant rate, i.e. each point on the boundary of the circle moves along its inwards pointing normally at some fixed speed. The circle will shrink and eventually collapse down to a point. If an initial distance field is constructed (i.e. a function whose value is the signed Euclidean distance to the boundary, positive interior, negative exterior) on the initial circle, the normalized gradient of this field will be the circle normal. If the field has a constant value subtracted from it in time, the zero level (which was the initial boundary) of the new fields will also be circular and will similarly collapse to a point. This is due to this being effectively the temporal integration of the Eikonal equation with a fixed front velocity. == Applications == In mathematical modeling of combustion, LSM is used to describe the instantaneous flame surface, known as the G equation. Level-set data structures have been developed to facilitate the use of the level-set method in computer applications. Computational fluid dynamics Trajectory planning Optimization Image processing Computational biophysics Discrete complex dynamics (visualization of the parameter plane and the dynamic plane) == History == The level-set method was developed in 1979 by Alain Dervieux, and subsequently popularized by Stanley Osher and James Sethian. It has since become popular in many disciplines, such as image processing, computer graphics, computational geometry, optimization, computational fluid dynamics, and computational biology.

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  Rapid prototyping

  Rapid prototyping is a group of techniques used to quickly fabricate a scale model of a physical part or assembly using three-dimensional computer aided design (CAD) data. Construction of the part or assembly is usually done using 3D printing or "additive layer manufacturing" technology. The first methods for rapid prototyping became available in mid 1987 and were used to produce models and prototype parts. Today, they are used for a wide range of applications and are used to manufacture production-quality parts in relatively small numbers if desired without the typical unfavorable short-run economics. This economy has encouraged online service bureaus. Historical surveys of RP technology start with discussions of simulacra production techniques used by 19th-century sculptors. Some modern sculptors use the progeny technology to produce exhibitions and various objects. The ability to reproduce designs from a dataset has given rise to issues of rights, as it is now possible to interpolate volumetric data from 2D images. As with CNC subtractive methods, the computer-aided-design – computer-aided manufacturing CAD -CAM workflow in the traditional rapid prototyping process starts with the creation of geometric data, either as a 3D solid using a CAD workstation, or 2D slices using a scanning device. For rapid prototyping this data must represent a valid geometric model; namely, one whose boundary surfaces enclose a finite volume, contain no holes exposing the interior, and do not fold back on themselves. In other words, the object must have an "inside". The model is valid if for each point in 3D space the computer can determine uniquely whether that point lies inside, on, or outside the boundary surface of the model. CAD post-processors will approximate the application vendors' internal CAD geometric forms (e.g., B-splines) with a simplified mathematical form, which in turn is expressed in a specified data format which is a common feature in additive manufacturing: STL file format, a de facto standard for transferring solid geometric models to SFF machines. To obtain the necessary motion control trajectories to drive the actual SFF, rapid prototyping, 3D printing or additive manufacturing mechanism, the prepared geometric model is typically sliced into layers, and the slices are scanned into lines (producing a "2D drawing" used to generate trajectory as in CNC's toolpath), mimicking in reverse the layer-to-layer physical building process. == Application areas == Rapid prototyping is also commonly applied in software engineering to try out new business models and application architectures such as Aerospace, Automotive, Financial Services, Product development, and Healthcare. Aerospace design and industrial teams rely on prototyping in order to create new AM methodologies in the industry. Using SLA they can quickly make multiple versions of their projects in a few days and begin testing quicker. Rapid Prototyping allows designers/developers to provide an accurate idea of how the finished product will turn out before putting too much time and money into the prototype. 3D printing being used for Rapid Prototyping allows for Industrial 3D printing to take place. With this, you could have large-scale moulds to spare parts being pumped out quickly within a short period of time. == Types of Rapid Prototyping == Stereolithography (SLA) → a laser-cured photopolymer for materials such as thermoplastic-like photopolymers. Selective Laser Sintering (SLS) → a laser-sintered powder for materials such as Nylon or TPU. Direct Metal Laser Sintering (DMLS) → laser-sintered metal powder for materials like stainless steel, titanium, chrome, and aluminum. Fused Deposition Modeling (FDM) → fused extrusions of filaments like ABS, PC, and PPCU. Multi Jet Fusion (MJF) → it is an inkjet array selective fusing across bed of nylon powder for Black Nylon 12. PolyJet (PJET) → it is a uv-cured jetted photopolymer to work with acrylic-based and elastomeric photopolymers. Computer Numerical Controlled Machine (CNC) → it is used for manipulating engineering-grade thermoplastics and metals. Injection Molding (IM) → the injection is done using aluminum molds and it is used for thermoplastics, metals and liquid silicone rubber. Vacuum Casting→ is a manufacturing process used to create high-quality prototypes and small batches of parts. == History == In the 1970s, Joseph Henry Condon and others at Bell Labs developed the Unix Circuit Design System (UCDS), automating the laborious and error-prone task of manually converting drawings to fabricate circuit boards for the purposes of research and development. By the 1980s, U.S. policy makers and industrial managers were forced to take note that America's dominance in the field of machine tool manufacturing evaporated, in what was named the machine tool crisis. Numerous projects sought to counter these trends in the traditional CNC CAM area, which had begun in the US. Later when Rapid Prototyping Systems moved out of labs to be commercialized, it was recognized that developments were already international and U.S. rapid prototyping companies would not have the luxury of letting a lead slip away. The National Science Foundation was an umbrella for the National Aeronautics and Space Administration (NASA), the US Department of Energy, the US Department of Commerce NIST, the US Department of Defense, Defense Advanced Research Projects Agency (DARPA), and the Office of Naval Research coordinated studies to inform strategic planners in their deliberations. One such report was the 1997 Rapid Prototyping in Europe and Japan Panel Report in which Joseph J. Beaman founder of DTM Corporation [DTM RapidTool pictured] provides a historical perspective: The roots of rapid prototyping technology can be traced to practices in topography and photosculpture. Within TOPOGRAPHY Blanther (1892) suggested a layered method for making a mold for raised relief paper topographical maps .The process involved cutting the contour lines on a series of plates which were then stacked. Matsubara (1974) of Mitsubishi proposed a topographical process with a photo-hardening photopolymer resin to form thin layers stacked to make a casting mold. PHOTOSCULPTURE was a 19th-century technique to create exact three-dimensional replicas of objects. Most famously Francois Willeme (1860) placed 24 cameras in a circular array and simultaneously photographed an object. The silhouette of each photograph was then used to carve a replica. Morioka (1935, 1944) developed a hybrid photo sculpture and topographic process using structured light to photographically create contour lines of an object. The lines could then be developed into sheets and cut and stacked, or projected onto stock material for carving. The Munz (1956) Process reproduced a three-dimensional image of an object by selectively exposing, layer by layer, a photo emulsion on a lowering piston. After fixing, a solid transparent cylinder contains an image of the object. "The Origins of Rapid Prototyping - RP stems from the ever-growing CAD industry, more specifically, the solid modeling side of CAD. Before solid modeling was introduced in the late 1980's, three-dimensional models were created with wire frames and surfaces. But not until the development of true solid modeling could innovative processes such as RP be developed. Charles Hull, who helped found 3D Systems in 1986, developed the first RP process. This process, called stereolithography, builds objects by curing thin consecutive layers of certain ultraviolet light-sensitive liquid resins with a low-power laser. With the introduction of RP, CAD solid models could suddenly come to life". The technologies referred to as Solid Freeform Fabrication are what we recognize today as rapid prototyping, 3D printing or additive manufacturing: Swainson (1977), Schwerzel (1984) worked on polymerization of a photosensitive polymer at the intersection of two computer controlled laser beams. Ciraud (1972) considered magnetostatic or electrostatic deposition with electron beam, laser or plasma for sintered surface cladding. These were all proposed but it is unknown if working machines were built. Hideo Kodama of Nagoya Municipal Industrial Research Institute was the first to publish an account of a solid model fabricated using a photopolymer rapid prototyping system (1981). The first 3D rapid prototyping system relying on Fused Deposition Modeling (FDM) was made in April 1992 by Stratasys but the patent did not issue until June 9, 1992. Sanders Prototype, Inc introduced the first desktop inkjet 3D Printer (3DP) using an invention from August 4, 1992 (Helinski), Modelmaker 6Pro in late 1993 and then the larger industrial 3D printer, Modelmaker 2, in 1997. Z-Corp using the MIT 3DP powder binding for Direct Shell Casting (DSP) invented 1993 was introduced to the market in 1995. Even at that early date the technology was seen as having a place in manufacturing practice. A low resol

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  Jordan Antiquities Database and Information System

  The Jordan Antiquities Database and Information System (JADIS) was a computer database of antiquities in Jordan, the first of its kind in the Arab world. It was established by the Department of Antiquities in 1990, in cooperation with the American Center for Oriental Research in Amman and sponsored by the United States Agency for International Development. JADIS was in use until 2002, when it was superseded by a new system, MEGA-J. Over 10,841 antiquities were registered in the database. An introduction and printed summary of the database was published by the Department of Antiquities in 1994, edited by Gaetano Palumbo.

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  Real-time transcription

  Real-time transcription is the general term for transcription by court reporters using real-time text technologies to deliver computer text screens within a few seconds of the words being spoken. Specialist software allows participants in court hearings or depositions to make notes in the text and highlight portions for future reference. Real-time transcription is also used in the broadcasting environment where it is more commonly termed "captioning." == Career opportunities == Real-time reporting is used in a variety of industries, including entertainment, television, the Internet, and law. Specific careers include the following: Judicial reporters use a stenotype to provide instant transcripts on computer screens as a trial or deposition occurs. Communication access real-time translation (CART) reporters assist the hearing-impaired by transcribing spoken words, giving them personal access to the communications they need day to day. Television broadcast captioners use real-time reporting technology to allow hard-of-hearing or deaf people to see what is being said on live television broadcasts such as news, emergency broadcasts, sporting events, awards shows, and other programs. Internet information (or Webcast) reporters provide real-time reporting of sales meetings, press conferences, and other events, while simultaneously transmitting the transcripts to computers worldwide. Other rapid data entry positions. == History == Before the advent of the stenotype machine, court reporters wrote official trial transcripts by hand using a shorthand system of stenoforms that could later be translated into readable English. It often took eight years of training to learn this manual form of writing at the necessary speed. Walter Heironimus was among the first stenographers to make use of the stenotype machine during his work in the U.S. District Court system in New Jersey in 1935. A "transcript crisis" arose during the later half of the twentieth century due to the increasing volume of lawsuits. There were not enough number of court reporters to match the increasing number of trials. Not only were court reporters unavailable to attend many court proceedings, court transcripts were constantly late and the qualities varied. Some believed it was due to the non-interchangeability between court reporters, and others believed it was simply due to a labor shortage. In the meantime, magnetic audiotape recording, or known as electronic recording (ER) began to threaten all reporters' job since it could record long-hour courtroom trials and replace a court reporter's position in the courtroom. As a result, machine translation (MT) intended to serve as a solution for preventing ER from potentially replacing reporters' jobs. However, MT relied heavily on human labors operating behind the system and many started to question if it should be the right way to end the "transcript crisis." Later in 1964, set up by CIA, the Automatic Language Processing Advisory Committee (ALPAC) was set to review whether MT was capable of solving this crisis. They concluded that MT had failed to do so. Then Patrick O'Neill, a skilled and experienced court reporter, stayed to work on the stenotype-translation project with CIA and developed the prototype CAT system. After adopting the CAT system in court-reporting community, CAT was brought into the television broadcasting system, aiming to provide captions for the deaf or hard-of-hearing communities. In 1983, Linda Miller developed a further use for the CAT system. She successfully translated a lecture live on the television screen and provided a transcript for students. This technique is known as Computer-Aided Real-time Translation, or CART. == Court reporter == It is the court reporter's job to note down the exact words spoken by every participants during a court or deposition proceeding. Then court reporters will provide verbatim transcripts. The reason to have an official court transcript is that the real-time transcriptions allows attorneys and judges to have immediate access to the transcript. It also helps when there's a need to look up for information from the proceeding. Additionally, the deaf and the hard-of-hearing communities can also participate in the judicial process with the help of real-time transcriptions provided by court reporters. === Education and training === The required degree level for a court reporter to have is an Associate's degree or postsecondary certificate. In order to become a court reporter, more than 150 reporter training programs are provided at proprietary schools, community colleges, and four-year universities. After graduation, court reporters can choose to further pursue certifications to achieve a higher level of expertise and increase their marketability during a job search. In most states, Certificates of Proficiency from the NCRA or from state agencies are now required certificates for court reporters to have in order to qualify for appointments. The NCRA aims to set the national standard for the certification of court reporters, and since 1937 it has offered its certification program which is now accepted by 22 states instead of state licenses. Court reporter training programs include but not limited to: Training in rapid writing skill, or shorthand, which will enable students to record, with accuracy, at least 225 words per minute Training in typing, which will enable students to type at least 60 words per minute A general training in English, which covers aspects of grammar, word formation, punctuation, spelling and capitalization Taking Law related courses in order to understand the overall principles of civil and criminal law, legal terminology and common Latin phrases, rules of evidence, court procedures, the duties of court reporters, the ethics of the profession Visits to actual trials Taking courses in elementary anatomy and physiology and medical word study including medical prefixes, roots and suffixes. Other than official court reporters, who are assigned to and work for a particular court, other types of court reporters include free-lance reporter, who either works for a court reporting firm or self-employed. They are different from official court reporters in that they have the chances to work on a wider range of assignments and work on basis of hourly wage. Hearing reporters work at governmental agency hearings. Legislative reporters work in law-making bodies. The demand for reporters is not limited in just the court settings. Reporters are also needed in conferences, meetings, conventions, investigations, and a variety of industries with needs for employers with real-time data entry skills. == Non-English transcription == Transcription services are universally necessary, so it is not limited to the English language. A stenographer's ability to transcribe languages beyond only English is especially valuable as society as a whole becomes increasingly multilingual. Education in non-English transcription demands a comprehensive understanding of the given language. Phonetic differences between English and other languages are a particular challenge in carrying English transcription skills over into other languages. Stenography represents various sounds of a language in a formal system of shorthand, so differences within the sets of sounds that emerge in other languages require an alternative system of shorthand transcription. For example, the presence of many diphthongs and triphthongs in Spanish requires certain sounds to be distinguished that would not be present in transcribing English into shorthand. == Controversies == The usage of transcription in the context of linguistic discussions has been controversial. Typically, two kinds of linguistic records are considered to be scientifically relevant. First, linguistic records of general acoustic features, and secondly, records that only focuses on the distinctive phonemes of a language. While transcriptions are not entirely illegitimate, transcriptions without enough detailed commentary regarding any linguistic features, or transcriptions of poor quality resources, has a great chance of the content being misinterpreted. Besides misinterpretation, transcribers could also bring in cultural biases and ignorance that reflect onto their transcription. These instances may cause a disruption of reliability in the final real-time transcription, which could influence how the written utterance is seen as an evidence for a court-case. === Quality issues === Problems in the final resulting transcription can be caused by either the quality of the transcriber or the original source that is being transcribed. Transcribers can come from different levels of skill and training background. This makes the final transcription prone to poor quality, or if the transcription is being done by multiple people, lack of consistency in the content. If the source of the transcription is a recording, the problem may root back to the quality of the re

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  Metigo

  metigo is a software application that performs image-based modelling and close range photogrammetry. It produces rectified imagery plans, true ortho-projections on planar, cylindric and conic surfaces, 3D photorealistic models, measurements from photography and mappings on a photographic base for uses in the cultural heritage sector, mainly conservation. == Products == The metigo product line currently consists of the mapping software metigo MAP, the stereo-photogrammetry modeling software metigo 3D, the free viewer metigo VIEW. These products are all standalone and are not depending on other software, such as AutoCAD. === metigo MAP === metigo MAP is mainly used to map findings and conservation measured on a uniform metric photographic base. Therefore, photos of planar surfaces can be rectified based on geometrical informations, e.g. height and width of a rectangle, or cartesian coordinates measured by total station. Beside rectified imagery several other metric mapping bases can be imported and used: true ortho-projections; scaled scans of plans and plots; CAD-files; 3D models, such as digital surface models (DSM) produced by stereo-photogrammetry, SfM or 3D scanning. metigo MAP 's strong point is that rectified imagery taken with different techniques (visual light, sided light, IR, UV, UV-fluorescence, X-ray), historic images and photos taken at various stages of the conservation process can be superimposed and evaluated mutually. The user can allocate several attributes, such as different conservation measures and damage classes, to the mapped geometries. The mappings can be analysed by geometries as well as by user-defined attributes at any stage of the project. metigo MAP targets mainly conservators in different cultural heritage fields. Using it no specialist knowledge of surveying and photogrammetric techniques are needed. === metigo 3D === metigo 3D is a stereo-photogrammetric kit that allows to calculate bundle adjustments (axios3D), create high-quality 3D point clouds using multiple stereo photo pairs combined with metric survey data, mesh these point clouds, texture the meshes with high-resolution image data to create photo-realistic models, ortho-project orientated images on digital surface models (DSM) on planes and best-fit cylinders and cones, create unwrappings and developed views of curved surfaces, analyse deformations of 3D surfaces. metigo 3D targets metric survey specialists working in the cultural heritage sector. == Supported file formats == metigo has the ability to read the following formats: images: JPEG (.jpg), Tiff (.tif), Bitmaps (.bmp), CompuServ (.gif), Encapsualated Postscript (.eps), PCX (.pcx), Photo-CD (.pcd), PICT (.pcd), PNG (.png), Targa (.tga), RAW-format of several camera brands. CAD: DBX, DXF, DWG. 3D: many ASCII-formats (.stl, .wrl, etc.) point data: format editor for ASCII files. == Supported languages == Currently, an English and German version of the software is supported. For metigo MAP beside these a French and Polish GUI is offered for sale. == Applications == The main applications of metigo are: conservation in the cultural heritage context, e.g. stone conservation paintings tapestry etc. architecture, archaeology, many other are possible, e.g. forensics. == History == The first public release of metigo was in 2000.

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  Sensory, Inc.

  Sensory, Inc. is an American company which develops software AI technologies for speech, sound and vision. It is based in Santa Clara, California. Sensory’s technologies have shipped in over three billion products from hundreds of leading consumer electronics manufacturers including AT&T, Hasbro, Huawei, Google, Amazon, Samsung, LG, Mattel, Motorola, Plantronics, GoPro, Sony, Tencent, Garmin, LG, Microsoft, Lenovo, and more. Sensory has over 60 issued patents covering speech recognition in consumer electronics, biometric authentication, sensor/speech combinations, wake word technology, and more. == History == Sensory, Inc. was founded in 1994, originally as Sensory Circuits, by Forrest Mozer, Mike Mozer and Todd Mozer. The three had also co-founded ESS Technology years earlier. In 1999 Sensory acquired Fluent Speech Technologies, which was formed and started by a group of professors out of the Oregon Graduate Institute (formerly OGI, now OHSU). Fluent Speech Technologies developed high performance embedded speech engines, the technology from this acquisition is now the core technology used throughout Sensory's chip and software line. === Company timeline === 1994 – Founded 1995 – Introduces the RSC 164 - first commercially successful speech recognition IC 1998 – Introduces first speaker verification IC 2000 – Acquires Oregon based Fluent-Speech Technologies 2002 – Acquires Texas Instruments line of speech output ICs (the SC series) 2007 – Introduces first Voice User Interface for Bluetooth silicon (CSR BC-5) - BlueGenie 2008 - Sensory and BlueAnt partner on the V1 - Revolutionary new Bluetooth headset with a voice user interface. First wearable to use a voice user interface for control and best-reviewed speech recognition product in history 2009 – Introduced world's smallest text to speech system (TTS) and Truly HandsfreeTM Triggers/ wake words. 2010 – Introduced the NLP-5x – First Natural Language Voice Processor and TrulyHandsfree wake words in SDKs for Android, iOS, Linux, and Windows. NLP5x used the first generation of TrulyHandsfree wake words with low power and enhanced accuracy. 2011 – Sensory partners with Google and Microsoft to enable TrulyHandsfree as a front end to Goog411 and Bing411 2012 – Partnered with Tensilica to offer ultra-low power TrulyHandsfree wake words; introduced Speaker Verification and Speaker Identification for mobile phones and other consumer electronics. 2012 - TrulyHandsfree released into Samsung's Galaxy S2 for "Hey Galaxy" wake word 2013 – TrulyHandsfree wake words migrated to many new platforms and began shipping as MotoVoice in the Google-owned MotoX. Sensory's TrulyHandsfree in mobile takes off with the Galaxy S3 and S4 and Galaxy Note and is licensed into wearables like Google Glass. 2014 – Announced new initiative in Vision; added LG and Motorola as customers; received the 2014 Global Mobile Award for Best Mobile Technology Breakthrough at the GSMA Mobile World Congress in Barcelona, Spain (judges commented, "A big advance for the wearables market, this offers many benefits for consumers, increasing uptake and usage of many mobile apps, driving revenue for operators and content providers.") 2015-2018 - Licensed Google, Amazon, MSFT, Baidu, Huawei, ZTE, and many others with TrulyHandsfree wake words. Sensory develops first wake words for OK Google, Hey Siri, and Hey Cortana. 2019 - Sensory launched two new solutions: SoundID, sound identification, and TrulyNatural, embedded large vocabulary speech recognition. Sensory also acquired Vocalize.ai, an independent testing lab. 2020 - Sensory introduced VoiceHub, which allows the automated generation of wake words. 2021 - Sensory expands VoiceHub with speech recognition and NLU capabilities. The company initiated a new cloud platform, SensoryCloud.ai. 2022-Sensory rolls out SensoryCloud.ai with speech to text, text to speech, face & voice biometrics 2024- Sensory Automotive & TrulyNatural Speech-to-text On-Device launched == Technology and products == Sensory originally developed both hardware (Integrated Circuit - IC or "chip") and software platforms but migrated to software only around 2005 and added cloud and hybrid computing capabilities in 2021. Sensory's RSC-164 IC (Integrated Circuit or "chip") was used on NASA's Mars Polar Lander in the Mars Microphone on the Lander. Speech Synthesis SC-6x chips – acquired some speech synthesis technology from Texas Instruments. Sensory’s embedded AI solutions include the following: TrulyHandsfree (THF) - wake word detection and phrase spotting. TrulyNatural (TNL) - large vocabulary continuous speech recognition with NLU. TrulySecure (TS) - face and voice biometrics. TrulySecureSpeakerVerification (TSSV) - speaker and sound identification. VoiceHub - Online portal for creating custom wake words and speech recognition models with NLU. Sensory Automotive- Sensory Automotive is a full voice and vision suite of AI technologies that operate efficiently in the car without connecting to a network. The cloud initiative, SensoryCloud.ai, is targeting Speech To Text (STT), Text To Speech (TTS), Wake Word verification, face and voice recognition, and sound identification.

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  Signal transfer function

  The signal transfer function (SiTF) is a measure of the signal output versus the signal input of a system such as an infrared system or sensor. There are many general applications of the SiTF. Specifically, in the field of image analysis, it gives a measure of the noise of an imaging system, and thus yields one assessment of its performance. == SiTF evaluation == In evaluating the SiTF curve, the signal input and signal output are measured differentially; meaning, the differential of the input signal and differential of the output signal are calculated and plotted against each other. An operator, using computer software, defines an arbitrary area, with a given set of data points, within the signal and background regions of the output image of the infrared sensor, i.e. of the unit under test (UUT), (see "Half Moon" image below). The average signal and background are calculated by averaging the data of each arbitrarily defined region. A second order polynomial curve is fitted to the data of each line. Then, the polynomial is subtracted from the average signal and background data to yield the new signal and background. The difference of the new signal and background data is taken to yield the net signal. Finally, the net signal is plotted versus the signal input. The signal input of the UUT is within its own spectral response. (e.g. color-correlated temperature, pixel intensity, etc.). The slope of the linear portion of this curve is then found using the method of least squares. == SiTF curve == The net signal is calculated from the average signal and background, as in signal to noise ratio (imaging)#Calculations. The SiTF curve is then given by the signal output data, (net signal data), plotted against the signal input data (see graph of SiTF to the right). All the data points in the linear region of the SiTF curve can be used in the method of least squares to find a linear approximation. Given n {\displaystyle n\,} data points ( x i , y i ) {\displaystyle (x_{i}\,,y_{i}\,)} a best fit line parameterized as y = m x + b {\displaystyle y=mx+b\,} is given by: m = ∑ x i y i n − ∑ x i n ∑ y i n ∑ x i 2 n − ( ∑ x i n ) 2 b = ∑ y i n − m ∑ x i n {\displaystyle m={\frac {{\frac {\sum x_{i}y_{i}}{n}}-{\frac {\sum x_{i}}{n}}{\frac {\sum y_{i}}{n}}}{{\frac {\sum x_{i}^{2}}{n}}-({\frac {\sum x_{i}}{n}})^{2}}}\qquad \qquad b={\frac {\sum y_{i}}{n}}-m{\frac {\sum x_{i}}{n}}}

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  Transcription software

  Transcription software assists in the conversion of human speech into a text transcript. Audio or video files can be transcribed manually or automatically. Transcriptionists can replay a recording several times in a transcription editor and type what they hear. By using transcription hot keys, the manual transcription can be accelerated, the sound filtered, equalized or have the tempo adjusted when the clarity is not great. With speech recognition technology, transcriptionists can automatically convert recordings to text transcripts by opening recordings in a PC and uploading them to a cloud for automatic transcription, or transcribe recordings in real-time by using digital dictation. Depending on quality of recordings, machine generated transcripts may still need to be manually verified. The accuracy rate of the automatic transcription depends on several factors such as background noises, speakers' distance to the microphone, and accents. Transcription software, as with transcription services, is often used for business, legal, or medical purposes. Compared with audio content, a text transcript is searchable, takes up less computer memory, and can be used as an alternate method of communication, such as for subtitles and closed captions. Some clinical environments also use digital tools to support transcription workflows, including ambient documentation systems that employ Speech recognition to capture portions of clinical encounters and generate draft notes for later review. These tools are typically used alongside conventional transcription methods. The definition of transcription "software", as compared with transcription "service", is that the former is sufficiently automated that a user can run the entire system without engaging outside personnel. New software-as-a-service and cloud computing models use artificial intelligence, machine learning and natural language processing to convert speech to text and continuously learn new phrases and accents. AI transcription can, however, lead to hallucinations and other errors. == Development == Research at Google released a free android app Google Live Transcribe, it runs on Google Cloud. Google Chrome developed and has an available built in English Live Caption. Google Docs, Google Translate, Google Assistant, GBoard Google Text to Speech engine support transcription tool too. OpenAI launched Whisper, an open-source speech recognition deep learning model in September 2022. In 2024, an AI-powered transcription platform, Transkriptor, was launched, enabling the automatic conversion of audio and video recordings into text using speech recognition technology, with support for transcription in 100 languages and processing of content uploaded via a web interface as well as mobile and browser extensions. It is part of the Tor.app suite of AI-based language processing tools.

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  Fyre (software)

  Fyre, formerly de Jong Explorer, is a cross-platform tool for producing artwork based on histograms of iterated chaotic functions. It implements the Peter de Jong map in a fixed function pipeline through either a GTK GUI frontend, or a command line facility for easier rendering of high-resolution, high quality images. The program was renamed from de Jong Explorer to Fyre simply because 'It wasn't taken yet' and so that in the future, it could support more functions than just the standard Peter de Jong map. Fyre features a sidebar on the left to which the user can input the required variables and on the right is displayed the result of the equation. == Extra features == Additional image manipulation tools such as Gaussian blurs and Gamma controls are included in the program. The advantage to using them directly within Fyre is that the image accuracy and quality do not decline. Fyre features animation capabilities so that a user can link together several maps and create uncompressed AVIs from them. However, the uncompressed animation files are very large and so should be compressed with a separate tool, such as mencoder. == Peter de Jong Map == For most values of a,b,c and d the point (x,y) moves chaotically. The resulting image is a map of the probability that the point lies within the area represented by each pixel. Therefore, the longer that the user lets Fyre render for, the larger the probability map becomes and the more accurate the resulting image.

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  ActivTrak

  ActivTrak is an American company that produces workforce analytics and productivity software. The company was founded in 2009 by Birch Grove Software and is headquartered in Austin, Texas. The company has raised US$77.5 million in funding and is backed by Sapphire Ventures and Elsewhere Partners. == History == ActivTrak was founded in 2009 by Herb Axilrod and Anton Seidler in Dallas, Texas. ActivTrak's first on-demand software product launched in 2012, and the workforce analytics platform launched in 2015. It uses data sourced from more than 9,500 customers and 900,000 users. In 2019, ActivTrak raised $20 million in a Series A round of funding with Elsewhere Partners, a growth-stage venture capital firm that principally invests in B2B startups. Rita Selvaggi assumed the role of CEO. In 2020, ActivTrak raised $50M in a Series B round of funding with Sapphire Ventures and Elsewhere Partners. The company also introduced the ActivTrak Productivity Lab, an online resource about workforce productivity research, industry benchmark data, and best practices. == Product == ActivTrak is a workforce analytics and productivity platform that uses reports, dashboards, and data analysis. The platform uses machine learning (AI) to collect and analyze user activity data and produce reports about workforce productivity. The software runs on Microsoft Windows, Mac, Chrome, Terminal Services, and VDI. It includes the ActivTrak Agent, which runs in the background and collects data. It responds to user activity, sensing mouse and keyboard movement in the active window(s) of the user's device. This data is collected and stored in a database that aggregates the data based on the user's request. ActivTrak does not utilize keystroke logging, content scraping, camera access, video recording or mobile device monitoring. The database leverages data analytics to generate account and team benchmarks, and identify productivity patterns and outliers. == Awards == Built In, 100 Best Midsize Places to Work in Austin, 2025 G2, Winter: Best Estimated ROI, High Performer, Best Relationship, Best Support, Users Most Likely to Recommend, Easiest Setup, Easiest Admin, Best Meets Requirements, Users Love Us, 2025 TrustRadius, Buyer’s Choice, 2025 Deloitte Technology Fast 500, No. 468 Fastest-Growing Company, 2024 Product Marketing Alliance, AI Marketing Innovation, 2024 Fortune Best Workplaces in Technology™, 2024 Inc. 5000, No. 2335 of America’s Fastest-Growing Private Companies, 2024 Fortune Best Workplaces in Texas™, 2024 Reworked IMPACT Gold Award: Most Innovative Workplace Productivity Solution, 2024 TrustRadius, Most Loved, 2024 Great Place To Work-Certified™, 2024 Inc. 5000 Regionals: Southwest, 2024 Brandon Hall Group, Best Advance in HR Predictive Analytics Technology, 2024

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  JasPer

  JasPer is a computer software project to create a reference implementation of the codec specified in the JPEG-2000 Part-1 standard (i.e. ISO/IEC 15444-1) - started in 1997 at Image Power Inc. and at the University of British Columbia. It consists of a C library and some sample applications useful for testing the codec. The copyright owner began licensing the code to the public under an MIT License-style license in 2004 in response to requests from the open-source community. As of 2011 JasPer operated as a component of many software projects, both free and proprietary, including (but not limited to) netpbm (as of release 10.12), ImageMagick and KDE (as of version 3.2). As of 22 June 2010 the GEGL graphics library supported JasPer in its latest Git versions. In a series of objective JPEG-2000-compression quality tests conducted in 2004, "JasPer was the best codec, closely followed by IrfanView and Kakadu". However, Jasper remains one of the slowest implementations of the JPEG-2000 codec, as it was designed for reference, not performance. == Etymology == The name "JasPer" has simultaneous connotations with Canada's Jasper National Park, with the semi-precious gemstone, jasper, and with "JP" as an abbreviation of the JPEG-2000 standard.

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  Necrobotics

  Necrobotics is the practice of using biotic materials (or dead organisms) as robotic components. Necrobotics can serve as an alternative to mechanical components that are difficult to manufacture by using biological components designed by natural selection in order to exploit the highly developed selective design implemented in biological lifeforms via the process of evolution. In July 2022, researchers in the Preston Innovation Lab at Rice University in Houston, Texas published a paper in Advanced Science introducing the concept and demonstrating its capability by repurposing dead spiders as robotic grippers and applying pressurized air to activate their gripping arms. In April 2025 researchers at Shinshu University created a “bio-hybrid drone” using silk-worm moth antennae to detect the source of a smell. In November 2025 researchers at McGill University demonstrated the use of a mosquito proboscis as a fine nozzle in experimental 3D printing. Necrobotics utilizes the spider's organic hydraulic system and their compact legs to create an efficient and simple gripper system. The necrobotic spider gripper is capable of lifting small and light objects, thereby serving as an alternative to complex and costly small mechanical grippers. == Background == The main appeal of the spider's body in necrobotics is its compact leg mechanism and use of hydraulic pressure. The spider's anatomy utilizes a simple hydraulic (fluid) pressure system. Spider legs have flexor muscles that naturally constrict their legs when relaxed. A force is required to straighten and extend their legs, which spiders accomplish by pumping hemolymph fluid (blood) through their joints as a means of hydraulic pressure. It takes no external power to curl their legs due to their flexor muscles' natural curled state. In July 2022, researchers in the Preston Innovation Lab at Rice University published a paper detailing their experiments with the gripper. Although dead spiders no longer produce hemolymph, Te Faye Yap (lead author and mechanical engineering graduate) found that pumping air through a needle into the spider's cephalothorax (main body) accomplishes the same results as hemolymph. The original hydraulic (fluid) system is essentially converted into a pneumatic (air) system. == Fabrication == Obtain a spider Euthanize the spider using a cold temperature of around -4°C for 5-7 days Insert a 25 gauge hypodermic needle into the spider's cephalothorax (main body) Apply glue around the needle to form a seal and allow it to dry Connect a syringe or pump to the needle Extend the spider's legs by pumping air in == Testing and Data == === Internal Force Versus Gripping Force === The typical pressure in a resting spider's legs ranges from 4 kPa to 6.1 kPa. Researchers extended the legs by increasing the spider's internal pressure to 5.5 kPa. Pumping air into the body increases the internal pressure, causing the legs to expand. Pumping air out of the body decreases internal pressure, causing the legs to contract due to their flexor leg muscles. When the internal pressure decreases to 0 kPa, the gripper would be fully closed, allowing for the gripper to grasp objects. This action demonstrates that as internal pressure decreases, the gripping force increases. Inversely, when internal pressure increases, the gripping force decreases. By gripping individual weighted acetate beads, it is found that the necrobotic gripper achieves a maximum gripping force of 0.35 milinewtons. === Spider Weight Versus Gripping Force === To estimate the gripping forces of smaller and larger spiders, researchers created a plot to predict the gripping force relative to the size of the spider. The wolf spider's body weight is relatively equal to the gripping force of its legs. The mass of the gripper is 33.5 mg and can lift 1.3 times its body weight (43.6 mg or 0.35 mN). However, with larger spiders, the gripping force relative to body weight decreases. For example, a 200-gram goliath birdeater is predicted to lift 10% of its weight (20 grams or 196 mN). Though there is an inverse relationship between spider mass and gripping force, larger spiders exert greater gripping forces than smaller spiders. === Gripper Lifespan === The necrobotic gripper's functionality is entirely reliant on the structural integrity of the spider. If the spider were to break down easily and frequently, the gripper would not be practical. Using cyclic testing, a series of repeated actions, it is found that the necrobotic gripper can actuate 700 to 1000 times. After 1000 cycles, cracks begin forming on the membrane of the leg joints due to dehydration. Weakened and decomposing joints lead to frequent breakage and replacement, thereby serving as an obstacle in applying necrobotics to real-world scenarios. One theorized fix to this issue is applying beeswax or a lubricant to the joints. Researchers found that over 10 days, the mass of an uncoated spider decreased 17 times more than the mass of a spider coated with beeswax. Lubricating joints combats dehydration and slows the loss of organic material. == Constraints == With the usage of organic material, there is a higher chance of the component decomposing and breaking down as opposed to traditional mechanical systems. There may be additional work and management required to replace these grippers if they fail. Additionally, organic inconsistencies with the spiders will yield inaccurate results. Not all wolf spiders develop the same, so gripping force and leg contraction can vary between grippers. There are moral implications behind euthanizing spiders for robotics. The ethical boundaries that necrobotics push in the pursuit of biohybrid systems raise concerns, as opponents say it may lead to the hybridization of mammals and is intrusive to nature. Proponents respond that repurposing dead animals has been human practice for millennia and that necrobotics should be pursued to advance science.

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  Polynomial texture mapping

  Polynomial texture mapping (PTM), also known as Reflectance Transformation Imaging (RTI), is a technique of imaging and interactively displaying objects under varying lighting conditions to reveal surface phenomena. The data acquisition method is single camera multi light (SCML). == Origins == The method was originally developed by Tom Malzbender of HP Labs in order to generate enhanced 3D computer graphics and it has since been adopted for cultural heritage applications. == Methodology == A series of images is captured in a darkened environment with the camera in a fixed position and the object lit from different angles (Single Camera Multi Light). Interactive software processes and combines the set of images to enable the user inspecting the object to control a virtual light source. The virtual light source may be manipulated to simulate light from different angles and of different intensity or wavelengths to illuminate the surface of artefacts and reveal details. Open-source tools for processing the captured images and publishing the resulting relightable images on the web are freely available. == Applications == Polynomial texture mapping may be used for detailed recording and documentation, 3D modeling, edge detection, and to aid the study of inscriptions, rock art and other artefacts. It has been applied to hundreds of the Vindolanda tablets by the Centre for the Study of Ancient Documents at the University of Oxford in conjunction with the British Museum. It has also been deployed, by Ben Altshuler of the Institute for Digital Archaeology, to scan the Philae obelisk at Kingston Lacy and the Parian Chronicle at the Ashmolean Museum; in both cases scans revealed significant, previously illegible text. Method was also used for identifying microscopic worked antler from Star Carr and recording ancient rock art in Armenia. A 'dome' supporting twenty-four lights has been used to image paintings in the National Gallery and produce polynomial texture maps, providing information on condition phenomena for conservation purposes. Studies of the technique at the National Gallery and Tate concluded that it is an effective tool for documenting changes in the condition of paintings, more easily repeatable than raking light photography, and therefore could be used to assess paintings during structural treatment and before and after loan. Twelve dome-based systems built by the University of Southampton have been used to capture thousands of cuneiform tablets at various museums. The technique is now also finding uses in the field of forensic science, for example in imaging footprints, tyre marks, and indented writing.

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