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Anaconda (Python distribution)

Anaconda is an open source data science and artificial intelligence distribution platform for the Python programming language. Developed by Anaconda, Inc., an American company founded in 2012, the platform is used to develop and manage data science and AI projects. In 2024, Anaconda Inc. has about 300 employees and 45 million users. == History == Co-founded in Austin, Texas in 2012 as Continuum Analytics by Peter Wang and Travis Oliphant, Anaconda Inc. operates from the United States and Europe. Anaconda Inc. developed Conda, a cross-platform, language-agnostic binary package manager. It also launched PyData community workshops and the Jupyter Cloud Notebook service (Wakari.io). In 2013, it received funding from DARPA. In 2015, the company had two million users including 200 of the Fortune 500 companies and raised $24 million in a Series A funding round led by General Catalyst and BuildGroup. Anaconda secured an additional $30 million in funding in 2021. Continuum Analytics rebranded as Anaconda in 2017. That year, it announced the release of Anaconda Enterprise 5, an integration with Microsoft Azure, and had over 13 million users by year's end. In 2022, it released Anaconda Business; new integrations with Snowflake and others; and the open-source PyScript. It also acquired PythonAnywhere, while Anaconda's user base exceeded 30 million in 2022. In 2023, Anaconda released Python in Excel, a new integration with Microsoft Excel, and launched PyScript.com. The company made a series of investments in AI during 2024. That February, Anaconda partnered with IBM to import its repository of Python packages into Watsonx, IBM's generative AI platform. The same year, Anaconda joined IBM's AI Alliance and released an integration with Teradata and Lenovo. In 2024, Anaconda's user base reached 45 million users and Barry Libert was named company CEO, after serving on Anaconda's board of directors. He was succeeded as CEO in October 2025 by David DeSanto, who also became a company director. In May 2025, the company introduced the first unified AI platform for Open Source, Anaconda AI Platform, a central control for AI workflows that enables customization in Python-based enterprise AI development. That July, after reaching over $150 million in a Series C funding round, Anaconda was evaluated at about $1.5 billion. == Overview == Anaconda distribution comes with over 300 packages automatically installed, and over 7,500 additional open-source packages can be installed from the Anaconda repository as well as the Conda package and virtual environment manager. It also includes a GUI, Anaconda Navigator, as a graphical alternative to the command-line interface (CLI). Conda was developed to address dependency conflicts native to the pip package manager, which would automatically install any dependent Python packages without checking for conflicts with previously installed packages (until its version 20.3, which later implemented consistent dependency resolution). The Conda package manager's historical differentiation analyzed and resolved these installation conflicts. Anaconda is a distribution of the Python programming language (and previously also R) for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment. Anaconda distribution includes data-science packages suitable for Windows, Linux, and macOS. Other company products include Anaconda Free, and subscription-based Starter, Business and Enterprise. Anaconda's business tier offers Package Security Manager. Package versions in Anaconda are managed by the package management system Conda, which was spun out as a separate open-source package as useful both independently and for applications other than Python. There is also a small, bootstrap version of Anaconda called Miniconda, which includes only Conda, Python, the packages they depend on, and a small number of other packages. Open source packages can be individually installed from the Anaconda repository, Anaconda Cloud (anaconda.org), or the user's own private repository or mirror, using the conda install command. Anaconda, Inc. compiles and builds the packages available in the Anaconda repository itself, and provides binaries for Windows 32/64 bit, Linux 64 bit and MacOS 64-bit (Intel, Apple Silicon). Anything available on PyPI may be installed into a Conda environment using pip, and Conda will keep track of what it has installed and what pip has installed. Custom packages can be made using the conda build command, and can be shared with others by uploading them to Anaconda Cloud, PyPI or other repositories. The default installation of Anaconda2 includes Python 2.7 and Anaconda3 includes Python 3.7. However, it is possible to create new environments that include any version of Python packaged with Conda. === Anaconda Navigator === Anaconda Navigator is a desktop graphical user interface (GUI) included in Anaconda distribution that allows users to launch applications and manage Conda packages, environments and channels without using command-line commands. Navigator can search for packages on Anaconda Cloud or in a local Anaconda Repository, install them in an environment, run the packages and update them. It is available for Windows, macOS and Linux. The following applications are available by default in Navigator: JupyterLab Jupyter Notebook QtConsole Spyder Glue Orange RStudio Visual Studio Code === Conda === Conda is an open source, cross-platform, language-agnostic package manager and environment management system that installs, runs, and updates packages and their dependencies. It was created for Python programs, but it can package and distribute software for any language, including multi-language projects. The Conda package and environment manager is included in all versions of Anaconda, Miniconda, and Anaconda Repository. == Anaconda.org == Anaconda Cloud is a package management service by Anaconda where users can find, access, store and share public and private notebooks, environments, and Conda and PyPI packages. Cloud hosts useful Python packages, notebooks and environments for a wide variety of applications. Users do not need to log in or to have a Cloud account, to search for public packages, download and install them. Users can build new Conda packages using Conda-build and then use the Anaconda Client CLI to upload packages to Anaconda.org. Notebooks users can be aided with writing and debugging code with Anaconda's AI Assistant.
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Fatsecret

Fatsecret, commonly styled as fatsecret, is a mobile application, website and API that helps people achieve their weight loss goals and find accurate nutrition information. It also offers a weight loss clinic with coaching and medically supported programs. The platform powers global health apps. == History == Fatsecret was founded in 2006 in Melbourne, Australia by Lenny Moses and Rodney Moses. As of 2019, Lenny serves as the company's CEO. The company is known for its calorie counting and meal tracking app, and by April 2016, the company claimed to have 45 million users of its services. In August 2018, a premium version of its app was released. Since August 2009, the company has operated the Fatsecret Platform API, which allows access to its global food and nutrition database. Fatsecret reportedly had 900,000 downloads of its app in January 2020. In an analysis of several Health & Fitness app subcategories for the United States in January 2021, Fatsecret was reported to have the highest 30 day user retention rate of top Calorie Counter + Meal Planner for Weight Loss apps.
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Automated attendant

In telephony, an automated attendant (also auto attendant, auto-attendant, autoattendant, automatic phone menus, AA, or virtual receptionist) allows callers to be automatically transferred to an extension without the intervention of an operator/receptionist. Many AAs will also offer a simple menu system ("for sales, press 1, for service, press 2," etc.). An auto attendant may also allow a caller to reach a live operator by dialing a number, usually "0". Typically the auto attendant is included in a business's phone system such as a PBX, but some services allow businesses to use an AA without such a system. Modern AA services (which now overlap with more complicated interactive voice response or IVR systems) can route calls to mobile phones, VoIP virtual phones, other AAs/IVRs, or other locations using traditional land-line phones or voice message machines. == Feature description == Telephone callers will recognize an automated attendant system as one that greets calls incoming to an organization with a recorded greeting of the form, "Thank you for calling .... If you know your party's extension, you may dial it any time during this message." Callers who have a touch-tone (DTMF) phone can dial an extension number or, in most cases, wait for operator ("attendant") assistance. Since the telephone network does not transmit the DC signals from rotary dial telephones (except for audible clicks), callers who have rotary dial phones have to wait for assistance. On a purely technical level it could be argued that an automated attendant is a very simple kind of IVR however, in the telecom industry the terms IVR and auto attendant are generally considered distinct. An automated attendant serves a very specific purpose (replace live operator and route calls), whereas an IVR can perform all sorts of functions (telephone banking, account inquiries, etc.). An AA will often include a directory which will allow a caller to dial by name in order to find a user on a system. There is no standard format to these directories, and they can use combinations of first name, last name, or both. The following lists common routing steps that are components of an automated attendant: Transfer to extension Transfer to voicemail Play message (i.e., "our address is ...") Go to a sub-menu Repeat choices In addition, an automated attendant would be expected to have values for the following: '0' – where to go when the caller dials '0' Timeout – what to do if the caller does nothing (usually go to the same place as '0') Default mailbox – where to send calls if '0' is not answered (or is not pointing to a live person) == Background == PBXs (private branch exchanges) or PABXs (private automatic branch exchanges) are telephone systems that serve an organization that has many telephone extensions but fewer telephone lines (sometimes called "trunks") that connect that organization to the rest of the global telecommunications network. While persons within an enterprise served by a PBX can call each other by dialing their extension numbers, incoming calls, i.e., calls originating from a telephone not served by the PBX but intended for a party served by the PBX, required assistance from a switchboard operator (also called a "switchboard attendant") or a telephone service called DID ("direct inward dialing"). Direct inward dialing has advantages such as rapid connection to the destination party and disadvantages including cost, lack of identification of the called organization and use of ten-digit telephone numbers. Automated attendants provide, among many other things, a way for an external caller to be directed to an extension or department served by a PBX system without using direct inward dialing or without switchboard attendant assistance. == History == Automated attendants are not part of voicemail systems. Voice messaging (or voicemail or VM) technology has existed since the late 1970s; in the early 1980s companies provided voice-prompting systems that allowed callers to reach (route the call) to an intended party, not necessarily to leave a message. Automated attendant systems are also referred to as automated menu systems and much early work in this field was done by Michael J. Freeman, Ph.D. == Time-based routing == Many auto attendants will have options to allow for time-of-day routing, as well as weekend and holiday routing. The specifics of these features will depend entirely on the particular automated attendant, but typically there would be a normal greeting and routing steps that would take place during normal business hours, and a different greeting and routing for non-business hours.
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Kernel-phase

Kernel-phases are observable quantities used in high resolution astronomical imaging used for superresolution image creation. It can be seen as a generalization of closure phases for redundant arrays. For this reason, when the wavefront quality requirement are met, it is an alternative to aperture masking interferometry that can be executed without a mask while retaining phase error rejection properties. The observables are computed through linear algebra from the Fourier transform of direct images. They can then be used for statistical testing, model fitting, or image reconstruction. == Prerequisites == In order to extract kernel-phases from an image, some requirements must be met: Images are nyquist-sampled (at least 2 pixels per resolution element ( λ D {\displaystyle {\frac {\lambda }{D}}} )) Images are taken in near monochromatic light Exposure time is shorter than the timescale of aberrations Strehl ratio is high (good adaptive optics) Linearity of the pixel response (i.e. no saturation) Deviations from these requirements are known to be acceptable, but lead to observational bias that should be corrected by the observation of calibrators. == Definition == The method relies on a discrete model of the instrument's pupil plane and the corresponding list of baselines to provide corresponding vectors φ {\displaystyle \varphi } of pupil plane errors and Φ {\displaystyle \Phi } of image plane Fourier Phases. When the wavefront error in the pupil plane is small enough (i.e. when the Strehl ratio of the imaging system is sufficiently high), the complex amplitude associated to the instrumental phase in one point of the pupil φ k {\displaystyle \varphi _{k}} , can be approximated by e i φ k ≈ 1 + i φ k {\displaystyle e^{i\varphi _{k}}\approx 1+{\mathit {i}}\varphi _{k}} . This permits the expression of the pupil-plane phase aberrations φ {\displaystyle \varphi } to the image plane Fourier phase as a linear transformation described by the matrix A {\displaystyle A} : Φ = Φ 0 + A ⋅ φ {\displaystyle \Phi =\Phi _{0}+A\cdot \varphi } Where Φ 0 {\displaystyle \Phi _{0}} is the theoretical Fourier phase vector of the object. In this formalism, singular value decomposition can be used to find a matrix K {\displaystyle K} satisfying K ⋅ A = 0 {\displaystyle K\cdot A=0} . The rows of K {\displaystyle K} constitute a basis of the kernel of A T {\displaystyle A^{T}} . K ⋅ Φ = K ⋅ Φ 0 + K ⋅ A ⋅ φ {\displaystyle K\cdot \Phi =K\cdot \Phi _{0}+{\cancel {K\cdot A\cdot \varphi }}} The vector K . Φ {\displaystyle K.\Phi } is called the kernel-phase vector of observables. This equation can be used for model-fitting as it represents the interpretation of a sub-space of the Fourier phase that is immune to the instrumental phase errors to the first order. == Applications == The technique was first used in the re-analysis of archival images from the Hubble Space Telescope where it enabled the discovery of a number of brown dwarf in close binary systems. The technique is used as an alternative to aperture masking interferometry, especially for fainter stars because it does not require the use of masks that typically block 90% of the light, and therefore allows higher throughput. It is also considered to be an alternative to coronagraphy for direct detection of exoplanets at very small separations (below 2 λ D {\displaystyle 2{\frac {\lambda }{D}}} ) where coronagraphs are limited by the wavefront errors of adaptive optics. The same framework can be used for wavefront sensing. In the case of an asymmetric aperture, a pseudo-inverse of A {\displaystyle A} can be used to reconstruct the wavefront errors directly from the image. A Python library called xara is available on GitHub and maintained by Frantz Martinache to facilitate the extraction and interpretation of kernel-phases. The KERNEL project, has received funding from the European Research Council to explore the potential of these observables for a number of use-cases, including direct detection of exoplanets, image reconstruction, and image plane wavefront sensing for adaptive optics.
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Podium (company)

Podium is a private technology company headquartered in Lehi, Utah that develops cloud-based software related to messaging, customer feedback, online reviews, selling products, and requesting payments. == History == Podium was founded in 2014 by Eric Rea and Dennis Steele, who developed a tool to help small businesses "build their online reputation" through online reviews. Podium was initially known as RepDrive before rebranding as Podium in 2015. In 2015, Podium moved from a spare bedroom to a new location above a Provo bike shop. In March 2020, Podium added payments technology to its product suite. In November 2021, Podium raised $201 million in Series D funding and was valued at $3 billion. == Product == Podium is a software-as-a-service platform designed to improve business online reputation. It helps users manage business interactions in one tool. Users can communicate reviews, texts, chats, and post payment directly within the app.
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Fluency Voice Technology

Fluency Voice Technology was a company that developed and sold packaged speech recognition solutions for use in call centers. Fluency's Speech Recognition solutions are used by call centers worldwide to improve customer service and significantly reduce costs and are available on-premises and hosted. == History == 1998 – Fluency was created as a spin-off from the Voice Research & Development team of a company called netdecisions. This R&D operation was established in Cambridge UK. The focus of the development was speech recognition systems based on the VXML standard. 2001 – Fluency became a separate entity in May 2001. Fluency began the creation of a software development platform specifically aimed at automating call center activities. This platform became Fluency's VoiceRunner. 2002 to 2004 – Fluency establishes accomplishes many successful deployments in customer sites such as National Express and Barclaycard. 2003 – Fluency expanded into the USA. Fluency also acquires Vocalis of Cambridge, UK in August 2003. 2004 – Fluency receives £6 million investment from leading European Venture Capitalists and establishes a global OEM partnership with Avaya, and the acquisition of SRC Telecom. 2008 – Fluency is acquired by Syntellect Ltd == Customers == Call Centers around the world use Fluency to improve service and reduce costs. They include Travelodge, Standard Life Bank, Sutton and East Surrey Water, Pizza Hut, CWT, Barclays, Powergen, First Choice, OutRight, J D Williams, Capital Blue Cross, Chelsea Building Society, EDF, bss, TV Licensing and Capita Software Services.
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Fluency Voice Technology

Fluency Voice Technology was a company that developed and sold packaged speech recognition solutions for use in call centers. Fluency's Speech Recognition solutions are used by call centers worldwide to improve customer service and significantly reduce costs and are available on-premises and hosted. == History == 1998 – Fluency was created as a spin-off from the Voice Research & Development team of a company called netdecisions. This R&D operation was established in Cambridge UK. The focus of the development was speech recognition systems based on the VXML standard. 2001 – Fluency became a separate entity in May 2001. Fluency began the creation of a software development platform specifically aimed at automating call center activities. This platform became Fluency's VoiceRunner. 2002 to 2004 – Fluency establishes accomplishes many successful deployments in customer sites such as National Express and Barclaycard. 2003 – Fluency expanded into the USA. Fluency also acquires Vocalis of Cambridge, UK in August 2003. 2004 – Fluency receives £6 million investment from leading European Venture Capitalists and establishes a global OEM partnership with Avaya, and the acquisition of SRC Telecom. 2008 – Fluency is acquired by Syntellect Ltd == Customers == Call Centers around the world use Fluency to improve service and reduce costs. They include Travelodge, Standard Life Bank, Sutton and East Surrey Water, Pizza Hut, CWT, Barclays, Powergen, First Choice, OutRight, J D Williams, Capital Blue Cross, Chelsea Building Society, EDF, bss, TV Licensing and Capita Software Services.
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Non-native speech database

A non-native speech database is a speech database of non-native pronunciations of English. Such databases are used in the development of: multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers, and second language learning systems. == List == The actual table with information about the different databases is shown in Table 2. === Legend === In the table of non-native databases some abbreviations for language names are used. They are listed in Table 1. Table 2 gives the following information about each corpus: The name of the corpus, the institution where the corpus can be obtained, or at least further information should be available, the language which was actually spoken by the speakers, the number of speakers, the native language of the speakers, the total amount of non-native utterances the corpus contains, the duration in hours of the non-native part, the date of the first public reference to this corpus, some free text highlighting special aspects of this database and a reference to another publication. The reference in the last field is in most cases to the paper which is especially devoted to describe this corpus by the original collectors. In some cases it was not possible to identify such a paper. In these cases a paper is referenced which is using this corpus is. Some entries are left blank and others are marked with unknown. The difference here is that blank entries refer to attributes where the value is just not known. Unknown entries, however, indicate that no information about this attribute is available in the database itself. As an example, in the Jupiter weather database no information about the origin of the speakers is given. Therefore this data would be less useful for verifying accent detection or similar issues. Where possible, the name is a standard name of the corpus, for some of the smaller corpora, however, there was no established name and hence an identifier had to be created. In such cases, a combination of the institution and the collector of the database is used. In the case where the databases contain native and non-native speech, only attributes of the non-native part of the corpus are listed. Most of the corpora are collections of read speech. If the corpus instead consists either partly or completely of spontaneous utterances, this is mentioned in the Specials column.
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NIS2 Directive

The Directive (EU) 2022/2555, commonly known as NIS2 is a directive of the European Union aimed at protecting digital infrastructure, in particular critical infrastructure. It broadened the sectors covered by EU network and information security rules and updated incident reporting and oversight compared to the NIS1. Member States were required to transpose NIS2 by 17 October 2024, and the earlier NIS Directive was repealed on 18 October 2024. Only 23 Member States have fully implemented the measures contained with the NIS Directive. Infringement proceedings against them to enforce the Directive have not taken place, and they are not expected to take place in the near future. This failed implementation has led to the fragmentation of cybersecurity capabilities across the EU, with differing standards, incident reporting requirements and enforcement requirements being implemented in different Member States. From the EFTA countries (to April 2026) only Liechtenstein has fully transposed the NIS2 Directive. While the EFTA commission is conducting preparations to transpose the directive into its legislation. == National implementations == === Czech Republic === It is implemented through the Act No. 264/2025 Coll. also called Zákon o kybernetické bezpečnosti (Cybersecurity law) and through another five implementing regulations. The transposing legislation came into force on November 1st, 2025. === Germany === It is implemented through the Gesetz zur Umsetzung der NIS-2-Richtlinie und zur Regelung wesentlicher Grundzüge des Informationssicherheitsmanagements in der Bundesverwaltung. === Ireland === It is implemented through the National Cyber Security Bill. === The Netherlands === It is implemented through the Cyberbeveiligingswet (Cbw). === Slovakia === It is implemented through via an amendment of the Act No. 69/2018 Coll. also called Zákon o kybernetickej bezpečnosti a o zmene a doplnení niektorých zákonov (Law on Cybersecurity and change and amendment of certain laws). It came into force on November 1st, 2025. === Spain === It is implemented through the Esquema Nacional de Seguridad (ENS).
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Pandemonium architecture

Pandemonium architecture is a theory in cognitive science that describes how visual images are processed by the brain. It has applications in artificial intelligence and pattern recognition. The theory was introduced by the artificial intelligence pioneer Oliver Selfridge in his 1959 paper "Pandemonium - A Paradigm for Learning". It describes the process of object recognition as the exchange of signals within a hierarchical system of detection and association, the elements of which Selfridge metaphorically termed "demons". This model is now recognized as the basis of visual perception in cognitive science. Pandemonium architecture arose in response to the inability of template matching theories to offer a biologically plausible explanation of the image constancy phenomenon. Contemporary researchers praise this architecture for its elegancy and creativity; that the idea of having multiple independent systems (e.g., feature detectors) working in parallel to address the image constancy phenomena of pattern recognition is powerful yet simple. The basic idea of the pandemonium architecture is that a pattern is first perceived in its parts before the "whole". Pandemonium architecture was one of the first computational models in pattern recognition. Although not perfect, the pandemonium architecture influenced the development of modern connectionist, artificial intelligence, and word recognition models. == History == Most research in perception has been focused on the visual system, investigating the mechanisms of how we see and understand objects. A critical function of our visual system is its ability to recognize patterns, but the mechanism by which this is achieved is unclear. The earliest theory that attempted to explain how we recognize patterns is the template matching model. According to this model, we compare all external stimuli against an internal mental representation. If there is "sufficient" overlap between the perceived stimulus and the internal representation, we will "recognize" the stimulus. Although some machines follow a template matching model (e.g., bank machines verifying signatures and accounting numbers), the theory is critically flawed in explaining the phenomena of image constancy: we can easily recognize a stimulus regardless of the changes in its form of presentation (e.g., T and T are both easily recognized as the letter T). It is highly unlikely that we have a stored template for all of the variations of every single pattern. As a result of the biological plausibility criticism of the template matching model, feature detection models began to rise. In a feature detection model, the image is first perceived in its basic individual elements before it is recognized as a whole object. For example, when we are presented with the letter A, we would first see a short horizontal line and two slanted long diagonal lines. Then we would combine the features to complete the perception of A. Each unique pattern consists of different combination of features, which means those that are formed with the same features will generate the same recognition. That is, regardless of how we rotate the letter A, is still perceived as the letter A. It is easy for this sort of architecture to account for the image constancy phenomena because you only need to "match" at the basic featural level, which is presumed to be limited and finite, thus biologically plausible. The best known feature detection model is called the pandemonium architecture. == Pandemonium architecture == The pandemonium architecture was originally developed by Oliver Selfridge in the late 1950s. The architecture is composed of different groups of "demons" working independently to process the visual stimulus. Each group of demons is assigned to a specific stage in recognition, and within each group, the demons work in parallel. There are four major groups of demons in the original architecture. The concept of feature demons, that there are specific neurons dedicated to perform specialized processing is supported by research in neuroscience. Hubel and Wiesel found there were specific cells in a cat's brain that responded to specific lengths and orientations of a line. Similar findings were discovered in frogs, octopuses and a variety of other animals. Octopuses were discovered to be only sensitive to verticality of lines, whereas frogs demonstrated a wider range of sensitivity. These animal experiments demonstrate that feature detectors seem to be a very primitive development. That is, it did not result from the higher cognitive development of humans. Not surprisingly, there is also evidence that the human brain possesses these elementary feature detectors as well. Moreover, this architecture is capable of learning, similar to a back-propagation styled neural network. The weight between the cognitive and feature demons can be adjusted in proportion to the difference between the correct pattern and the activation from the cognitive demons. To continue with our previous example, when we first learned the letter R, we know is composed of a curved, long straight, and a short angled line. Thus when we perceive those features, we perceive R. However, the letter P consists of very similar features, so during the beginning stages of learning, it is likely for this architecture to mistakenly identify R as P. But through constant exposure of confirming R's features to be identified as R, the weights of R's features to P are adjusted so the P response becomes inhibited (e.g., learning to inhibit the P response when a short angled line is detected). In principle, a pandemonium architecture can recognize any pattern. As mentioned earlier, this architecture makes error predictions based on the amount of overlapping features. Such as, the most likely error for R should be P. Thus, in order to show this architecture represents the human pattern recognition system we must put these predictions into test. Researchers have constructed scenarios where various letters are presented in situations that make them difficult to identify; then types of errors were observed, which was used to generate confusion matrices: where all of the errors for each letter are recorded. Generally, the results from these experiments matched the error predictions from the pandemonium architecture. Also as a result of these experiments, some researchers have proposed models that attempted to list all of the basic features in the Roman alphabet. == Criticism == A major criticism of the pandemonium architecture is that it adopts a completely bottom-up processing: recognition is entirely driven by the physical characteristics of the targeted stimulus. This means that it is unable to account for any top-down processing effects, such as context effects (e.g., pareidolia), where contextual cues can facilitate (e.g., word superiority effect: it is relatively easier to identify a letter when it is part of a word than in isolation) processing. However, this is not a fatal criticism to the overall architecture, because is relatively easy to add a group of contextual demons to work along with the cognitive demons to account for these context effects. Although the pandemonium architecture is built on the fact that it can account for the image constancy phenomena, some researchers have argued otherwise; and pointed out that the pandemonium architecture might share the same flaws from the template matching models. For example, the letter H is composed of 2 long vertical lines and a short horizontal line; but if we rotate the H 90 degrees in either direction, it is now composed of 2 long horizontal lines and a short vertical line. In order to recognize the rotated H as H, we would need a rotated H cognitive demon. Thus we might end up with a system that requires a large number of cognitive demons in order to produce accurate recognition, which would lead to the same biological plausibility criticism of the template matching models. However, it is rather difficult to judge the validity of this criticism because the pandemonium architecture does not specify how and what features are extracted from incoming sensory information, it simply outlines the possible stages of pattern recognition. But of course that raises its own questions, to which it is almost impossible to criticize such a model if it does not include specific parameters. Also, the theory appears to be rather incomplete without defining how and what features are extracted, which proves to be especially problematic with complex patterns (e.g., extracting the weight and features of a dog). Some researchers have also pointed out that the evidence supporting the pandemonium architecture has been very narrow in its methodology. Majority of the research that supports this architecture has often referred to its ability to recognize simple schematic drawings that are selected from a small finite set (e.g., letters in the Roman alphabet). Evidence from these types of exper
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Picture Prowler

Picture Prowler was an early piece of photo management software developed around and meant to show off Xing Technology's JPEG image decompression library during the early 1990s. Little known today, it featured thumbnail based picture management, printing, etc. The primary developer was Ray Bunnage from compression / decompression libraries developed by Howard Gordon and Chris Eddy.
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Sorenson Squeeze

Sorenson Squeeze was a software video encoding tool used to compress and convert video and audio files on Mac OS X or Windows operating systems. It was sold as a standalone tool and has also long been bundled with Avid Media Composer. == History == Sorenson Squeeze was first announced on July 17, 2001, as the first variable bit rate (VBR) compression application for Mac OS X, and was released on October 29 of that same year. By March 2002, Sorenson Squeeze became available for Windows OS. Sorenson Squeeze was originally released as a tool for encoding videos for the Web and QuickTime playback but began adding new codecs as more versions were released. The software was discontinued by Sorenson in January 2019, and correspondingly was no longer offered as part of Avid Media Composer. == Features == Squeeze included a number of features to improve video & audio quality. Features included: GPU accelerated H.264 encoding, adaptive bitrate encoding, HD encoding and Dolby certified AC3 Audio. Intelligent encoding presets available in Squeeze included: x265 (H.265) MainConcept H.264 and MainConcept H.264 CUDA. Adaptive bitrate encoding allows for optimal bitrate and error resilience based on network conditions, resulting in a dynamic adjustment of the video bitstream being delivered. It encoded to multiple formats including QuickTime, Windows Media, Flash Video, Silverlight, WebM & WMV. It uses multiple codecs, including the Sorenson codecs SV3 Pro and Spark, H.265, H.264, H.263, VP6, VC1, MPEG2, and many others. Squeeze operates on the Apple Macintosh and Microsoft Windows operating systems. Squeeze offers native plugins to Avid, Apple Final Cut Pro and Adobe Premiere (CS4, CS5) NLEs. Each copy of Squeeze included the Dolby Certified AC3 Consumer encoder. Squeeze also included a simplified review and approval process, which allows the user to automatically send secure, password protected videos for immediate review. Instant feedback is received via Web or mobile. == Versions == Sorenson Squeeze was released on October 29, 2001. Sorenson Squeeze for Macromedia Flash MX was released on March 14, 2002. Sorenson Squeeze 3 for MPEG-4 was released in January 2003. Sorenson Squeeze 3 Compression Suite was released in January 2003. Sorenson Squeeze 5 was released on March 31, 2008. Sorenson Squeeze was updated to version 5.1 on May 11, 2009. Sorenson Squeeze 6 was released on November 3, 2009. Sorenson Squeeze 7 was released January 25, 2011. Sorenson Squeeze 11 was released August 27, 2016. == Awards == Streaming Media magazine Readers’ Choice Award for Encoding Software for 2007, 2008, 2009 and 2010. 2008 Vanguard Award from Digital Content Producer magazine == Squeeze 7 system requirements == Windows Pentium IV-based computer or greater Windows XP, Vista or 7 32- and 64-bit compatible (including AVID 64-bit update); Faster performance on 64-bit systems 512 MB RAM 120 MB available hard drive space QuickTime 7.2 or later DirectX 9.0b or later Macintosh Intel-based processor Mac OS 10.4 or later 32- and 64-bit compatible; Faster performance on 64-bit systems 512 MB RAM 120 MB available hard drive space QuickTime 7.2 or later
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Semi-automation

Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.
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Parasolid

Parasolid is a geometric modeling kernel originally developed by Shape Data Limited, now owned and developed by Siemens Digital Industries Software. It can be licensed by other companies for use in their 3D computer graphics software products. Parasolid's abilities include model creation and editing utilities such as Boolean modeling operators, feature modeling support, advanced surfacing, thickening and hollowing, blending and filleting, and sheet modeling. It also incorporates modeling with mesh surfaces and lattices. Parasolid also includes tools for direct model editing, including tapering, offsetting, geometry replacement and removing feature details with automated regeneration of surrounding data. Parasolid also provides wide-ranging graphical and rendering support, including hidden-line, wireframe and drafting, tessellation, and model data inquiries. To use Parasolid effectively, software developers need knowledge of CAD in general, computational geometry, and topology. Parasolid is available for Windows (32-bit, 64-bit and AArch64), Linux (64-bit and AArch64), macOS (Apple silicon and Intel), iOS, and Android. == Parasolid XT format == Parasolid parts are normally saved in XT format, which usually has the file extension .X_T. The format is documented and open. There is also a binary version of the format, usually with an .X_B extension, which is somewhat more compact. Both .X_T and .X_B are used for parts files. == Applications == It is used in many computer-aided design (CAD), computer-aided manufacturing (CAM), computer-aided engineering (CAE), product visualization, and CAD data exchange packages. Notable uses include:
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Shepp–Logan phantom

The Shepp–Logan phantom is a standard test image created by Larry Shepp and Benjamin F. Logan for their 1974 paper "The Fourier Reconstruction of a Head Section". It serves as the model of a human head in the development and testing of image reconstruction algorithms. == Definition == The function describing the phantom is defined as the sum of 10 ellipses inside a 2×2 square:
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