AI Generator App Free

AI Generator App Free — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Biopython

  Biopython is an open-source collection of non-commercial Python modules for computational biology and bioinformatics. It makes robust and well-tested code easily accessible to researchers. Python is an object-oriented programming language and is a suitable choice for automation of common tasks. The availability of reusable libraries saves development time and lets researchers focus on addressing scientific questions. Biopython is constantly updated and maintained by a large team of volunteers across the globe. Biopython contains parsers for diverse bioinformatic sequence, alignment, and structure formats. Sequence formats include FASTA, FASTQ, GenBank, and EMBL. Alignment formats include Clustal, BLAST, PHYLIP, and NEXUS. Structural formats include the PDB, which contains the 3D atomic coordinates of the macromolecules. It has provisions to access information from biological databases like NCBI, Expasy, PBD, and BioSQL. This can be used in scripts or incorporated into their software. Biopython contains a standard sequence class, sequence alignment, and motif analysis tools. It also has clustering algorithms, a module for structural biology, and a module for phylogenetics analysis. == History == The development of Biopython began in 1999, and it was first released in July 2000. First "semi-complete" and "semi-stable" release was done in March 2001 and December 2002 respectively. It was developed during a similar time frame and with analogous goals to other projects that added bioinformatics capabilities to their respective programming languages, including BioPerl, BioRuby and BioJava. Early developers on the project included Jeff Chang, Andrew Dalke and Brad Chapman, though over 100 people have made contributions to date. In 2007, a similar Python project, namely PyCogent, was established. The initial scope of Biopython involved accessing, indexing and processing biological sequence files. The retrieved data from common biological databases will then be parsed into a python data structure. While this is still a major focus, over the following years added modules have extended its functionality to cover additional areas of biology. The key challenge in the design of parsers for bioinformatics file formats is the frequency at which the data formats change. This is due to inadequate curation of the structure of the data, and changes in the database contents. This problem is overcome by the application of a standard event-oriented parser design (see Key features and examples). As of version 1.77, Biopython no longer supports Python 2. The current stable release of Biopython version 1.85 was released on 15 January 2025. It only supports Python 3 and the recent releases of Biopython require NumPy (and not Numeric). == Design == Wherever possible, Biopython follows the conventions used by the Python programming language to make it easier for users familiar with Python. For example, Seq and SeqRecord objects can be manipulated via slicing, in a manner similar to Python's strings and lists. It is also designed to be functionally similar to other Bio projects, such as BioPerl. It is organized into modular sub-packages, e.g., Bio.Seq, Bio.Align, Bio.PDB, Bio.Entrez each of them useful in a different bioinformatics domain. It used principles, like encapsulation and polymorphism, notably in classes Seq, SeqRecord, and Bio.PDB.Structure. It can also interoperate with other Python tools (Pandas, Matplotlib and SciPy). Biopython can read and write most common file formats for each of its functional areas, and its license is permissive and compatible with most other software licenses, which allows Biopython to be used in a variety of software projects. == Requirements == Biopython is currently supported and tested with the following Python implementations: Python 3 or PyPy3 NumPy == Key features and examples == === Input and output === Biopython can read and write to a number of common formats. When reading files, descriptive information in the file is used to populate the members of Biopython classes, such as SeqRecord. This allows records of one file format to be converted into others. Very large sequence files can exceed a computer's memory resources, so Biopython provides various options for accessing records in large files. They can be loaded entirely into memory in Python data structures, such as lists or dictionaries, providing fast access at the cost of memory usage. Alternatively, the files can be read from disk as needed, with slower performance but lower memory requirements. === Sequences === A core concept in Biopython is the biological sequence, and this is represented by the Seq class. A Biopython Seq object is similar to a Python string in many respects: it supports the Python slice notation, can be concatenated with other sequences and is immutable. This object includes both general string-like and biological sequence-specific methods. It is best to store information about the biological type (DNA, RNA, protein) separately from the sequence, rather than using an explicit alphabet argument. === Sequence annotation === The SeqRecord class describes sequences, along with information such as name, description and features in the form of SeqFeature objects. Each SeqFeature object specifies the type of the feature and its location. Feature types can be ‘gene’, ‘CDS’ (coding sequence), ‘repeat_region’, ‘mobile_element’ or others, and the position of features in the sequence can be exact or approximate. === Accessing online databases === Through the Bio.Entrez module, users of Biopython can download biological data from NCBI databases. Each of the functions provided by the Entrez search engine is available through functions in this module, including searching for and downloading records. === Phylogeny === The Bio.Phylo module provides tools for working with and visualising phylogenetic trees. A variety of file formats are supported for reading and writing, including Newick, NEXUS and phyloXML. Common tree manipulations and traversals are supported via the Tree and Clade objects. Examples include converting and collating tree files, extracting subsets from a tree, changing a tree's root, and analysing branch features such as length or score. Rooted trees can be drawn in ASCII or using matplotlib (see Figure 1), and the Graphviz library can be used to create unrooted layouts (see Figure 2). === Genome diagrams === The GenomeDiagram module provides methods of visualising sequences within Biopython. Sequences can be drawn in a linear or circular form (see Figure 3), and many output formats are supported, including PDF and PNG. Diagrams are created by making tracks and then adding sequence features to those tracks. By looping over a sequence's features and using their attributes to decide if and how they are added to the diagram's tracks, one can exercise much control over the appearance of the final diagram. Cross-links can be drawn between different tracks, allowing one to compare multiple sequences in a single diagram. === Macromolecular structure === The Bio.PDB module can load molecular structures from PDB and mmCIF files, and was added to Biopython in 2003. The Structure object is central to this module, and it organises macromolecular structure in a hierarchical fashion: Structure objects contain Model objects which contain Chain objects which contain Residue objects which contain Atom objects. Disordered residues and atoms get their own classes, DisorderedResidue and DisorderedAtom, that describe their uncertain positions. Using Bio.PDB, one can navigate through individual components of a macromolecular structure file, such as examining each atom in a protein. Common analyses can be carried out, such as measuring distances or angles, comparing residues and calculating residue depth. === Population genetics === The Bio.PopGen module adds support to Biopython for Genepop, a software package for statistical analysis of population genetics. This allows for analyses of Hardy–Weinberg equilibrium, linkage disequilibrium and other features of a population's allele frequencies. This module can also carry out population genetic simulations using coalescent theory with the fastsimcoal2 program. === Wrappers for command line tools === Biopython previously included command-line wrappers for tools such as BLAST, Clustal, EMBOSS, and SAMtools. This option allowed users to run external tool commands from within the code using specialized Biopython classes. However, Bio.Application modules and their wrappers have deprecated and will be removed in future Biopython releases. The main reason for this is the high maintenance burden of updating them with the evolving external tools. The recommended approach is to directly construct and execute command-line tool commands using Python’s built-in subprocess module. This method provides flexibility and removes the dependency on the Biopython wrappers. subprocess is a native Python module useful for running ext

  Read more →
• 

  Friendica

  Friendica (formerly Friendika, originally Mistpark) is a free and open-source software distributed social network. It forms one part of the Fediverse, an interconnected and decentralized network of independently operated servers. == Features == Friendica users can connect with others via their own Friendica server, but may also fully integrate contacts from other platforms including Diaspora, Pump.io, GNU social, email, Discourse and more recently ActivityPub (including Mastodon, Pleroma and Pixelfed) and Bluesky into their 'newsfeed'. In addition to these two way connections, users can also use Friendica as a publishing platform to post content to WordPress, Tumblr, Insanejournal and Libertree. Posting to Google+ was also supported until that service was shut down. In addition, RSS feeds can be ingested. Because users are distributed across many servers, their "addresses" consist of a username, the "@" symbol, and the domain name of the Friendica instance in the same manner email addresses are formed. Twitter support was available but was deprecated due to API changes under Elon Musk's leadership rendering it unusable. Most of the functionality from major microblogging and social networking platforms are available in Friendica; for example, tagging users and groups via "@ mentions"; direct messages; hashtags; photo albums; "likes"; "dislikes"; comments; and re-shares of publicly visible posts. Published items can be edited and updated across the network. Comprehensive settings for privacy and the public visibility of posts allow users to regulate who can read which contributions, or see specific information about the user. Users can also create multiple profiles, allowing different groups of people (such as friends, or work mates) to see a different profile entirely when viewing the same page. User accounts can be downloaded or deleted, and can be imported to a different Friendica server if so required. Public forums can be created under different accounts, which can be switched between if the accounts are registered with the same email address. == Development == There is no corporation behind Friendica. The developers work on a voluntary basis and the project is run informally; the platform itself is used for the communication between the developers. There are different forums within Friendica, such as "Friendica Developers" and "Friendica Support". The source code of Friendica is hosted on GitHub. == Installation == The developers aim to make installation of the software as simple as possible for technical laymen. They argue that decentralization on small servers is a key condition for the freedom of users and their self-determination. The difficulty level is similar to an installation of WordPress. However, the installing on shared hosting is sometimes difficult because of missing PHP5 modules. Some volunteers also run public servers so that newcomers can also avoid the installation of their own software. == List of clients == Friendica implements multiple client-server API variants simultaneously. Along with endpoints needed to use enhanced Friendica features, it also implements the API used by GNU social, Twitter and since version 2021.06 also the one used by Mastodon. As a result, most GNU social and Mastodon clients can be used for Friendica. Examples of Friendica compatible clients include: Raccoon for Friendica, Friendiqa, Fedilab, AndStatus, Twidere and DiCa for Android, friendly for Sailfish OS, friclicli (CLI client), choqok and Friendiqa for Linux and Friendica Mobile for Windows 10. == Reception == Friendica was cited in January 2012 by Infoshop News as an "alternative to Google+ and Facebook" to be used on the Occupy Nigeria movement. In January 2012 Free Software Foundation Europe's blog cited Friendica as a reasonable alternative to centralized and controlled social networks such as Facebook or Google+. Biblical Notes writer J. Randal Matheny described Friendica in January 2012 as "One social networking option flying under the radar until recently deserves consideration as an already stable platform with a wide range of options, applications, plug-ins, and possibilities for opening up the Internet." In February 2012, the German computer magazine c't wrote: "Friendica demonstrates how decentralized social networks can become widely accepted." Another German publication, the professional magazine t3n listed Friendica as a Facebook rival in an online article in March 2012 about Facebook alternatives. It compared Friendica with similar social networks like Diaspora and identi.ca. MSN Tech & Gadgets contributor Emma Boyes wrote about Friendica in May 2012: "why you'll love it: you can use it to access all the other social networks and get recommendations of new friends and groups to join. Friendica is open source and decentralised. There's no corporation behind it and there are extensive privacy settings. You can choose from a variety of user interfaces and it boasts some cool features—for instance, being able to key in a list of your interests and use the 'profile match' feature to recommend other users who share them with you. A word of warning, though, the site is not as user-friendly as the others on this list, so it may be this one is one for the geeks." == Later reviews == Acquisition of Twitter by Elon Musk had revitalized public interest in Fediverse technologies in April 2022. Friendica received favorable reviews, with a PCMag article describing it as "mostly comparable to Facebook", drawing a parallel to Google+ and highlighting using it "for planning events, and its multiple profile feature means you can show a different face to your friends, coworkers, and family". The September 2022 issue of Linux Magazine contains a detailed comparison and walk-through of registering to and using basic functions of Diaspora, Friendica and Mastodon. They describe Friendica as "intuitive" and highlight the "huge choice of account settings" and that "Friendica does not require any specific hardware, so you can use an old computer system as a server." == Vulnerabilities == In September 2020, a hotfix was released to patch a security vulnerability that could leak sensitive information from the server environment since versions released in April 2019 (develop branch) and June 2019 (stable).

  Read more →
• 

  Contrast-to-noise ratio

  Contrast-to-noise ratio (CNR) is a measure used to determine image quality. CNR is similar to the metric signal-to-noise ratio (SNR), but subtracts a term before taking the ratio. This is important when there is a significant bias in an image, such as from haze. As can be seen in the picture at right, the intensity is rather high even though the features of the image are washed out by the haze. Thus this image may have a high SNR metric, but will have a low CNR metric. One way to define contrast-to-noise ratio is: C = | S A − S B | σ o {\displaystyle C={\frac {|S_{A}-S_{B}|}{\sigma _{o}}}} where SA and SB are signal intensities for signal producing structures A and B in the region of interest and σo is the standard deviation of the pure image noise.

  Read more →
• 

  Color space

  A color space is a specific organization of colors. In combination with color profiling supported by various physical devices, it supports reproducible representations of color – whether such representation entails an analog or a digital representation. A color space may be arbitrary, i.e. with physically realized colors assigned to a set of physical color swatches with corresponding assigned color names (including discrete numbers in – for example – the Pantone collection), or structured with mathematical rigor (as with the NCS System, Adobe RGB and sRGB). A "color space" is a useful conceptual tool for understanding the color capabilities of a particular device or digital file. When trying to reproduce color on another device, color spaces can show whether shadow/highlight detail and color saturation can be retained, and by how much either will be compromised. A "color model" is an abstract mathematical model describing the way colors can be represented as tuples of numbers (e.g. triples in RGB or quadruples in CMYK); however, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with no connection to any globally understood system of color interpretation. Adding a specific mapping function between a color model and a reference color space establishes within the reference color space a definite "footprint", known as a gamut, and for a given color model, this defines a color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB color model. When defining a color space, the usual reference standard is the CIELAB or CIEXYZ color spaces, which were specifically designed to encompass all colors the average human can see. Since "color space" identifies a particular combination of the color model and the mapping function, the word is often used informally to identify a color model. However, even though identifying a color space automatically identifies the associated color model, this usage is incorrect in a strict sense. For example, although several specific color spaces are based on the RGB color model, there is no such thing as the singular RGB color space. == History == In 1802, Thomas Young postulated the existence of three types of photoreceptors (now known as cone cells) in the eye, each of which was sensitive to a particular range of visible light. Hermann von Helmholtz developed the Young–Helmholtz theory further in 1850: that the three types of cone photoreceptors could be classified as short-preferring (blue), middle-preferring (green), and long-preferring (red), according to their response to the wavelengths of light striking the retina. The relative strengths of the signals detected by the three types of cones are interpreted by the brain as a visible color. But it is not clear that they thought of colors as being points in color space. The color-space concept was likely due to Hermann Grassmann, who developed it in two stages. First, he developed the idea of vector space, which allowed the algebraic representation of geometric concepts in n-dimensional space. Fearnley-Sander (1979) describes Grassmann's foundation of linear algebra as follows: The definition of a linear space (vector space)... became widely known around 1920, when Hermann Weyl and others published formal definitions. In fact, such a definition had been given thirty years previously by Peano, who was thoroughly acquainted with Grassmann's mathematical work. Grassmann did not put down a formal definition—the language was not available—but there is no doubt that he had the concept. With this conceptual background, in 1853, Grassmann published a theory of how colors mix; it and its three color laws are still taught, as Grassmann's law. As noted first by Grassmann... the light set has the structure of a cone in the infinite-dimensional linear space. As a result, a quotient set (with respect to metamerism) of the light cone inherits the conical structure, which allows color to be represented as a convex cone in the 3- D linear space, which is referred to as the color cone. == Examples == Colors can be created in printing with color spaces based on the CMYK color model, using the subtractive primary colors of pigment (cyan, magenta, yellow, and key [black]). To create a three-dimensional representation of a given color space, we can assign the amount of magenta color to the representation's X axis, the amount of cyan to its Y axis, and the amount of yellow to its Z axis. The resulting 3-D space provides a unique position for every possible color that can be created by combining those three pigments. Colors can be created on computer monitors with color spaces based on the RGB color model, using the additive primary colors (red, green, and blue). A three-dimensional representation would assign each of the three colors to the X, Y, and Z axes. Colors generated on a given monitor will be limited by the reproduction medium, such as the phosphor (in a CRT monitor) or filters and backlight (LCD monitor). Another way of creating colors on a monitor is with an HSL or HSV color model, based on hue, saturation, brightness (value/lightness). With such a model, the variables are assigned to cylindrical coordinates. Many color spaces can be represented as three-dimensional values in this manner, but some have more, or fewer dimensions, and some, such as Pantone, cannot be represented in this way at all. == Conversion == Color space conversion is the translation of the representation of a color from one basis to another. This typically occurs in the context of converting an image that is represented in one color space to another color space, the goal being to make the translated image look as similar as possible to the original. == RGB density == The RGB color model is implemented in different ways, depending on the capabilities of the system used. The most common incarnation in general use as of 2021 is the 24-bit implementation, with 8 bits, or 256 discrete levels of color per channel. Any color space based on such a 24-bit RGB model is thus limited to a range of 256×256×256 ≈ 16.7 million colors. Some implementations use 16 bits per component for 48 bits total, resulting in the same gamut with a larger number of distinct colors. This is especially important when working with wide-gamut color spaces (where most of the more common colors are located relatively close together), or when a large number of digital filtering algorithms are used consecutively. The same principle applies for any color space based on the same color model, but implemented at different bit depths. == Lists == CIE 1931 XYZ color space was one of the first attempts to produce a color space based on measurements of human color perception (earlier efforts were by James Clerk Maxwell, König & Dieterici, and Abney at Imperial College) and it is the basis for almost all other color spaces. The CIERGB color space is a linearly-related companion of CIE XYZ. Additional derivatives of CIE XYZ include the CIELUV, CIEUVW, and CIELAB. === Generic === RGB uses additive color mixing, because it describes what kind of light needs to be emitted to produce a given color. RGB stores individual values for red, green and blue. RGBA is RGB with an additional channel, alpha, to indicate transparency. Common color spaces based on the RGB model include sRGB, Adobe RGB, ProPhoto RGB, scRGB, and CIE RGB. CMYK uses subtractive color mixing used in the printing process, because it describes what kind of inks need to be applied so the light reflected from the substrate and through the inks produces a given color. One starts with a white substrate (canvas, page, etc.), and uses ink to subtract color from white to create an image. CMYK stores ink values for cyan, magenta, yellow and black. There are many CMYK color spaces for different sets of inks, substrates, and press characteristics (which change the dot gain or transfer function for each ink and thus change the appearance). YIQ was formerly used in NTSC (North America, Japan and elsewhere) television broadcasts for historical reasons. This system stores a luma value roughly analogous to (and sometimes incorrectly identified as) luminance, along with two chroma values as approximate representations of the relative amounts of blue and red in the color. It is similar to the YUV scheme used in most video capture systems and in PAL (Australia, Europe, except France, which uses SECAM) television, except that the YIQ color space is rotated 33° with respect to the YUV color space and the color axes are swapped. The YDbDr scheme used by SECAM television is rotated in another way. YPbPr is a scaled version of YUV. It is most commonly seen in its digital form, YCbCr, used widely in video and image compression schemes such as MPEG and JPEG. xvYCC is an international digital video color space standard published by the IEC (IEC 61966-2-4). It is based on the ITU BT.601 and BT.709

  Read more →
• 

  Computer Graphics: Principles and Practice

  Computer Graphics: Principles and Practice is a textbook written by James D. Foley, Andries van Dam, Steven K. Feiner, John Hughes, Morgan McGuire, David F. Sklar, and Kurt Akeley and published by Addison–Wesley. First published in 1982 as Fundamentals of Interactive Computer Graphics, it is widely considered a classic standard reference book on the topic of computer graphics. It is sometimes known as the bible of computer graphics (due to its size). == Editions == === First Edition === The first edition, published in 1982 and titled Fundamentals of Interactive Computer Graphics, discussed the SGP library, which was based on ACM's SIGGRAPH CORE 1979 graphics standard, and focused on 2D vector graphics. === Second Edition === The second edition, published 1990, was completely rewritten and covered 2D and 3D raster and vector graphics, user interfaces, geometric modeling, anti-aliasing, advanced rendering algorithms and an introduction to animation. The SGP library was replaced by SRGP (Simple Raster Graphics Package), a library for 2D raster primitives and interaction handling, and SPHIGS (Simple PHIGS), a library for 3D primitives, which were specifically written for the book. === Second Edition in C === In the second edition in C, published in 1995, all examples were converted from Pascal to C. New implementations for the SRGP and SPHIGS graphics packages in C were also provided. === Third Edition === A third edition covering modern GPU architecture was released in July 2013. Examples in the third edition are written in C++, C#, WPF, GLSL, OpenGL, G3D, or pseudocode. == Awards == The book has won a Front Line Award (Hall of Fame) in 1998.

  Read more →
• 

  Native cloud application

  A native cloud application (NCA) is a type of computer software that natively utilizes services and infrastructure from cloud computing providers such as Amazon EC2, Force.com, or Microsoft Azure. NCAs exhibit a combined usage of the three fundamental technologies: Computational grid - loosely, e.g. MapReduce Data grids (e.g. distributed in-memory data caches) Auto-scaling on any managed infrastructure

  Read more →
• 

  Microsoft Clipchamp

  Microsoft Clipchamp is a freemium video editing tool developed by Australian company Clipchamp Pty Ltd., a subsidiary of Microsoft. It is a web-based, non-linear editing software that allows users to import, edit, and export audiovisual material in a web browser window. The application is designed to be easy to use for beginners. Clipchamp has offices in Australia, the Philippines, Germany, and the United States. According to figures published by the company, at the beginning of 2021, it had more than 14 million users worldwide. In September 2021, Clipchamp Pty Ltd. was acquired by Microsoft. It has since been offered in a personal version through a Microsoft account and in a business or education version through a work or school account that is built on OneDrive and SharePoint. == Features == Microsoft Clipchamp has multiple features that allow further creativity and accessibility. Since July 2023, users can drag and drop files from their computer, OneDrive, and SharePoint (images, sound & video files) into a list of all media uploaded or inserted. Users can insert media into the video timeline as many times as they want. Users can replace an image, sound, or video clip with another by dragging and dropping it over the target. There is also a Gap Remover tool that removes gaps in the video. Videos can be trimmed, along with timings that can be edited. The user can crop videos and images, too. Text can be added anywhere on the screen, and can be in many fonts, and the size can be changed, too. Specific text color can be selected using presets or an HSV picker, and specific Text Styles (bold, medium, italics, normal) can be selected. The aspect ratio can also be selected, including 16:9, 9:16, 1:1, 4:5, 2:3, and 21:9. Clipchamp also supports numerous effects and transitions for videos and images. The user can export videos in 480p, 720p, and 1080p for free. Exporting GIFs are possible, while the video has to be 15 seconds or less. Microsoft Clipchamp uses a hybrid model of desktop and online application. In the personal version of Clipchamp (on Windows and in a web browser), video processing is all done locally on the computer and mobile phones, but the app itself runs online as a browser-based web app. This is done by uploading and saving project data and information like file names online but not the associated media files themselves. In the work version of Clipchamp, which is a part of Microsoft 365, media files are still processed locally but are automatically backed up to the user's OneDrive or SharePoint work or school account so that it can be accessed anywhere. This version also has integration with other Microsoft productivity services like Microsoft Teams and Microsoft Stream. == History == Clipchamp Pty Ltd. was founded as a startup company by Alexander Dreiling (current CEO), Dave Hewitt, Tobias Raub and Soeren Balko, in Brisbane, Australia, in 2013. In an interview given to SmartCompany, Dreiling commented that at first, the company was "trying to build an enormous, distributed supercomputer". Among the first software developed by the company's team was a tool for video compression and conversion. 2014 saw the official launch of the first version of the free, audiovisual browser-based software on the Clipchamp platform. When the supercomputer project ground to a halt, the team decided to keep going with the video programming technology, which was, in the words of Dreiling, "a tool that worked on Chromebooks". In June 2016, Clipchamp was valued at 1.1 million dollars, according to the Wall Street Journal. In the same month, the second version of Clipchamp was launched internationally. By 2018, the firm had amassed 6.5 million users, attracting investors such as Steve Baxter, who invested one million dollars. In 2020, Clipchamp set up a base in Seattle, USA, after achieving capital of 13.2 million dollars, from alliances made with investment funds such as Transition Level Investments, Tola Capital, and TEN13, among others. In February 2021, Clipchamp published on its website that it has 14 million users worldwide, registered in 250 countries and territories. At that time, the company announced that it had an audiovisual library of 800,000 files. On September 7, 2021, Microsoft announced the acquisition of Clipchamp. In a press release, they expressed their interest in learning more about the video content creation market. Johnson Winter Slattery advised Microsoft on its acquisition. Clipchamp was integrated as part of Windows 11 beginning on March 9, 2022, as part of Insider Preview Build 22572.

  Read more →
• 

  Abiquo Enterprise Edition

  Abiquo Hybrid Cloud Management Platform is a web-based cloud computing software platform developed by Abiquo. Written entirely in Java, it is used to build, integrate and manage public and private clouds in homogeneous environments. Users can deploy and manage servers, storage system and network and virtual devices. It also supports LDAP integration. == Hypervisors == Abiquo supports five hypervisor systems. VMware ESXi Microsoft Hyper-V Citrix XenServer Oracle VM Server for x86 KVM From version 3.1, it also supports multiple public cloud providers: Amazon AWS Rackspace Google Compute Engine HP Cloud ElasticHosts DigitalOcean Abiquo version 3.2 added: Microsoft Azure Abiquo version 3.4 added: Support for Docker hosts, adding multi-tenant networking, storage management and private registry management for Docker SoftLayer CloudSigma Later versions continued to add features including autoscaling on any cloud, integration to VMware NSX and OpenStack Neutron for software defined networking, guest config with cloud-init and integrated monitoring driving guest automation. == Storage services == Abiquo supports any vendor for hypervisor storage, and also supports tiered storage pools, enabling storage-as-a-service from specific vendors and technologies including: NFS Generic iSCSI NetApp Nexenta == SAAS version == In April 2014 Abiquo launched Abiquo anyCloud, a SAAS version of the Abiquo Hybrid Cloud Platform software. This version lets users manage public cloud resources from: Amazon AWS Microsoft Azure IBM SoftLayer DigitalOcean Rackspace Open Cloud (an OpenStack cloud) HP Public Cloud (an OpenStack cloud) Google Compute Engine ElasticHosts Additional security and process features include workflow, to have an enterprise administrator electronically sign off on changes, an audit trail of activity and the ability to share cloud accounts among and enterprise team in a secure way. == Reviews and awards == Finalist for the 2015 Cloud Awards Finalist for the 2015 UK Cloud Awards in the category Cloud Management Product of the Year EMA Radar for Private Cloud platforms 2013 Global Telecoms Business Innovation Summit and Awards 2013 (with Interoute) EuroCloud UK Awards

  Read more →
• 

  T-vertices

  T-vertices is a term used in computer graphics to describe a problem that can occur during mesh refinement or mesh simplification. The most common case occurs in naive implementations of continuous level of detail, where a finer-level mesh is "sewn" together with a coarser-level mesh by simply aligning the finer vertices on the edges of the coarse polygons. The result is a continuous mesh, however due to the nature of the z-buffer and certain lighting algorithms such as Gouraud shading, visual artifacts can often be detected. Some modeling algorithms such as subdivision surfaces will fail when a model contains T-vertices.

  Read more →
• 

  ObjectVision

  ObjectVision was a forms-based programming language and environment for Windows 3.x developed by Borland. The latest version, 2.1, was released in 1992. An ObjectVision application is composed by forms designed in a graphic way that contains objects and events to provide interactivity. Forms are connected together with logic in the form of decision trees. ObjectVision applications also can interact with databases using multiple engines, like Paradox and dBase. A finished project is saved as an OVD file, that is executed by an interpreted runtime that can be freely distributed. ObjectVision was not used broadly except in some niche segments, but the visual programming ideas were the basis for Borland Delphi.

  Read more →
• 

  Free boundary condition

  In image processing, the free boundary condition is the convention used when applying a convolution kernel to a digital image in which pixel locations that lie outside the image boundaries are interpreted as having a value of zero.[1] The question of what value to assign out-of-bounds pixels may arise, for instance, when applying a 3×3 kernel to the corner pixel in an image.

  Read more →
• 

  Oracle Cloud

  Oracle Cloud is a cloud computing service offered by Oracle Corporation providing servers, storage, network, applications and services through a global network of Oracle Corporation managed data centers. The company allows these services to be provisioned on demand over the Internet. Oracle Cloud provides infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and data as a service (DaaS). These services are used to build, deploy, integrate, and extend applications in the cloud. This platform supports numerous open standards (SQL, HTML5, REST, etc.), open-source applications (Kubernetes, Spark, Hadoop, Kafka, MySQL, Terraform, etc.), and a variety of programming languages, databases, tools, and frameworks including Oracle-specific, open source, and third-party software and systems. == Services == === Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) === Oracle's cloud infrastructure was made generally available (GA) on October 20, 2016 under the name "Oracle Bare Metal Cloud Services". Oracle Bare Metal Cloud Services was rebranded as Oracle Cloud Infrastructure in 2018 and dubbed Oracle's "Generation 2 Cloud" at Oracle OpenWorld 2018. Oracle Cloud Infrastructure offerings include the following services: Compute: The company provides Virtual Machine Instances to provide different shapes (VM sizes) catering to different types of workloads and performance characteristics. They also provide on-demand Bare metal servers and Bare metal GPU servers, without a hypervisor. In 2016, Oracle Cloud Infrastructure launched with bare metal instances with Intel processors. These first bare metal instances offered were powered by Intel servers. In 2018, Oracle Cloud added bare metal instances powered by AMD processors, followed by Ampere Cloud-native processors in 2021. In 2021, Oracle also released its first VM-based compute instances based on Arm processors. Storage: The platform provides block volumes, file storage, object storage, and archive storage for database, analytics, content, and other applications across common protocols and APIs. Networking: This cloud platform provides network with fully configurable IP addresses, subnets, routing, and firewalls to support new or existing private networks with end-to-end security. Governance: For auditing, identity and access management, the platform has data integrity checks, traceability, and access management features. Database Management / Data Management: Oracle offers a data management platform for database workloads as well as hyper-scale big data and streaming workloads including OLTP, data warehousing, Spark, machine learning, text search, image analytics, data catalog, and deep learning. The platform allows Oracle, MySQL, and NoSQL databases to be deployed on demand as managed cloud services. Oracle Databases uniquely offer the Oracle Autonomous Database (optimized for data warehouse, transaction processing, or JSON), the Exadata shape, as well as Real Application Clusters (RAC). Load Balancing: The cloud platform offers load balancing capability to automatically route traffic across fault domains and availability domains for high availability and fault-tolerance for hosted applications. Edge Services: These services can monitor the path between users and resources and adapt to changes and outages. They include Domain Name System (DNS) services from Oracle's acquisition of Dyn. FastConnect: The cloud platform provides private connectivity across on-premises and cloud networks through providers like Equinix, AT&T, and Colt. Application Development: For application development, the company's cloud offers an open, standards-based application development platform to build, deploy, and manage API-first, mobile-first cloud applications. This platform supports container-native, cloud-native, and low code development. This platform also provides a DevOps platform for CI/CD, diagnostics for Java applications, and integration with SaaS and on-prem applications. Services include Java, mobile, digital assistants (evolution from chatbots), messaging, application container cloud, developer cloud, visual builder, API catalog, AI platform, DataScience.com (Oracle acquired) and blockchain. Integration: This is a platform offering with adapters to integrate on-premise and cloud applications. Capabilities include data integration and replication, API management, integration analytics, along with data migration and integration. They offer services such as data integration platform cloud, data integrator cloud service, GoldenGate cloud service, integration cloud, process cloud service, API platform cloud service, apiary cloud service, and SOA cloud service. Business Analytics: The company provides this business analytics platform which can analyze and generate insights from data across various applications, data warehouses, and data lakes. The services offered include analytics cloud, business intelligence, big data discovery, big data preparation, data visualization, and essbase. Security: The Oracle Cloud Platform provides identity and security applications for providing secure access and monitoring of hybrid cloud environment and addressing IT governance and compliance requirements. This platform delivers an identity SOC (Security Operations Center) through a combined offering of SIEM, UEBA, CASB, and IDaaS. The services offered include Identity Cloud Service and CASB Cloud Service. Management: The platform provides an integrated monitoring, management, and analytics platform. This platform also uses machine learning and big data on the operational data set. The platform is used to improve IT stability, prevent application outages, improve DevOps, and harden security. Services offered include Application Performance Monitoring, Infrastructure Monitoring, Log Analytics, Orchestration, IT Analytics, Configuration and Compliance, Security Monitoring, and Analytics. Content and Experience: This is a platform for content, website, and workflow management. This service is used to provide content collaboration and web presence. This tool comes integrated with Oracle on-premise and SaaS services. The services offered are Content and Experience Cloud, WebCenter Portal Cloud, and DIVA Cloud. In 2016, Oracle acquired Dyn, an internet infrastructure company. On May 16, 2018 Oracle announced that it had acquired DataScience.com, a privately held cloud workspace platform for data science projects and workloads. In April 2020, Oracle became the cloud infrastructure provider for Zoom, an online and video meeting platform. The same month, Nissan announced its migration to Oracle Cloud for its high-performance computing (HPC) workloads used for simulating the structural impacts of a car design. Xerox announced a partnership with Oracle Cloud in 2021, where Xerox will use Oracle's cloud-computing capabilities within its business incubator. === Software as a Service (SaaS) === Oracle provides SaaS applications also known as Oracle Cloud Applications. These applications are offered across a variety of products, industrial sectors with various deployment options to adhere to compliance standards. The below list mentions Oracle Cloud Applications provided by Oracle Corporation. Customer Experience (CX) Human Capital Management (HCM) Enterprise Resource Planning (ERP) Supply Chain Management (SCM) Enterprise Performance Management (EPM) Internet of Things Applications (IoT) SaaS Analytics Data Industry Solutions (Communications, Financial Services, Consumer Goods, High Tech and Manufacturing, Higher Education, Hospitality, Utilities) Deployment (adhering to standards for sectors such as Financial Services, Retail Services, Public Sector, Defense) Block-Chain Cloud Service (in partnership with SAP, IBM and Microsoft) Blockchain Applications On July 28, 2016 Oracle bought NetSuite, the very first cloud company, for $9.3 billion. === Data as a Service (DaaS) === This platform is known as the Oracle Data Cloud. This platform aggregates and analyzes consumer data powered by Oracle ID Graph across channels and devices to create cross-channel consumer understanding. == Deployment models == Oracle Cloud is available in 44 regions as of July 2023, including North America, South America, UK, European Union, Middle East, Africa, India, Australia, Korea, and Japan. Oracle Cloud is available as a public cloud (Oracle-managed regions); to selected government agencies as an Oracle-managed government cloud in the United States (with FedRAMP High and DISA SRG IL5 compliance) and United Kingdom; and as a "private cloud" or "hybrid cloud" as an Oracle-managed database-only service or full-service dedicated region - what Oracle calls "Cloud at Customer". == Architecture == Oracle's public and government cloud is offered through a global network of Oracle-managed data centers, connected by an Oracle-managed backbone network. Oracle's Exadata Cloud at Customer leverages this network for contr

  Read more →
• 

  Cache language model

  A cache language model is a type of statistical language model. These occur in the natural language processing subfield of computer science and assign probabilities to given sequences of words by means of a probability distribution. Statistical language models are key components of speech recognition systems and of many machine translation systems: they tell such systems which possible output word sequences are probable and which are improbable. The particular characteristic of a cache language model is that it contains a cache component and assigns relatively high probabilities to words or word sequences that occur elsewhere in a given text. The primary, but by no means sole, use of cache language models is in speech recognition systems. To understand why it is a good idea for a statistical language model to contain a cache component one might consider someone who is dictating a letter about elephants to a speech recognition system. Standard (non-cache) N-gram language models will assign a very low probability to the word "elephant" because it is a very rare word in English. If the speech recognition system does not contain a cache component, the person dictating the letter may be annoyed: each time the word "elephant" is spoken another sequence of words with a higher probability according to the N-gram language model may be recognized (e.g., "tell a plan"). These erroneous sequences will have to be deleted manually and replaced in the text by "elephant" each time "elephant" is spoken. If the system has a cache language model, "elephant" will still probably be misrecognized the first time it is spoken and will have to be entered into the text manually; however, from this point on the system is aware that "elephant" is likely to occur again – the estimated probability of occurrence of "elephant" has been increased, making it more likely that if it is spoken it will be recognized correctly. Once "elephant" has occurred several times, the system is likely to recognize it correctly every time it is spoken until the letter has been completely dictated. This increase in the probability assigned to the occurrence of "elephant" is an example of a consequence of machine learning and more specifically of pattern recognition. There exist variants of the cache language model in which not only single words but also multi-word sequences that have occurred previously are assigned higher probabilities (e.g., if "San Francisco" occurred near the beginning of the text subsequent instances of it would be assigned a higher probability). The cache language model was first proposed in a paper published in 1990, after which the IBM speech-recognition group experimented with the concept. The group found that implementation of a form of cache language model yielded a 24% drop in word-error rates once the first few hundred words of a document had been dictated. A detailed survey of language modeling techniques concluded that the cache language model was one of the few new language modeling techniques that yielded improvements over the standard N-gram approach: "Our caching results show that caching is by far the most useful technique for perplexity reduction at small and medium training data sizes". The development of the cache language model has generated considerable interest among those concerned with computational linguistics in general and statistical natural language processing in particular: recently, there has been interest in applying the cache language model in the field of statistical machine translation. The success of the cache language model in improving word prediction rests on the human tendency to use words in a "bursty" fashion: when one is discussing a certain topic in a certain context, the frequency with which one uses certain words will be quite different from their frequencies when one is discussing other topics in other contexts. The traditional N-gram language models, which rely entirely on information from a very small number (four, three, or two) of words preceding the word to which a probability is to be assigned, do not adequately model this "burstiness". Recently, the cache language model concept – originally conceived for the N-gram statistical language model paradigm – has been adapted for use in the neural paradigm. For instance, recent work on continuous cache language models in the recurrent neural network (RNN) setting has applied the cache concept to much larger contexts than before, yielding significant reductions in perplexity. Another recent line of research involves incorporating a cache component in a feed-forward neural language model (FN-LM) to achieve rapid domain adaptation.

  Read more →
• 

  InciWeb

  InciWeb is an interagency all-risk incident web information management system provided by the United States Forest Service released in 2004. It was originally developed for wildland fire emergencies, but can be also used for other emergency incidents (natural disasters, such as earthquakes, floods, hurricanes, and tornadoes). == Introduction == It was developed with two primary missions: 1. Provide the public a single source of incident related information 2. Provide a standardized reporting tool for the Public Affairs community Official announcements include evacuations, road closures, news releases, maps, photographs, and basic info and current situation about the incident. Incident information can be accessed by: web browser at https://inciweb.wildfire.gov/ Twitter RSS web feed == Technical == The original application was hosted at the United States Forest Service - Wildland Fire Training and Conference Center, at McClellan Airfield, California, comprising three servers: Database server Administrative server Load balancer for the public content which routes traffic to a pool of eight servers. Web traffic averages 2 million plus hits daily during the fire season with the ability to handle 3.5 million hits. The servers were moved to the National information Technology Center (NITC), Kansas City, Missouri on July 16, 2008, along with the release of version 2.0; the current version is 2.2. == Availability issues == InciWeb was having technical difficulties due to the high volume of Internet users trying to access the site during the September–October 2006 Day Fire and the Summer 2008 California wildfires. == Participating agencies == United States Forest Service Bureau of Land Management Bureau of Indian Affairs Fish and Wildlife Service National Park Service National Oceanic & Atmospheric Administration Department of the Interior Office of Aircraft Services National Association of State Foresters United States Fire Administration These same agencies are also in the National Interagency Fire Center.

  Read more →
• 

  Tertiary review

  In software engineering, a tertiary review is a systematic review of systematic reviews. It is also referred to as a tertiary study in the software engineering literature. However, Umbrella review is the term more commonly used in medicine. Kitchenham et al. suggest that methodologically there is no difference between a systematic review and a tertiary review. However, as the software engineering community has started performing tertiary reviews new concerns unique to tertiary reviews have surfaced. These include the challenge of quality assessment of systematic reviews, search validation and the additional risk of double counting. == Examples of Tertiary reviews in software engineering literature == Test quality Machine Learning Test-driven development

  Read more →