AI Video Tools

Explore the best AI Video Tools — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

  • 30 Boxes

    30 Boxes

    30 Boxes is a minimalist calendaring IOS application created by 83 Degrees. Originating as a web application in March 2006, 30 Boxes was founded by Webshots cofounder Narendra Rocherolle. The website shut down some time in 2020, but relaunched for the IOS in February 2021. The original website was tailored towards "social media junkies". == Reception == Barry Collins of The Sunday Times appreciated the website's plain-language event adding feature, but did not appreciate that he was unable to see more than one month of events at a time. Collins was also unhappy that the website was not capable of warning him when he had two events scheduled at the same time. In a list of the best web-based calendar software for small businesses, Forbes ranked 30 Boxes second, after Google Calendar. They described 30 Boxes like “buying a new car with manual transmission and lots of extras—you don't just want to drive it, you want to fool around with it to see what it can do”.

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  • Abiquo Enterprise Edition

    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

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  • GeoNetwork opensource

    GeoNetwork opensource

    The GeoNetwork opensource (GNOS) project is a free and open source (FOSS) cataloging application for spatially referenced resources. It is a catalog of location-oriented information. == Outline == It is a standardized and decentralized spatial information management environment designed to enable access to geo-referenced databases, cartographic products and related metadata from a variety of sources, enhancing the spatial information exchange and sharing between organizations and their audience, using the capacities of the internet. Using the Z39.50 protocol it both accesses remote catalogs and makes its data available to other catalog services. As of 2007, OGC Web Catalog Service are being implemented. Maps, including those derived from satellite imagery, are effective communicational tools and play an important role in the work of decision makers (e.g., sustainable development planners and humanitarian and emergency managers) in need of quick, reliable and up-to-date user-friendly cartographic products as a basis for action and to better plan and monitor their activities; GIS experts in need of exchanging consistent and updated geographical data; and spatial analysts in need of multidisciplinary data to perform preliminary geographical analysis and make reliable forecasts. == Deployment == The software has been deployed to various organizations, the first being FAO GeoNetwork and WFP VAM-SIE-GeoNetwork, both at their headquarters in Rome, Italy. Furthermore, the WHO, CGIAR, BRGM, ESA, FGDC and the Global Change Information and Research Centre (GCIRC) of China are working on GeoNetwork opensource implementations as their spatial information management capacity. It is used for several risk information systems, in particular in the Gambia. Several related tools are packaged with GeoNetwork, including GeoServer. GeoServer stores geographical data, while GeoNetwork catalogs collections of such data.

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  • Charge-coupled device

    Charge-coupled device

    A charge-coupled device (CCD) is an integrated circuit containing an array of linked, or coupled, capacitors. Under the control of an external circuit, each capacitor can transfer its electric charge to a neighboring capacitor. CCD sensors are a major technology used in digital imaging. In a CCD image sensor, pixels are represented by p-doped metal–oxide–semiconductor (MOS) capacitors. These MOS capacitors, the basic building blocks of a CCD, are biased above the threshold for inversion when image acquisition begins, allowing the conversion of incoming photons into electron charges at the semiconductor-oxide interface; the CCD is then used to read out these charges. Although CCDs are not the only technology to allow for light detection, CCD image sensors are widely used in professional, medical, and scientific applications where high-quality image data are required. In applications with less exacting quality demands, such as consumer and professional digital cameras, active pixel sensors, also known as CMOS sensors (complementary MOS sensors), are generally used. However, the large quality advantage CCDs enjoyed early on has narrowed over time and since the late 2010s CMOS sensors are the dominant technology, having largely if not completely replaced CCD image sensors. == History == The basis for the CCD is the metal–oxide–semiconductor (MOS) structure, with MOS capacitors being the basic building blocks of a CCD, and a depleted MOS structure used as the photodetector in early CCD devices. In the late 1960s, Willard Boyle and George E. Smith at Bell Labs were researching MOS technology while working on semiconductor bubble memory. They realized that an electric charge was the analog of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. This led to the invention of the charge-coupled device by Boyle and Smith in 1969. They conceived of the design of what they termed, in their notebook, "Charge 'Bubble' Devices". The initial paper describing the concept in April 1970 listed possible uses as memory, a delay line, and an imaging device. The device could also be used as a shift register. The essence of the design was the ability to transfer charge along the surface of a semiconductor from one storage capacitor to the next. The first experimental device demonstrating the principle was a row of closely spaced metal squares on an oxidized silicon surface electrically accessed by wire bonds. It was demonstrated by Gil Amelio, Michael Francis Tompsett and George Smith in April 1970. This was the first experimental application of the CCD in image sensor technology, and used a depleted MOS structure as the photodetector. The first patent (U.S. patent 4,085,456) on the application of CCDs to imaging was assigned to Tompsett, who filed the application in 1971. The first working CCD made with integrated circuit technology was a simple 8-bit shift register, reported by Tompsett, Amelio and Smith in August 1970. This device had input and output circuits and was used to demonstrate its use as a shift register and as a crude eight pixel linear imaging device. Development of the device progressed at a rapid rate. By 1971, Bell researchers led by Michael Tompsett were able to capture images with simple linear devices. Several companies, including Fairchild Semiconductor, RCA and Texas Instruments, picked up on the invention and began development programs. Fairchild's effort, led by ex-Bell researcher Gil Amelio, was the first with commercial devices, and by 1974 had a linear 500-element device and a 2D 100 × 100 pixel device. Peter L. P. Dillon, a scientist at Kodak Research Labs, invented the first color CCD image sensor by overlaying a color filter array on this Fairchild 100 x 100 pixel Interline CCD starting in 1974. Steven Sasson, an electrical engineer working for the Kodak Apparatus Division, invented a digital still camera using this same Fairchild 100 × 100 CCD in 1975. The interline transfer (ILT) CCD device was proposed by L. Walsh and R. Dyck at Fairchild in 1973 to reduce smear and eliminate a mechanical shutter. To further reduce smear from bright light sources, the frame-interline-transfer (FIT) CCD architecture was developed by K. Horii, T. Kuroda and T. Kunii at Matsushita (now Panasonic) in 1981. The first KH-11 KENNEN reconnaissance satellite equipped with charge-coupled device array (800 × 800 pixels) technology for imaging was launched in December 1976. Under the leadership of Kazuo Iwama, Sony started a large development effort on CCDs involving a significant investment. Eventually, Sony managed to mass-produce CCDs for their camcorders. Before this happened, Iwama died in August 1982. Subsequently, a CCD chip was placed on his tombstone to acknowledge his contribution. The first mass-produced consumer CCD video camera, the CCD-G5, was released by Sony in 1983, based on a prototype developed by Yoshiaki Hagiwara in 1981. Early CCD sensors suffered from shutter lag. This was largely resolved with the invention of the pinned photodiode (PPD). It was invented by Nobukazu Teranishi, Hiromitsu Shiraki and Yasuo Ishihara at NEC in 1980. They recognized that lag can be eliminated if the signal carriers could be transferred from the photodiode to the CCD. This led to their invention of the pinned photodiode, a photodetector structure with low lag, low noise, high quantum efficiency and low dark current. It was first publicly reported by Teranishi and Ishihara with A. Kohono, E. Oda and K. Arai in 1982, with the addition of an anti-blooming structure. The new photodetector structure invented at NEC was given the name "pinned photodiode" (PPD) by B.C. Burkey at Kodak in 1984. In 1987, the PPD began to be incorporated into most CCD devices, becoming a fixture in consumer electronic video cameras and then digital still cameras. Since then, the PPD has been used in nearly all CCD sensors and then CMOS sensors. In January 2006, Boyle and Smith were awarded the National Academy of Engineering Charles Stark Draper Prize, and in 2009 they were awarded the Nobel Prize for Physics for their invention of the CCD concept. Michael Tompsett was awarded the 2010 National Medal of Technology and Innovation, for pioneering work and electronic technologies including the design and development of the first CCD imagers. He was also awarded the 2012 IEEE Edison Medal for "pioneering contributions to imaging devices including CCD Imagers, cameras and thermal imagers". == Basics of operation == In a CCD for capturing images, there is a photoactive region (an epitaxial layer of silicon), and a transmission region made out of a shift register (the CCD, properly speaking). An image is projected through a lens onto the capacitor array (the photoactive region), causing each capacitor to accumulate an electric charge proportional to the light intensity at that location. A one-dimensional array, used in line-scan cameras, captures a single slice of the image, whereas a two-dimensional array, used in video and still cameras, captures a two-dimensional picture corresponding to the scene projected onto the focal plane of the sensor. Once the array has been exposed to the image, a control circuit causes each capacitor to transfer its contents to its neighbor (operating as a shift register). The last capacitor in the array dumps its charge into a charge amplifier, which converts the charge into a voltage. By repeating this process, the controlling circuit converts the entire contents of the array in the semiconductor to a sequence of voltages. In a digital device, these voltages are then sampled, digitized, and usually stored in memory; in an analog device (such as an analog video camera), they are processed into a continuous analog signal (e.g. by feeding the output of the charge amplifier into a low-pass filter), which is then processed and fed out to other circuits for transmission, recording, or other processing. == Detailed physics of operation == === Charge generation === Before the MOS capacitors are exposed to light, they are biased into the depletion region; in n-channel CCDs, the silicon under the bias gate is slightly p-doped or intrinsic. The gate is then biased at a positive potential, above the threshold for strong inversion, which will eventually result in the creation of an n channel below the gate as in a MOSFET. However, it takes time to reach this thermal equilibrium: up to hours in high-end scientific cameras cooled at low temperature. Initially after biasing, the holes are pushed far into the substrate, and no mobile electrons are at or near the surface; the CCD thus operates in a non-equilibrium state called deep depletion. Then, when electron–hole pairs are generated in the depletion region, they are separated by the electric field, the elec

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  • Apps to analyse COVID-19 sounds

    Apps to analyse COVID-19 sounds

    Apps to analyse COVID-19 sounds are mobile software applications designed to collect respiratory sounds and aid diagnosis in response to the COVID-19 pandemic. Numerous applications are in development, with different institutions and companies taking various approaches to privacy and data collection. Current efforts are aimed at gathering data. In a later stage, it is possible that sound apps will have the capacity (and ethical approvals) to provide information back to users. In order to develop and train signal analysis approaches, large datasets are required. == History == The COVID-19 outbreak was announced as a global pandemic by the World Health Organization in March 2020 and has affected a growing number of people globally. In this context, advanced artificial intelligence techniques are being considered as tools in aiding our response to global health crisis. Other COVID-19 apps which offer solutions for user tracking have been developed. At the same time a number of approaches which tries to use respiratory sounds and artificial intelligence to understand if the disease can be diagnosed have been proposed. A few studies are available as preprints (i.e. not yet peer-reviewed) documents. == Methodologies == The potential for using speech and sound analysis by artificial intelligence to help in this scenario, by surveying which types of related or contextually significant phenomena can be automatically assessed from speech or sound has been recently overviewed. These include the automatic recognition and monitoring of breathing, dry and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain. Additionally, the potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients has also been presented. In particular, by analysing speech recordings from these patients, an audio-only-based model to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety, is constructed. This work shows promise in estimating the severity of illness. Machine learning methods have been explored to recognize and diagnose coughs from different diseases. These included a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes convolutional neural networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e. bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. A large-scale crowdsourced dataset of respiratory sounds has been collected to aid diagnosis of COVID-19: coughs and breathing sounds are sufficient to distinguish users affected by COVID-19 versus those affected by asthma or healthy controls. Behind these studies is the ambition that automated systems to screen for respiratory diseases based on voice, raw cough or other sound data would have positive medical applications in both clinical and public health arenas. == List of apps to analyse COVID-19 sounds ==

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  • BioBIKE

    BioBIKE

    BioBike(nee. BioLingua ) is a cloud-based, through-the-web programmable (Paas) symbolic biocomputing and bioinformatics platform that aims to make computational biology, and especially intelligent biocomputing (that is, the application of Artificial Intelligence to computational biology) accessible to research scientists who are not expert programmers. == Unique capabilities == BioBIKE is an integrated symbolic biocomputing and bioinformatics platform, built from the start as an entirely (what is now called) cloud-based architecture where all computing is done in remote servers, and all user access is accomplished through web browsers. BioBIKE has a built-in frame system in which all objects, data, and knowledge are represented. This enables code written either in the native Lisp, in the visual programming language, or systems of rules expressed in the SNARK theorem prover to access the whole of biological knowledge in an integrated manner. For its time (released in 2002) it was unique in permitting users to create fully functional biocomputing programs that run on the back-end servers entirely through the web browser UI. (In modern terms it was one of the first PaaS (Platform as a Service) systems, predating even Salesforce in this capability.) Initially this programming was carried out in raw Lisp, but Jeff Elhai's team at VCU, with NSF funding, created an entirely graphical programming environment on top of BioBIKE based upon the Boxer-style programming environments. Being a multi-headed, multi-threaded, multi-user, multi-tenancy cloud-based system, BioBIKE users were able to directly work together through their web browsers, remotely sharing the same listener and memory space. This permitted a unique sort of collaboration, discussed in Shrager (2007). A specialized offshoot of BioBIKE called "BioDeducta" includes SRI's SNARK theorem prover, offering unique "deductive biocomputing" capabilities. == Implementation == BioBIKE is open-source software implemented using the Lisp programming language. Continuing development takes place by the BioBIKE team centered at Virginia Commonwealth University . == History == BioBIKE was originally called "BioLingua", and was developed by Jeff Shrager at The Carnegie Inst. of Washington Dept. of Plant Biology, and JP Massar with funding from NASA's Astrobiology Division. Shrager and Massar wanted to create a web-based, multi-user Lisp Machine, specialized for bioinformatics. Other early contributors to the project included Mike Travers, and Jeff Elhai of VCU. Elhai obtained continuing funding from the National Science Foundation for the project, which was renamed BioBIKE. Elhai and colleagues added BioBIKE's unique visual programming language. Shrager, meanwhile, collaborated with Richard Waldinger at SRI to build SRI's (SNARK) theorem prover into BioBIKE, creating a deductive biocomputing system, called BioDeducta. == Instances == There used to be a number of BioBIKE verticals in different biological domains, including viral pathogens, cyanobacteria and other bacteria, Arabidopsis thaliana, and several others described in the references.

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  • SciPy

    SciPy

    SciPy (pronounced "sigh pie") is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal and image processing, ordinary differential equation solvers and other tasks common in science and engineering. SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India). Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science. == Components == The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include: cluster: hierarchical clustering, vector quantization, K-means constants: physical constants and conversion factors datasets: various example datasets for demonstrating image and data processing differentiate: numerical differentiation for first and second derivatives fft: Discrete Fourier Transform algorithms fftpack: Legacy interface for Discrete Fourier Transforms integrate: numerical integration routines interpolate: interpolation tools io: data input and output, including support for MATLAB and Matrix Market files linalg: linear algebra routines ndimage: various functions for multi-dimensional image processing odr: orthogonal distance regression classes and algorithms optimize: optimization algorithms including linear programming and a variety of numerical nonlinear programming optimizers signal: signal processing tools sparse: sparse matrices and related algorithms spatial: algorithms for spatial structures such as k-d trees, nearest neighbors, convex hulls, etc. special: special functions stats: statistical functions == Data structures == The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for linear algebra, Fourier transforms, and random number generation, but not with the generality of the equivalent functions in SciPy. NumPy can also be used as an efficient multidimensional container of data with arbitrary datatypes. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code. == History == In the 1990s, Python was extended to include an array type for numerical computing called Numeric. (This package was eventually replaced by NumPy, which was written by Travis Oliphant in 2006 as a blending of Numeric and Numarray, with Numarray itself being started in 2001.) As of 2000, there was a growing number of extension modules and increasing interest in creating a complete environment for scientific and technical computing. In 2001, Travis Oliphant, Eric Jones, and Pearu Peterson merged code they had written and called the resulting package SciPy. The newly created package provided a standard collection of common numerical operations on top of the Numeric array data structure. Shortly thereafter, Fernando Pérez released IPython, an enhanced interactive shell widely used in the technical computing community, and John Hunter released the first version of Matplotlib, the 2D plotting library for technical computing. Since then the SciPy environment has continued to grow with more packages and tools for technical computing. == Scientific Python versus ScientificPython == In the scientific literature, SciPy is occasionally referred to as "Scientific Python (SciPy)". This is incorrect: the official name of the project is just "SciPy". Furthermore, expanding "SciPy" as "Scientific Python" may cause confusion with "ScientificPython", a project led by Konrad Hinsen of Orléans University that was active between 1995 and 2014. "Scientific Python" is also used for the related ecosystem of tools.

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  • Co-occurrence matrix

    Co-occurrence matrix

    A co-occurrence matrix or co-occurrence distribution (also referred to as : gray-level co-occurrence matrices GLCMs) is a matrix that is defined over an image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset. It is used as an approach to texture analysis with various applications especially in medical image analysis. == Method == Given a grey-level image I {\displaystyle I} , co-occurrence matrix computes how often pairs of pixels with a specific value and offset occur in the image. The offset, ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} , is a position operator that can be applied to any pixel in the image (ignoring edge effects): for instance, ( 1 , 2 ) {\displaystyle (1,2)} could indicate "one down, two right". An image with p {\displaystyle p} different pixel values will produce a p × p {\displaystyle p\times p} co-occurrence matrix, for the given offset. The ( i , j ) th {\displaystyle (i,j)^{\text{th}}} value of the co-occurrence matrix gives the number of times in the image that the i th {\displaystyle i^{\text{th}}} and j th {\displaystyle j^{\text{th}}} pixel values occur in the relation given by the offset. For an image with p {\displaystyle p} different pixel values, the p × p {\displaystyle p\times p} co-occurrence matrix C is defined over an n × m {\displaystyle n\times m} image I {\displaystyle I} , parameterized by an offset ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} , as: C Δ x , Δ y ( i , j ) = ∑ x = 1 n ∑ y = 1 m { 1 , if I ( x , y ) = i and I ( x + Δ x , y + Δ y ) = j 0 , otherwise {\displaystyle C_{\Delta x,\Delta y}(i,j)=\sum _{x=1}^{n}\sum _{y=1}^{m}{\begin{cases}1,&{\text{if }}I(x,y)=i{\text{ and }}I(x+\Delta x,y+\Delta y)=j\\0,&{\text{otherwise}}\end{cases}}} where: i {\displaystyle i} and j {\displaystyle j} are the pixel values; x {\displaystyle x} and y {\displaystyle y} are the spatial positions in the image I; the offsets ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} define the spatial relation for which this matrix is calculated; and I ( x , y ) {\displaystyle I(x,y)} indicates the pixel value at pixel ( x , y ) {\displaystyle (x,y)} . The 'value' of the image originally referred to the grayscale value of the specified pixel, but could be anything, from a binary on/off value to 32-bit color and beyond. (Note that 32-bit color will yield a 232 × 232 co-occurrence matrix!) Co-occurrence matrices can also be parameterized in terms of a distance, d {\displaystyle d} , and an angle, θ {\displaystyle \theta } , instead of an offset ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} . Any matrix or pair of matrices can be used to generate a co-occurrence matrix, though their most common application has been in measuring texture in images, so the typical definition, as above, assumes that the matrix is an image. It is also possible to define the matrix across two different images. Such a matrix can then be used for color mapping. == Aliases == Co-occurrence matrices are also referred to as: GLCMs (gray-level co-occurrence matrices) GLCHs (gray-level co-occurrence histograms) spatial dependence matrices == Application to image analysis == Whether considering the intensity or grayscale values of the image or various dimensions of color, the co-occurrence matrix can measure the texture of the image. Because co-occurrence matrices are typically large and sparse, various metrics of the matrix are often taken to get a more useful set of features. Features generated using this technique are usually called Haralick features, after Robert Haralick. Texture analysis is often concerned with detecting aspects of an image that are rotationally invariant. To approximate this, the co-occurrence matrices corresponding to the same relation, but rotated at various regular angles (e.g. 0, 45, 90, and 135 degrees), are often calculated and summed. Texture measures like the co-occurrence matrix, wavelet transforms, and model fitting have found application in medical image analysis in particular. == Other applications == Co-occurrence matrices are also used for words processing in natural language processing (NLP).

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  • Blitter object

    Blitter object

    A blitter object (Bob) is a graphical element (GEL) used by the Amiga computer. Bobs are hardware sprite-like objects, movable on the screen with the help of the blitter coprocessor. == Overview == The AmigaOS GEL system consists of VSprites, Bobs, AnimComps (animation components) and AnimObs (animation objects), each extending the preceding with additional functionality. While VSprites are a virtualization of hardware sprites Bobs are drawn into a playfield by the blitter, saving and restoring the background of the GEL as required. The Bob with the highest video priority is the last one to be drawn, which makes it appear to be in front of all other Bobs. In contrast to hardware sprites Bobs are not limited in size and number. Bobs require more processing power than sprites, because they require at least one DMA memory copy operation to draw them on the screen. Sometimes three distinct memory copy operations are needed: one to save the screen area where the Bob would be drawn, one to actually draw the Bob, and one later to restore the screen background when the Bob moves away. An AnimComp adds animation to a Bob and an AnimOb groups AnimComps together and assigns them velocity and acceleration.

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  • Misskey

    Misskey

    Misskey (Japanese: ミスキー, romanized: Misukī) is an open source, federated, social networking service created in 2014 by Japanese software engineer Eiji "syuilo" Shinoda. Misskey uses the ActivityPub protocol for federation, allowing users to interact between independent Misskey instances, and other ActivityPub compatible platforms. Misskey is generally considered to be part of the Fediverse. Despite being a decentralized service, Misskey is not philosophically opposed to centralization. The name Misskey comes from the lyrics of Brain Diver, a song by the Japanese singer May'n. == History == Misskey was initially developed as a BBS-style internet forum by high school student Eiji Shinoda in 2014. After introducing a timeline feature, Misskey gained popularity as the microblogging platform it is today. In 2018, Misskey added support for ActivityPub, becoming a federated social media platform. The flagship Misskey server, Misskey.io, was started on April 15, 2019. Misskey, alongside Mastodon and Bluesky, has received attention as a potential replacement for Twitter following Twitter's acquisition by Elon Musk in 2022. On April 8, 2023, Misskey.io incorporated as MisskeyHQ K.K. As of February 2024, over 450,000 users were registered, making it the largest instance of Misskey. Misskey.io is crowdfunded. The administrator of Misskey.io is Japanese system administrator Yoshiki Eto, who operates under the alias Murakami-san. Eiji Shinoda serves as director. In July 2023, Twitter introduced extreme restrictions on their API in order to combat scraping from bots. Some users were critical of the changes, and as a result migrated to other social networks. The number of users registering on Misskey.io, Misskey's official instance and the largest one, increased rapidly, with other Misskey instances also receiving a spike in signups. In response to this trend, Skeb, a platform for sharing art, announced on July 14, 2023 that it would sponsor the Misskey development team. In early 2024, Misskey was targeted by a spam attack from Japan. The cause of the attack is believed to be a dispute between rival groups on a Japanese hacker forum and a DDoS attack on a Discord bot. Mastodon instances with open registration were used in the attack. In November 2025, Eto announced intentions to replace ActivityPub with Misskey's own low-overhead federation system in "a few years". Shinoda later said that this was "fake news". == Development == Misskey is open source software and is licensed under the AGPLv3. The Misskey API is publicly available and is documented using the OpenAPI Specification, which allows users to build automated accounts and use it on any Misskey instance. The service is translated using Crowdin. Misskey is developed using Node.js. TypeScript is used on both the frontend and backend. PostgreSQL is used as its database. Vue.js is used for the frontend. == Functionality == Posts on Misskey are called "notes". Notes are limited to a maximum of 3,000 characters (a limit which can be customized by instances), and can be accompanied by any file, including polls, images, videos, and audio. Notes can be reposted, either by themselves or with another "quote" note. Misskey comes with multiple timelines to sort through the notes that an instance has available, and are displayed in reverse chronological order. The Home timeline shows notes from users that you follow, the Local timeline shows all notes from the instance in use, the Social timeline shows both the Home and Local timeline, and the Global timeline shows every public note that the instance knows about. Notes have customizable privacy settings to control what users can see a note, similar to Mastodon's post visibility ranges. Public notes show up on all timelines, while Home notes only show on a user's Home timeline. Notes can also be set to be available only for followers. Direct messages using notes can be sent to users.

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  • CPanel

    CPanel

    cPanel is a web hosting control panel software developed by cPanel, L.L.C. It provides a graphical interface (GUI) and automation tools designed to simplify the process of hosting a web site for the website owner or "end user". It enables administration through a standard web browser using a three-tier structure. While cPanel is limited to managing a single hosting account, cPanel & WHM allow the administration of the entire server. In addition to the GUI, cPanel also has command line and API-based access that allows third-party software vendors, web hosting organizations, and developers to automate standard system administration processes. cPanel & WHM is designed to function either as a dedicated server or virtual private server. The latest cPanel & WHM version supports installation on AlmaLinux, Rocky Linux, CloudLinux OS, and Ubuntu. == History == cPanel is currently developed by cPanel, L.L.C., a privately owned company headquartered in Houston, Texas, United States. WebPros is the parent company of cPanel, L.L.C. It was originally designed in 1996 as the control panel for Speed Hosting, a now-defunct web hosting company. The original author of cPanel, J. Nick Koston, had a stake in Speed Hosting. Webking quickly began using cPanel after its merger with Speed Hosting. The new company moved its servers to Virtual Development Inc. (VDI), a now-defunct hosting facility. Following an agreement between Koston and VDI, cPanel was only available to customers hosted directly at VDI. At the time, there was little competition in the control panel market, with the main choices being VDI and Alabanza. Eventually, due to Koston leaving for college, he and William Jensen signed an agreement in which cPanel was split into a separate program called WebPanel; this version was run by VDI. Without the lead programmer, VDI was not able to continue any work on cPanel and eventually stopped supporting it completely. Koston kept working on cPanel while also working at BurstNET. Eventually, he left BurstNET to focus fully on cPanel. cPanel 3 was released in 1999: main additions over cPanel 2 were an automatic upgrade and the Web Host Manager (WHM). The interface was also improved when Carlos Rego of WizardsHosting made what became the default theme of cPanel. With the release of cPanel 11, cPanel adopted a four-tier versioning system, "Parent.Major.Minor.Patch" (e.g., 11.32.0.3). As of version 11.52, the "Parent" representation is deprecated, with 11.54 stylized as "Version 54." cPanel 11.30 is the last major version to support FreeBSD. On August 20, 2018 cPanel L.L.C. announced that it had signed an agreement to be acquired by a group led by Oakley Capital (who also own Plesk and SolusVM). While Koston sold his interest in cPanel, he will continue to be an owner of the company that owns cPanel. In April 2026, a severe vulnerability was discovered that affected all cPanel and WHM versions after 11.40, affectively allowing unauthenticated remote attackers to access the control panel. According to some web hosters the vulnerability was already being actively exploited, with some attempts even dating back to late February 2026. == Add-ons == cPanel provides front-ends for a number of common operations, including the management of PGP keys, crontab tasks, mail and FTP accounts, and mailing lists. Several add-ons exist, some for an additional fee, including auto installers such as Installatron, Fantastico, Softaculous, and WHMSonic (SHOUTcast/radio Control Panel Add-on). The add-ons need to be enabled by the server administrator in WHM to be accessible to the cPanel user. WHM manages some software packages separately from the underlying operating system, applying upgrades to Apache, PHP, MySQL and MariaDB, Exim, FTP, and related software packages automatically. This ensures that these packages are kept up-to-date and compatible with WHM, but makes it more difficult to install newer versions of these packages. It also makes it difficult to verify that the packages have not been tampered with, since the operating system's package management verification system cannot be used to do so. == WHM == WHM, short for WebHost Manager, is a web-based tool which is used for server administration. There are at least two tiers of WHM, often referred to as "root WHM", and non-root WHM (or Reseller WHM). Root WHM is used by server administrators and non-root WHM (with fewer privileges) is used by others, like entity departments, and resellers to manage hosting accounts often referred to as cPanel accounts on a web server. WHM is also used to manage SSL certificates (both server self generated and CA provided SSL certificates), cPanel users, hosting packages, DNS zones, themes, and authentication methods. The default automatic SSL (AutoSSL) provided by cPanel is powered by Let's Encrypt. Additionally, WHM can also be used to manage FTP, Mail (POP, IMAP, and SMTP) and SSH services on the server. As well as being accessible by the root administrator, WHM is also accessible to users with reseller privileges. Reseller users of cPanel have a smaller set of features than the root user, generally limited by the server administrator, to features which they determine will affect their customers' accounts rather than the server as a whole. From root WHM, the server administrator can perform maintenance operations such as upgrading and recompiling Apache and PHP, installing Perl modules, and upgrading RPMs installed on the system. == Enkompass == A version of cPanel & WHM for Microsoft Windows, called Enkompass, was declared end-of-life as of February 2014. Version 3 remained available for download, but without further development or support. In the preceding years, Enkompass had been available for free as product development slowed. == Pricing == On June 27, 2019 cPanel announced a new account-based pricing structure. After backlash from their customers, cPanel issued a second announcement but did not change the new structure.

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  • List of Java software and tools

    List of Java software and tools

    This is a list of software and programming tools for the Java programming language, which includes frameworks, libraries, IDEs, build tools, application servers, and related projects. == Java frameworks == == Libraries == Apache Ant – build automation tool Apache Batik – SVG processing Apache Cayenne – object-relational mapping Apache Xerces – collection of software libraries for parsing, validating, serializing and manipulating XML. Applet – applet API Ardor3D – 3D graphics engine Bonita BPM – workflow engine Cassowary – constraint solving Checkstyle – static code analysis GNU Classpath – standard library implementation Colt – scientific computing and technical computing Commons Daemon – manages applications as daemons DESMO-J – discrete event simulation Diagrams.net – diagramming Disruptor – high-performance messaging Dom4j – XML processing Dynamic Languages Toolkit – support for dynamic programming languages on the JVM Echo – GUI Flying Saucer – XHTML/CSS rendering Formatting Objects Processor – XSL-FO to PDF H2 Database Engine – relational database IAIK-JCE – cryptography Internet Foundation Classes – legacy GUI JavaBeans – reusable component architecture for enabling encapsulation, events, and properties for software components JavaCC – open-source parser generator and lexical analyzer Java Class Library – standard library of Java and other JVM languages Java Native Access – provides Java programs easy access to native shared libraries without using the Java Native Interface Javolution – real-time computing Jblas – linear algebra JDBCFacade – simplifies JDBC use JExcel – Excel API JFugue – music programming JMusic – music programming Joget Workflow – workflow engine JOOQ Object Oriented Querying – fluent API for SQL JPOS – financial messaging JUNG – open-source graph modeling and visualization LanguageWare – language processing LibGDX – game development Modular Audio Recognition Framework – collection of voice, sound, speech, text and natural language processing algorithms. ASM – bytecode manipulation Open Inventor – 3D graphics OpenPDF – PDF Parallel Colt – parallel computing Parboiled – parser PlayN – game development QOCA – constraint solving QtJambi – Qt bindings SLF4J – logging StableUpdate – update management SWT – GUI SuanShu – numerical computing SwingLabs – GUI extensions UBY – natural language processing Undecimber – calendar XDoclet – attribute-oriented programming XINS – XML network services XStream – object serialization == Machine learning and AI == Apache Mahout – scalable machine learning library focused on clustering, classification, and collaborative filtering Apache MXNet – deep learning framework with Java API support Apache OpenNLP – machine learning based toolkit for natural language processing of text Deeplearning4j – distributed deep learning library Deep Java Library – open-source deep learning framework developed by Amazon Web Services Encog – framework for neural networks, genetic algorithms, Hidden Markov model, and Bayesian networks. LIBSVM – Support Vector Machine implementation Mallet – machine learning toolkit for classification, clustering, and topic modeling. MLlib – distributed machine-learning framework on top of Apache Spark Core Neuroph – lightweight neural network framework Weka – collection of machine learning algorithms for data mining Yooreeka – machine learning == Data mining == Java Data Mining (JDM) – standard Java API for data mining Massive Online Analysis (MOA) – data stream mining with concept drift == Math and scientific libraries == Apache Commons Math – general-purpose mathematics library including statistics, linear algebra, and optimization. Colt – high-performance scientific computing, including linear algebra and random numbers. Efficient Java Matrix Library (EJML) – dense and sparse matrix computations and linear algebra Easy Java Simulations – Open Source Physics project designed to create discrete computer simulations Exp4j – evaluates mathematical expressions at runtime GroovyLab – numerical computational environment Hipparchus – fork of Apache Commons Math with updated algorithms for statistics, linear algebra, and optimization. JAMA – numerical linear algebra library Jblas: Linear Algebra for Java (Jblas) – linear algebra library using native BLAS/LAPACK bindings Java Astrodynamics Toolkit – numerical library of software components for use in spaceflight applications for Java or MATLAB Matrix Toolkit Java (MTJ) – linear algebra library with BLAS and LAPACK support OjAlgo – optimization, linear algebra, and financial calculations. OptimJ – extension for mathematical optimization and constraint programming Parallel Colt – A parallel extension of Colt SuanShu – numerical analysis, linear algebra, statistics, and optimization. == Integrated development environments == See also: Java IDEs on Wikibooks Android Studio – IDE for Google's Android operating system BlueJ – educational IDE for teaching Java DrJava – lightweight Java IDE for beginners Eclipse IDE – open-source IDE with extensive plugin ecosystem Greenfoot – educational IDE IntelliJ IDEA – commercial and community editions from JetBrains JDeveloper – freeware IDE supplied by Oracle Corporation jGRASP – software visualizations MyEclipse – Java EE IDE NetBeans IDE – Apache NetBeans Visual Studio Code – general-purpose editor with Java extensions === Online IDEs === Eclipse Che GitHub Codespaces JDoodle Replit == Text editors with Java support == == Build tools and package managers == Apache Ant – automating software build Apache Ivy – subproject of Apache Ant Apache Maven – build automation and dependency management Boot – build automation for Clojure CMake – build tool with limited support for java Gradle – modern build automation tool Go continuous delivery (GoCD) – continuous delivery and build automation server Jenkins – automation server continuous delivery JitPack – package repository for Git projects Leiningen – build automation for Clojure Simple build tool (sbt) – open-source build tool Spring Roo – rapid application development of Java-based enterprise software WaveMaker – low-code development platform == Java runtimes, compilers and virtual machines == Android Runtime – runtime environment javac – Java programming language compiler Java Virtual Machine (JVM) – virtual machine that executes Java bytecode JD Decompiler JEB decompiler – disassembler and decompiler software for Android applications GraalVM – Just-in-time compilation HotSpot – JVM implementation included in OpenJDK == JVM languages and dialects == Clojure – Lisp dialect Groovy JRuby – Ruby implementation Jython – Python implementation Kotlin – popular for Android app development Renjin – R implementation Scala == Application servers and containers == Apache Geronimo – open source application server Apache MINA – event-driven asynchronous network application framework Apache Tomcat – web container and web server Apache TomEE – Apache Tomcat with Java EE features Borland Enterprise Server – discontinued application server by Borland ColdFusion – commercial application server by Adobe Systems GlassFish – application server for Jakarta EE IBM WebSphere Application Server – enterprise application server by IBM IBM WebSphere Application Server Community Edition – open source edition of WebSphere (discontinued) JBoss Enterprise Application Platform – Red Hat's supported distribution of JBoss/WildFly JEUS – commercial Java EE application server from TmaxSoft Jetty – HTTP server and web container Lucee (formerly Railo) – open source CFML application server Netty – non-blocking I/O client–server framework for network applications Oracle Containers for J2EE – discontinued application server by Oracle Oracle WebLogic Server – enterprise application server by Oracle Orion Application Server – early commercial Java EE server by IronFlare Payara Server – fork of GlassFish for production use Resin – Java application server by Caucho (open source and professional editions) SAP NetWeaver Application Server – enterprise application server by SAP WildFly – application server == Debugging and profiling tools == jdb – Java debugger bundled with the JDK JConsole – JMX-compliant monitoring tool JDK Flight Recorder – method profiling, allocation profiling, and garbage collection related events. JProfiler – commercial Java profiler VisualVM – visual tool integrating commandline JDK tools for profiling and monitoring == Testing and quality assurance == Apache JMeter – load testing tool JaCoCo – Java code coverage library JArchitect – analyzes code quality, architecture, and dependencies. Jtest – software testing and static analysis JUnit – unit testing framework Mockito – open-source testing framework for Java PMD – static program analysis source code analyzer Selenium – browser automation for web app testing Spock – test framework SpotBugs (formerly FindBugs) – static analysis tool TestNG – testing framework inspired by JUnit and NUnit == Other == Apache XMLBeans –

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  • Texture atlas

    Texture atlas

    In computer graphics, a texture atlas (also called a spritesheet or an image sprite in 2D game development) is an image containing multiple smaller images, usually packed together to reduce overall dimensions. An atlas can consist of uniformly-sized images or images of varying dimensions. A sub-image is drawn using custom texture coordinates to pick it out of the atlas. == Benefits == In an application where many small textures are used frequently, it is often more efficient to store the textures in a texture atlas which is treated as a single unit by the graphics hardware. This reduces both the disk I/O overhead and the overhead of a context switch by increasing memory locality. Careful alignment may be needed to avoid bleeding between sub textures when used with mipmapping and texture compression. In web development, images are packed into a sprite sheet to reduce the number of image resources that need to be fetched in order to display a page. == Gallery ==

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  • Elastic cloud storage

    Elastic cloud storage

    An elastic cloud is a cloud computing offering that provides variable service levels based on changing needs. Elasticity is an attribute that can be applied to most cloud services. It states that the capacity and performance of any given cloud service can expand or contract according to a customer's requirements and that this can potentially be changed automatically as a consequence of some software-driven event or, at worst, can be reconfigured quickly by the customer's infrastructure management team. Elasticity has been described as one of the five main principles of cloud computing by Rosenburg and Mateos in The Cloud at Your Service - Manning 2011. == History == Cloud computing was first described by Gillet and Kapor in 1996; however, the first practical implementation was a consequence of a strategy to leverage Amazon's excess data center capacity. Amazon and other pioneers of the commercial use of this technology were primarily interested in providing a “public” cloud service, whereby they could offer customers the benefits of using the cloud, particularly the utility-based pricing model benefit. Other suppliers followed suit with a range of cloud-based models all offering elasticity as a core component, but these suppliers were only offering this service as an element of their public cloud service. Due to perceived weaknesses in security, or at least a lack of proven compliance, many organizations, particularly in the financial and public sectors, have been slow adopters of cloud technologies. These wary organizations can achieve some of the benefits of cloud computing by adopting private cloud technologies. An alternative form of the elastic cloud has been offered by vendors such as EMC and IBM, whereby the service is based around an enterprise's own infrastructure but still retains elements of elasticity and the potential to bill by consumption. == Description == Elasticity in cloud computing is the ability for the organization to adjust its storage requirements in terms of capacity and processing with respect to operational requirements. This has the following benefits: Operational Benefits - Services can be acquired quickly, meaning that the evolving requirements of the business can be addressed almost immediately, giving an organization a potential agility advantage. A properly implemented elastic system will provision/de-provision according to application demands, so if a particular business has activity spikes then the provision can be enabled to match the demand and the capacity can be re-allocated. Research and Development (R&D) Projects - R&D activities are no longer hindered by a requirement to secure a capex budget prior to a project starting. Capability can simply be provisioned from the cloud and released at the end of the exercise. Testing and Deployment - With most large-scale projects a size test needs to be performed prior to final rollout. By taking advantage of the elasticity of the cloud and creating a full-scale avatar of the proposed production system, realistic data and traffic volumes can be provisioned and released as needed. Expensive Resources Allocated - This will normally apply only in the context where a customer is applying at least some of their own servers as part of a cloud infrastructure, specifically where a business (for performance reasons) has decided to invest in solid-state storage as opposed to spinning platters. There are instances when, due to activity spikes, a less critical process may need to be moved from the high-performance resources to more traditional storage. Server Specification - When a customer has elected to own/lease hardware, they can select and specify servers that are specifically tuned to meet the likely needs of their operation (i.e., directly controlling the cost/benefit equation). Utility Based Payments - There is, of course, a key cost driver in this process, and the notion that you should pay for what you consume is acceptable for many organizations. When hardware capacity is sourced internally, organizations need to over-provision. This applies just as much to traditional outsourcing as it does to capex-related expenditure on in-house servers. Cloud Platform – At the heart of any cloud storage system is the ability to manage hyperscale object storage and a Hadoop Distributed Files System (HDFS). Elastic storage capability is particularly well suited to hyperscale and Hadoop environments, where its capability to rapidly respond to changing circumstances and priorities is essential

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  • TSheets

    TSheets

    TSheets was a web-based and mobile time tracking and employee scheduling app. The service was accessed via a web browser or a mobile app. TSheets was an alternative to a paper timesheet or punch cards. == History == Based in Eagle, Idaho, TSheets was co-founded in 2006 by CEO Matt Rissell and CTO Brandon Zehm. In 2008, TSheets released a native employee time tracking app for the iPhone. In 2012, TSheets released an integration with accounting and payroll software QuickBooks. In 2015, TSheets accepted $15 million in growth equity funding from Summit Partners, bought a building in Eagle, Idaho, and opened a second location in Sydney, Australia. On 5 December 2017, Intuit announced an agreement to acquire TSheets. The transaction was valued at approximately $340 million of cash and other consideration and closed on 11 January 2018. After the transaction closed, Time Capture became a new business unit within Intuit's Small Business and Self-Employed Group with Matt Rissell assuming the leader role reporting to Alex Chriss. TSheets's Eagle, Idaho site became an Intuit location.

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