AI Art Prints

AI Art Prints — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

SAP StreamWork

SAP StreamWork is an enterprise collaboration tool from SAP SE released in March 2010, and discontinued in December 2015. StreamWork allowed real-time collaboration like Google Wave, but focused on business activities such as analyzing data, planning meetings, and making decisions. It incorporated technology from Box.net and Evernote to allow users to connect to online files and documents, and document-reader technology from Scribd allowed users to view documents directly within its environment. StreamWork supported the OpenSocial set of application programming interfaces (APIs), allowing it to connect to tools built by third-party developers, such as Google Docs. A version of StreamWork intended for large enterprises used a virtual appliance based on Novell's SUSE Linux Enterprise to connect it to business systems, including those from SAP.
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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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FreshBooks

FreshBooks is accounting software operated by 2ndSite Inc. primarily for small and medium-sized businesses. It is a web-based software as a service (SaaS) model, that can be accessed through a desktop or mobile device. The company was founded in 2003 and is based in Toronto, Canada. == History == FreshBooks was founded in 2004 by Mike McDerment, Levi Cooperman, and Joe Sawada in Toronto, Ontario. McDerment incorporated a second company, BillSpring in January 2015 to work on new product development. It was rolled back into FreshBooks as an updated interface in 2016. Initially FreshBooks functioned like an electronic invoicing program targeting IT professionals. After the release of the new interface, the initial release of FreshBooks was referred to as "FreshBooks Classic." FreshBooks Classic was discontinued in 2022 after migrating users to the new platform. FreshBooks Classic's front-end application was built in PHP, and the backend services were built in Python while the new FreshBooks uses the same backend services with a JavaScript single-page application. == Product == FreshBooks is a subscription-based accounting software platform that provides features such as invoicing, accounts payable, expense and time tracking, retainers, fixed asset depreciation, purchase orders, payroll integrations, mileage tracking, double-entry accounting, and standard business reporting. Financial data is stored in the cloud on a unified ledger, enabling access from desktop and mobile devices. The platform includes a free API for integration with external applications and supports multiple tax rates and currencies. It also offers project management and payroll functionalities. Pricing is based on a recurring monthly fee. FreshBooks supports country-specific tax calculations, including GST and HST in Canada, sales taxes in the United States, and MTD compliance in the UK. == Operations == FreshBooks has its headquarters in Toronto, Canada with operations in North America, Europe and Australia. Founder Mike McDerment was the chief executive officer of the company from 2003 until 2021, when he stepped down and was replaced by Don Epperson, but stayed as the executive chair. Don Epperson had previously joined FreshBooks as executive director in 2019. == Funding == FreshBooks was initially self-funded. In 2014, the company raised a Series A venture investment of $30 million led by the venture capital firm Oak Investment Partners, with participation by Georgian Partners and Atlas Venture. In 2017, FreshBooks announced that it raised another $43 million in funding from Accomplice, Georgian Partners and Oak Investment Partners. On August 10, 2021, FreshBooks announced that it had secured $80.75 million in Series E funding and $50 million in debt financing. FreshBooks also reached a valuation of more than $1 billion.
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FedRAMP

The Federal Risk and Authorization Management Program (FedRAMP) is a United States federal government-wide compliance program that provides a standardized approach to security assessment, authorization, and continuous monitoring for cloud products and services. The US government describes FedRAMP as FISMA for the cloud. == Overview == The FedRAMP PMO mission is to promote the adoption of secure cloud services across the federal government by providing a standardized approach to security and risk assessment. Per the OMB memorandum, any cloud services that hold federal data must be FedRAMP authorized. FedRAMP prescribes the security requirements and processes that cloud service providers must follow in order for the government to use their service. There are two ways to authorize a cloud service through FedRAMP: a Joint Authorization Board (JAB) provisional authorization (P-ATO), and through individual agencies. FedRAMP provides accreditation for cloud services for the various cloud offering models which are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service, (SaaS). == History == In 2011, the Office of Management and Budget (OMB) released a memorandum establishing FedRAMP "to provide a cost-effective, risk-based approach for the adoption and use of cloud services to Executive departments and agencies." The General Services Administration (GSA) established the FedRAMP Program Management Office (PMO) in June 2012. Before the introduction of FedRAMP, individual federal agencies managed their own assessment methodologies following guidance set by the Federal Information Security Management Act of 2002. == Governance and applicable laws == FedRAMP is governed by different Executive Branch entities that collaborate to develop, manage, and operate the program. These entities include: The Office of Management and Budget (OMB): The governing body that issued the FedRAMP policy memo, which defines the key requirements and capabilities of the program The Joint Authorization Board (JAB): The primary governance and decision-making body for FedRAMP comprises the chief information officers (CIOs) from the Department of Homeland Security (DHS), General Services Administration (GSA), and Department of Defense (DOD) The National Institute of Standards and Technology (NIST): Advises FedRAMP on FISMA compliance requirements and assists in developing the standards for the accreditation of independent 3PAOs The Department of Homeland Security (DHS): Manages the FedRAMP continuous monitoring strategy including data feed criteria, reporting structure, threat notification coordination, and incident response The Federal Chief Information Officers (CIO) Council: Disseminates FedRAMP information to Federal CIOs and other representatives through cross-agency communications and events The FedRAMP PMO: Established within GSA and responsible for the development of the FedRAMP program, including the management of day-to-day operations There are several laws, mandates, and policies that are foundational to FedRAMP. FISMA–the Federal Information Security Modernization Act–requires that agencies authorize the information systems that they use. The US government describes FedRAMP as FISMA for the cloud. The FedRAMP Policy Memo requires federal agencies to use FedRAMP when assessing, authorizing, and continuously monitoring cloud services in order to aid agencies in the authorization process as well as save government resources and eliminate duplicative efforts. FedRAMP's security baselines are derived from NIST SP 800-53 (as revised) with a set of control enhancements that pertain to the unique security requirements of cloud computing. == Third-party assessment organizations == Third-party assessment organizations (3PAOs) play a critical role in the FedRAMP security assessment process, as they are the independent assessment organizations that verify cloud providers' security implementations and provide the overall risk posture of a cloud environment for a security authorization decision. Accredited by the American Association for Laboratory Accreditation (A2LA), these assessment organizations must demonstrate independence and the technical competence required to test security implementations and collect representative evidence. == FedRAMP Marketplace == The FedRAMP Marketplace provides a searchable, sortable database of Cloud Service Offerings (CSOs) that have achieved a FedRAMP designation. 3PAOs, accredited auditors that can perform the FedRAMP assessment, are listed within the Marketplace. The FedRAMP Marketplace is maintained by the FedRAMP Program Management Office (PMO). == Security and authorization concerns == A 2026 ProPublica investigation found that FedRAMP entered into a partnership with Microsoft despite considerable concerns about the security of its cloud technology.
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Text Database and Dictionary of Classic Mayan

The project Text Database and Dictionary of Classic Mayan (abbr. TWKM) promotes research on the writing and language of pre-Hispanic Maya culture. It is housed in the Faculty of Arts at the University of Bonn and was established with funding from the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts. The project has a projected run-time of fifteen years and is directed by Nikolai Grube from the Department of Anthropology of the Americas at the University of Bonn. The goal of the project is to conduct computer-based studies of all extant Maya hieroglyphic texts from an epigraphic and cultural-historical standpoint, and to produce and publish a database and a comprehensive dictionary of the Classic Mayan language. == Subject of the Project == The text database, as well as the dictionary that will be compiled by the conclusion of the project, will be assembled based on all known texts from the pre-Hispanic Maya culture. These texts were produced and used between approximately the third century B.C. through A.D. 1500, in a region that today includes parts of the countries of Mexico, Guatemala, Belize, and Honduras. The thousands of hieroglyphic inscriptions on monuments, ceramics, or daily objects that have survived into the present offer insight into the language's vocabulary and structure. The project's database and dictionary will digitally represent original spellings using the logo-syllabic Maya hieroglyphs, as well as their transcription and transliteration in the Roman alphabet. The data will be additionally annotated with various epigraphic analyses, translations, and further object-specific information. == Project Partners == TWKM will employ digital technologies in order to compile and make available the data and metadata, as well as to publish the project's research results. The project thereby methodologically positions itself in the field of the digital humanities. The project will be conducted in cooperation with the project partners (below), the research association for the eHumanities TextGrid, as well as the University and Regional Library of Bonn (ULB). The working environment that is currently under construction, in which the data and metadata will be compiled and annotated, will be realized in theTextGrid Laboratory, a software of the virtual research environment. A further component of this software, the TextGrid Repository, will make the data that are authorized for publication freely available online and ensure their long-term storage. The tools for data compilation and annotation attained from the modularly constructed and extended TextGrid lab thereby provide all the necessary materials for facilitating the research team's the typical epigraphic workflow. The workflow usually begins by documenting the texts and the objects on which they are preserved, and by compiling descriptive data. It then continues with the various levels of epigraphic and linguistic analysis, and concludes in the best case scenario with a translation of the analyzed inscription and a corresponding publication. In cooperation with the ULB, selected data will additionally be made available. The project's Virtual Inscription Archive will present online, in the Digital Collections of the ULB, hieroglyphic inscriptions selected from the published data in the repository, including an image of and brief information about the texts and the objects on which they are written, epigraphic analysis, and translation. == Project Goal == One of the project's goals is to produce a dictionary of Classic Mayan, in both digital and print form, towards the end of the project run-time. Additionally, a database with a corpus of inscriptions, including their translations and epigraphic analyses, will be made freely available online. The database furthermore will provide an ontology-like link of the contextual object data with the inscriptions and with each other, thereby allowing a cultural-historical arrangement of all contents within the periods of pre-Hispanic Maya culture. The contents of the database are additionally linked to citations of relevant literature. As a result, the database will also make freely available to both the scientific community and other interested parties a bibliography representing the research history and a base of knowledge concerning ancient Maya culture and script. In addition, the Classic Maya script, in its temporally defined stages of language development, will be gathered into and documented in a comprehensive language corpus with the aid of the information gathered by the project. In collaboration with all project participants, the corpus data can be used, together with the aid of various comparable analyses and also computational linguistic methods, such as inference-based methods, to confirm readings of some hieroglyphs that are currently only partially confirmed, and to eventually completely decipher the Classic Maya script.
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Data cube

In computer programming, a data cube (or datacube) is a multi-dimensional array of values. Typically, the term "data cube" is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data. Even though it is called a cube, a data cube generally is a multi-dimensional concept which can be 1-dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. The data cube is used to represent data (sometimes called facts) along some dimensions of interest. In satellite image timeseries, dimensions would be latitude and longitude coordinates and time; a fact (sometimes called measure) would be a pixel at a given space and time as taken by the satellite. For example, in online analytical processing, an OLAP cube about a company would have dimensions that could be the company subsidiaries, the company products, and time; in this setup, a fact would be a sales event where a particular product has been sold in a particular subsidiary at a particular time. In any case, every dimension divides data into groups of cells whereas each cell in the cube represents a single measure of interest. Sometimes cubes hold only a few values with the rest being empty, i.e. undefined, while sometimes most or all cube coordinates hold a cell value. In the first case such data are called sparse, and in the second case they are called dense, although there is no hard delineation between the two. Data cubes may be stored in database management systems (DBMS) as part of array DBMS. Spatio-temporal databases and geospatial databases may also be represented as coverage data. == History == Multi-dimensional arrays have long been familiar in programming languages. Fortran offers arbitrarily-indexed 1-D arrays and arrays of arrays, which allows the construction of higher-dimensional arrays, up to 15 dimensions. APL supports n-D arrays with a rich set of operations. All these have in common that arrays must fit into the main memory and are available only while the particular program maintaining them (such as image processing software) is running. A series of data exchange formats support storage and transmission of data cube-like data, often tailored towards particular application domains. Examples include MDX for statistical (in particular, business) data, Zarr and Hierarchical Data Format for general scientific data, and TIFF for imagery. In 1992, Peter Baumann introduced management of massive data cubes with high-level user functionality combined with an efficient software architecture. Datacube operations include subset extraction, processing, fusion, and in general queries in the spirit of data manipulation languages like SQL. Some years after, the data cube concept was applied to describe time-varying business data as data cubes by Jim Gray, et al., and by Venky Harinarayan, Anand Rajaraman and Jeff Ullman. Around that time, a working group on Multi-Dimensional Databases ("Arbeitskreis Multi-Dimensionale Datenbanken") was established at German Gesellschaft für Informatik. Datacube Inc. was an image processing company selling hardware and software applications for the PC market in 1996, however without addressing data cubes as such. The EarthServer initiative has established geo data cube service requirements. == Standardization == In 2018, the ISO SQL database language was extended with data cube functionality as "SQL – Part 15: Multi-dimensional arrays (SQL/MDA)". Web Coverage Processing Service is a geo data cube analytics language issued by the Open Geospatial Consortium in 2008. In addition to the common data cube operations, the language knows about the semantics of space and time and supports both regular and irregular grid data cubes, based on the concept of coverage data. An industry standard for querying business data cubes, originally developed by Microsoft, is MultiDimensional eXpressions. == Implementation == Many high-level computer languages treat data cubes and other large arrays as single entities distinct from their contents. These languages, of which Fortran, APL, IDL, NumPy, PDL, and S-Lang are examples, allow the programmer to manipulate complete film clips and other data en masse with simple expressions derived from linear algebra and vector mathematics. Some languages (such as PDL) distinguish between a list of images and a data cube, while many (such as IDL) do not. Array DBMSs (Database Management Systems) offer a data model which generically supports definition, management, retrieval, and manipulation of n-dimensional data cubes. This database category has been pioneered by the rasdaman system since 1994. == Applications == Multi-dimensional arrays can meaningfully represent spatio-temporal sensor, image, and simulation data, but also statistics data where the semantics of dimensions is not necessarily of spatial or temporal nature. Generally, any kind of axis can be combined with any other into a data cube. === Mathematics === In mathematics, a one-dimensional array corresponds to a vector, a two-dimensional array resembles a matrix; more generally, a tensor may be represented as an n-dimensional data cube. === Science and engineering === For a time sequence of color images, the array is generally four-dimensional, with the dimensions representing image X and Y coordinates, time, and RGB (or other color space) color plane. For example, the EarthServer initiative unites data centers from different continents offering 3-D x/y/t satellite image timeseries and 4-D x/y/z/t weather data for retrieval and server-side processing through the Open Geospatial Consortium WCPS geo data cube query language standard. A data cube is also used in the field of imaging spectroscopy, since a spectrally-resolved image is represented as a three-dimensional volume. Earth observation data cubes combine satellite imagery such as Landsat 8 and Sentinel-2 with Geographic information system analytics. === Business intelligence === In online analytical processing (OLAP), data cubes are a common arrangement of business data suitable for analysis from different perspectives through operations like slicing, dicing, pivoting, and aggregation.
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C3D Toolkit

C3D Toolkit is a proprietary cross-platform geometric modeling kit software developed by Russian C3D Labs (previously part of ASCON Group). It's written in C++ . It can be licensed by other companies for use in their 3D computer graphics software products. The most widely known software in which C3D Toolkit is typically used are computer aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) systems. C3D Toolkit provides routines for 3D modeling, 3D constraint solving, polygonal mesh-to-B-rep conversion, 3D visualization, and 3D file conversions etc. == History == Nikolai Golovanov is a graduate of the Mechanical Engineering department of Bauman Moscow State Technical University as a designer of space launch vehicles. Upon his graduation, he began with the Kolomna Engineering Design bureau, which at the time employed the future founders of ASCON, Alexander Golikov and Tatiana Yankina. While at the bureau, Dr Golovanov developed software for analyzing the strength and stability of shell structures. In 1989, Alexander Golikov and Tatiana Yankina left Kolomna to start up ASCON as a private company. Although they began with just an electronic drawing board, even then they were already conceiving the idea of three-dimensional parametric modeling. This radical concept eventually changed flat drawings into three-dimensional models. The ASCON founders shared their ideas with Nikolai Golovanov, and in 1996 he moved to take up his current position with ASCON. As of 2012 he was involved in developing algorithms for C3D Toolkit. In 2012 the earliest version of the C3D Modeller kernel was extracted from KOMPAS-3D CAD. It was later adopted to a range of different platforms and advertised as a separate product. == Overview == It incorporates five modules: C3D Modeler constructs geometric models, generates flat projections of models, performs triangulations, calculates the inertial characteristics of models, and determines whether collisions occur between the elements of models; C3D Modeler for ODA enables advanced 3D modeling operations through the ODA's standard "OdDb3DSolid" API from the Open Design Alliance; C3D Solver makes connections between the elements of geometric models, and considers the geometric constraints of models being edited; C3D B-Shaper converts polygonal models to boundary representation (B-rep) bodies; C3D Vision controls the quality of rendering for 3D models using mathematical apparatus and software, and the workstation hardware; C3D Converter reads and writes geometric models in a variety of standard exchange formats. == Features == == Development == == Applications == Since 2013 - the date the company started issuing a license for the toolkit -, several companies have adopted C3D software components for their products, users include: Recently, C3D Modeler has been adapted to ODA Platform. In April 2017, C3D Viewer was launched for end users. The application allows to read 3D models in common formats and write it to the C3D file format. Free version is available.
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Apache ORC

Apache ORC (Optimized Row Columnar) is a free and open-source column-oriented data storage format. It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink, and Apache Hadoop. In February 2013, the Optimized Row Columnar (ORC) file format was announced by Hortonworks in collaboration with Facebook. A calendar month later, the Apache Parquet format was announced, developed by Cloudera and Twitter. Apache ORC format is widely supported including Amazon Web Services' Glue,Google Cloud Platform's BigQuery, and Pandas (software). == History ==
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Cheekd

Cheekd is a dating app based in New York City. It was founded in 2010 by Lori Cheek. == History == The service debuted with the name "Cheek'd". Founder Lori Cheek appeared on the television program, Shark Tank in February 2014, but did not succeed in obtaining funding from any of the five judges. She said Cheek’d only had 1000 subscribers at that time. === Business card model === Cheek'd offered two plans, paid and free. For $25, subscribers got a set of 50 business cards that could be given out once someone caught their eye. Each card had a phrase, an online code, and a URL to the subscriber's account. Recipients could look up the giver's profile. In addition to purchasing cards, there was a $9.95 monthly membership fee. === Smartphone app === In 2015, the service's name changed from "Cheek'd" to "Cheekd". The new app used Bluetooth technology to alert users whenever a compatible user was within a 30-foot radius, instead of using cards. == Patent lawsuit == The original business card-based model for Cheekd had been claimed as a patented process by Lori Cheek, as U.S. patent 8,543,465. In September 2017, a complaint was filed, alleging that the idea was not original to Lori Cheek. Cheek responded, stating that the complaint was baseless, and a complete fabrication. The lawsuit Pirri v. Cheek was dismissed in a pre-trial conference in New York's Federal Court on April 5, 2018.
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DesktopTwo

Desktoptwo was a free Webtop (whose URL was desktoptwo.com and which is now a parked domain) developed by Sapotek (whose URL was sapotek.com, which also is now a parked domain). It's also been called a WebOS although Sapotek stated on its website that the term is premature and presumptuous. It mimics the look, feel and functionality of the desktop environment of an operating system. The software only reached beta stage. It had a Spanish version called Computadora.de. Desktoptwo was web-based and required Adobe Flash Player to operate. The web applications' found on Desktoptwo were built on PHP in the back end. Features included drag-and-drop functionality. Sapotek had liberated all the web applications found on Desktoptwo through Sapodesk on an AGPL license. Desktoptwo belonged to a category of services that intended to turn the Web into a full-fledged platform by using web services as a foundation along with presentation technologies that replicated the experience of desktop applications for users. In a "Cloud OS" the functionality of a server was granularized and abstracted as Web services that Web developers used to create composite applications similar to how desktop software developers use several APIs of the OS to create their applications. Sites like Facebook attempt to create a similar effect by exposing their APIs and allowing developers to create applications upon these. Some of the features found on Desktoptwo were: File sharing, Webmail, Blog creator, Instant messenger, Address book, Calendar, RSS Reader and Office productivity applications. Desktoptwo.com and the Sapotek website no longer operate.
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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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Geo-replication

Geo-replication systems are designed to provide improved availability and disaster tolerance by using geographically distributed data centers. This is intended to improve the response time for applications such as web portals. Geo-replication can be achieved using software, hardware or a combination of the two. == Software == Geo-replication software is a network performance-enhancing technology that is designed to provide improved access to portal or intranet content for users at the most remote parts of large organizations. It is based on the principle of storing complete replicas of portal content on local servers, and then keeping the content on those servers up-to-date using heavily compressed data updates. === Portal acceleration === Geo-replication technologies are used to provide replication of the content of portals, intranets, web applications, content and data between servers, across wide area networks WAN to allow users at remote sites to access central content at LAN speeds. Geo-replication software can improve the performance of data networks that suffer limited bandwidth, latency and periodic disconnection. Terabytes of data can be replicated over a wide area network, giving remote sites faster access to web applications. Geo-replication software uses a combination of data compression and content caching technologies. differencing technologies can also be employed to reduce the volume of data that has to be transmitted to keep portal content accurate across all servers. This update compression can reduce the load that portal traffic places on networks, and improve the response time of a portal. === Portal replication === Remote users of web portals and collaboration environments will frequently experience network bandwidth and latency problems which will slow down their experience of opening and closing files, and otherwise interacting with the portal. Geo-replication technology is deployed to accelerate the remote end user portal performance to be equivalent to that experienced by users locally accessing the portal in the central office. === Differencing engine technologies === To deliver this reduction in the size of the required data updates across a portal, geo-replication systems often use differencing engine technologies. These systems are able to difference the content of each portal server right down to the byte level. This knowledge of the content that is already on each server enables the system to rebuild any changes to the content on one server, across each of the other servers in the deployment from content already hosted on those other servers. This type of differencing system ensures that no content, at the byte level, is ever sent to a server twice. === Offline portal replication on laptops === Geo-replication systems are often extended to deliver local replication beyond the server and down to the laptop used by a single user. Server to laptop replication enables mobile users to have access to a local replica of their business portal on a standard laptop. This technology may be employed to provide in the field access to portal content by, for example, sales forces and combat forces. == Geo-replication systems ==
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Automated essay scoring

Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a form of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades, for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification. Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost. The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test). == History == Most historical summaries of AES trace the origins of the field to the work of Ellis Batten Page. In 1966, he argued for the possibility of scoring essays by computer, and in 1968 he published his successful work with a program called Project Essay Grade (PEG). Using the technology of that time, computerized essay scoring would not have been cost-effective, so Page abated his efforts for about two decades. Eventually, Page sold PEG to Measurement Incorporated. By 1990, desktop computers had become so powerful and so widespread that AES was a practical possibility. As early as 1982, a UNIX program called Writer's Workbench was able to offer punctuation, spelling and grammar advice. In collaboration with several companies (notably Educational Testing Service), Page updated PEG and ran some successful trials in the early 1990s. Peter Foltz and Thomas Landauer developed a system using a scoring engine called the Intelligent Essay Assessor (IEA). IEA was first used to score essays in 1997 for their undergraduate courses. It is now a product from Pearson Educational Technologies and used for scoring within a number of commercial products and state and national exams. IntelliMetric is Vantage Learning's AES engine. Its development began in 1996. It was first used commercially to score essays in 1998. Educational Testing Service offers "e-rater", an automated essay scoring program. It was first used commercially in February 1999. Jill Burstein was the team leader in its development. ETS's Criterion Online Writing Evaluation Service uses the e-rater engine to provide both scores and targeted feedback. Lawrence Rudner has done some work with Bayesian scoring, and developed a system called BETSY (Bayesian Essay Test Scoring sYstem). Some of his results have been published in print or online, but no commercial system incorporates BETSY as yet. Under the leadership of Howard Mitzel and Sue Lottridge, Pacific Metrics developed a constructed response automated scoring engine, CRASE. Currently utilized by several state departments of education and in a U.S. Department of Education-funded Enhanced Assessment Grant, Pacific Metrics’ technology has been used in large-scale formative and summative assessment environments since 2007. Measurement Inc. acquired the rights to PEG in 2002 and has continued to develop it. In 2012, the Hewlett Foundation sponsored a competition on Kaggle called the Automated Student Assessment Prize (ASAP). 201 challenge participants attempted to predict, using AES, the scores that human raters would give to thousands of essays written to eight different prompts. The intent was to demonstrate that AES can be as reliable as human raters, or more so. The competition also hosted a separate demonstration among nine AES vendors on a subset of the ASAP data. Although the investigators reported that the automated essay scoring was as reliable as human scoring, this claim was not substantiated by any statistical tests because some of the vendors required that no such tests be performed as a precondition for their participation. Moreover, the claim that the Hewlett Study demonstrated that AES can be as reliable as human raters has since been strongly contested, including by Randy E. Bennett, the Norman O. Frederiksen Chair in Assessment Innovation at the Educational Testing Service. Some of the major criticisms of the study have been that five of the eight datasets consisted of paragraphs rather than essays, four of the eight data sets were graded by human readers for content only rather than for writing ability, and that rather than measuring human readers and the AES machines against the "true score", the average of the two readers' scores, the study employed an artificial construct, the "resolved score", which in four datasets consisted of the higher of the two human scores if there was a disagreement. This last practice, in particular, gave the machines an unfair advantage by allowing them to round up for these datasets. In 1966, Page hypothesized that, in the future, the computer-based judge will be better correlated with each human judge than the other human judges are. Despite criticizing the applicability of this approach to essay marking in general, this hypothesis was supported for marking free text answers to short questions, such as those typical of the British GCSE system. Results of supervised learning demonstrate that the automatic systems perform well when marking by different human teachers is in good agreement. Unsupervised clustering of answers showed that excellent papers and weak papers formed well-defined clusters, and the automated marking rule for these clusters worked well, whereas marks given by human teachers for the third cluster ('mixed') can be controversial, and the reliability of any assessment of works from the 'mixed' cluster can often be questioned (both human and computer-based). == Different dimensions of essay quality == According to a recent survey, modern AES systems try to score different dimensions of an essay's quality in order to provide feedback to users. These dimensions include the following items: Grammaticality: following grammar rules Usage: using of prepositions, word usage Mechanics: following rules for spelling, punctuation, capitalization Style: word choice, sentence structure variety Relevance: how relevant of the content to the prompt Organization: how well the essay is structured Development: development of ideas with examples Cohesion: appropriate use of transition phrases Coherence: appropriate transitions between ideas Thesis Clarity: clarity of the thesis Persuasiveness: convincingness of the major argument == Procedure == From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored. The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercase to lowercase letters—quantities that can be measured without any human insight. It then constructs a mathematical model that relates these quantities to the scores that the essays received. The same model is then applied to calculate scores of new essays. Recently, one such mathematical model was created by Isaac Persing and Vincent Ng. which not only evaluates essays on the above features, but also on their argument strength. It evaluates various features of the essay, such as the agreement level of the author and reasons for the same, adherence to the prompt's topic, locations of argument components (major claim, claim, premise), errors in the arguments, cohesion in the arguments among various other features. In contrast to the other models mentioned above, this model is closer in duplicating human insight while grading essays. Due to the growing popularity of deep neural networks, deep learning approaches have been adopted for automated essay scoring, generally obtaining superior results, often surpassing inter-human agreement levels. The various AES programs differ in what specific surface features they measure, how many essays are required in the training set, and most significantly in the mathematical modeling technique. Early attempts used linear regression. Modern systems may use linear regression or other machine learning techniques often in combination with other statistical techniques such as latent semantic analysis and Bayesian inference. The automated essay scoring task has also been studied in the cross-domain setting using machine learning models, where the models are trained on essays written for one prompt (topic) and tested on essays written for another prompt. Successful approaches in the cross-domain scenario are based on deep neural networks or models that combine deep and shallow features. == Criteria for success == Any method of a
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Color image pipeline

An image pipeline or video pipeline is the set of components commonly used between an image source (such as a camera, a scanner, or the rendering engine in a computer game), and an image renderer (such as a television set, a computer screen, a computer printer or cinema screen), or for performing any intermediate digital image processing consisting of two or more separate processing blocks. An image/video pipeline may be implemented as computer software, in a digital signal processor, on an FPGA, or as fixed-function ASIC. In addition, analog circuits can be used to do many of the same functions. Typical components include image sensor corrections (including debayering or applying a Bayer filter), noise reduction, image scaling, gamma correction, image enhancement, colorspace conversion (between formats such as RGB, YUV or YCbCr), chroma subsampling, framerate conversion, image compression/video compression (such as JPEG), and computer data storage/data transmission. Typical goals of an imaging pipeline may be perceptually pleasing end-results, colorimetric precision, a high degree of flexibility, low cost/low CPU utilization/long battery life, or reduction in bandwidth/file size. Some functions may be algorithmically linear. Mathematically, those elements can be connected in any order without changing the end-result. As digital computers use a finite approximation to numerical computing, this is in practice not true. Other elements may be non-linear or time-variant. For both cases, there is often one or a few sequences of components that makes sense for optimum precision and minimum hardware-cost/CPU-load.
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Skipper (computer software)

Skipper is a visualization tool and code/schema generator for PHP ORM frameworks like Doctrine2, Doctrine, Propel, and CakePHP, which are used to create database abstraction layer. Skipper is developed by Czech company Inventic, s.r.o. based in Brno, and was known as ORM Designer prior to rebranding in 2014. == Overview == Generates visual model from the schema definition files Repetitive import/export of schema definitions in supported formats (XML, YML, PHP annotations) Schema definition files are automatically generated from the visual model Visual representation uses ER diagram extended by concepts of inheritance and many-to-many Supports customization using .xml configuration files and JavaScript Does not support direct connections to the database Crude and simplistic visual representation and menus == Architecture == Skipper was built on the Qt framework. Import/export of the schema definitions uses XSL transformations powered by LibXslt library. Imported source files are first converted to XML format: no conversion for XML, simple conversion for YML, creating the Abstract Syntax Tree and its subsequent conversion to XML for PHP annotations. The import/export scripts are configured in JavaScript and can be freely customized. == Supported ORM frameworks == Frameworks supported for visual model and schema files generation: Doctrine2 Doctrine CakePHP == History == Skipper was created as an internal tool for the web applications developed by Inventic. It was first published as a commercial tool under the name ORM Designer in 2009. Application was reworked and optimized in January 2013, and released as ORM Designer 2. In May 2013 ORM Designer became part of the South Moravian Innovation Center Incubator program (support program for innovative technological startups). In June 2014, ORM Designer version 3 was released and rebranded under the name of Skipper
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