AI Email Builder

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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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  Open Data Center Alliance

  opendatacenteralliance.org appears to have been closed down. The Open Data Center Alliance is an independent organization created in Oct. 2010 with the assistance of Intel to coordinate the development of standards for cloud computing. Approximately 100 companies, which account for more than $50bn of IT spending, have joined the Alliance, including BMW, Royal Dutch Shell and Marriott Hotels. "The Alliance's Cloud 2015 vision is aimed at creating a federated cloud where common standards will be laid down for those in the hardware and software arena." == Usage Model Roadmap == The organization sees a growing need for solutions developed in an open, industry-standard and multivendor fashion, and has thus created a usage model roadmap featuring 19 prioritized usage models. The usage models provide detailed requirements for data center and cloud solutions, and will include detailed technical documentation discussing the requirements for technology deployments. To further its roadmap development, the steering committee established five initial technical workgroups in the areas of infrastructure, management, regulation & ecosystem, security and services. The organization delivered a 0.50 usage model roadmap to Open Data Center Alliance technical workgroups in Oct. 2010, and delivered a full 1.0 roadmap for public use in June 2011. == Membership == The steering committee consists of BMW, Capgemini, China Life, China Unicom Group, Deutsche Bank, JPMorgan Chase, Lockheed Martin, Marriott International, Inc., National Australia Bank, Royal Dutch Shell, Terremark and UBS. Other members include AT&T, CERN, eBay, Logica, Motorola Mobility Inc. and Nokia. "The demands on the IT organisations are coming at such an alarming rate that there are many, many different solutions being developed today that maybe don't work with each other. We need one voice, one road map, so that companies are able to say to manufacturers here is a clear vision of what they should be developing their product to do." says Marvin Wheeler, of Terremark, chairman of the Alliance. "While it's unclear how successful this alliance will be, it is at least shedding the spotlight on cloud interoperability, a big emerging issue," said Larry Dignan of ZDNet.

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  List of C++ software and tools

  This is a list of notable software and programming tools for the C++ programming language, including libraries, web frameworks, programming language implementations, compilers, integrated development environments (IDEs), and other related software development utilities. == Compilers and IDEs == AMD Optimizing C/C++ Compiler — proprietary fork of LLVM + Clang for Linux C++Builder — rapid application development (RAD) environment Clang – compiler front end for C, C++, and Objective-C, part of LLVM CLion — C++ IDE by JetBrains Code::Blocks — open-source cross-platform IDE that supports multiple compilers including GCC, Clang and Visual C++ CodeLite — cross-platform IDE for the C/C++ programming languages using the wxWidgets toolkit CodeSynthesis XSD – XML Data Binding compiler Dev-C++ — MinGW or TDM-GCC 64bit port of the GCC as its compiler GCC – GNU Compiler Collection Intel C++ Compiler – proprietary high-performance compiler by Intel KDevelop — IDE part of the KDE project and is based on KDE Frameworks and Qt, the C/C++ backend uses Clang. Microsoft Visual C++ – proprietary C++ compiler and IDE for Windows Oracle Developer Studio — Solaris, OpenSolaris, RHEL, and Oracle Linux operating systems. Qt Creator — part of the SDK for the Qt GUI application development framework and uses the Qt API SlickEdit — text editor and IDE Turbo C++ – legacy C++ IDE and compiler popular in the 1990s Understand — IDE that enables static code analysis through an array of visuals, documentation, and metric tools. Visual Studio — integrated development environment by Microsoft that supports C++ Visual Studio Code — integrated development environment by Microsoft that supports C++ Xcode — Apple IDE to develop macOS, iOS, iPadOS, watchOS, tvOS, and visionOS that supports C++ source code. == Debuggers == Allinea DDT – a graphical debugger dbx — a proprietary source-level debugger GNU Debugger – portable debugger that runs on many Unix-like systems Modular Debugger — a C/C++ source level debugger for Solaris and derivates Undo LiveRecorder — time travel debugger == Libraries == Active Template Library – template-based C++ classes developed by Microsoft Apache MXNet — deep learning framework Apache Xerces – parsing, validating, and serializing and manipulating XML. Asio — networking and low-level I/O library Bitpit — scientific computing and mesh manipulation library Boost — collection of peer-reviewed libraries Botan — cryptography library C++ AMP – easy way to write programs that compile and execute on data-parallel hardware, such as graphics cards and GPUs C++ Standard Library — standard library for the language C++/WinRT — library for Microsoft's Windows Runtime platform, designed to provide access to modern Windows APIs. C3D Toolkit — geometric modeling kernel Caffe — deep learning framework CAPD — library for rigorous numerics and dynamical systems Cassowary — constraint-solving toolkit that efficiently solves systems of linear equalities and inequalities Cinder — library for creative coding ClanLib — cross-platform game SDK CMU Sphinx — speech recognition system Crypto++ — cryptographic algorithms library Dlib — general-purpose cross-platform library Dune — partial differential equations using grid-based methods fastText — text representation and text classification library FLTK — GUI toolkit Geospatial Data Abstraction Library — geospatial data access library GDCM — image library General Polygon Clipper — polygon clipping library GiNaC — computer algebra system that uses Class Library for Numbers for implementing arbitrary-precision arithmetic GLFW — OpenGL and window management library HarfBuzz — text rendering and typesetting library High Efficiency Image File Format — digital container format for storing individual digital images and image sequences ITK — image analysis library Integrated Performance Primitives — domain-specific functions that are highly optimized for diverse Intel architectures Jackets library — GPU computing library JSBSim — open-source flight dynamics model JUCE — framework for audio applications KDE Frameworks — collection of libraries from the KDE project KFRlib — digital signal processing framework LEMON — library for optimization and graph problems LevelDB — key–value database library Libdash — MPEG-DASH streaming library libLAS — reading and writing geospatial data encoded in the ASPRS laser (LAS) file format libsigc++ — typesafe callbacks LibRaw — free and open-source software library for reading raw files from digital cameras libSBML — application programming interface (API) for the SBML (Systems Biology Markup Language) LIBSVM — sequential minimal optimization (SMO) algorithm for kernelized support vector machines Libx — DirectX .X files graphics library Loki — collection of design patterns LIVE555 — multimedia streaming library Metakit — embedded database library Microsoft Cognitive Toolkit — deep learning toolkit Microsoft Foundation Class Library — object-oriented library for developing desktop applications for Windows Microsoft SEAL — homomorphic encryption library mlpack — machine learning and AI library Mobile Robot Programming Toolkit — robotics research library Object Windows Library — Object Windows Library, superseded by VCL Open Cascade — CAD and 3D modeling library Open Asset Import Library — 3D model import library to provide a common API for different 3D asset file formats OpenCV – computer vision and machine learning library OpenFOAM — computational fluid dynamics toolkit OpenH264 — real-time encoding and decoding video streams in the H.264/MPEG-4 AVC format OpenImageIO — image processing library Open Inventor — higher layer of programming for OpenGL OpenNN — neural networks library OpenVDB — sparse volume data library openFrameworks — creative coding toolkit OpenRTM-aist — robotics middleware library Oracle Template Library — database access that supports IBM Db2 and Open Database Connectivity Orfeo toolbox — remote sensing image processing library OR-Tools — operations research and optimization library Parallel Augmented Maps — ordered sets, ordered maps, and augmented maps. Parallel Patterns Library — Microsoft library that provides features for multicore programming PhysX — physics simulation engine POCO C++ Libraries — general-purpose libraries for software development Poppler — PDF rendering library Protocol Buffers — data serialization library Qt — cross-platform widget toolkit QuantLib — quantitative finance library RocksDB — key–value database library ROOT — data analysis framework from CERN ROS — robotics middleware Scintilla — source code editing component SDL – Simple DirectMedia Layer, cross-platform development library for multimedia applications SFML – Simple and Fast Multimedia Library Shark – open-source machine learning library Shogun — machine learning toolbox Skia — 2D graphics library Snappy — compression library Sound Object Library — music and audio development Standard Template Library — library of containers and algorithms Stapl — parallel computing library SymbolicC++ — symbolic computation library TerraLib — GIS library Tesseract OCR — optical character recognition engine Threading Building Blocks — parallel computing library ThreadWeaver — concurrency framework Tiny-dnn — lightweight deep learning library TinyXML — lightweight XML parser Tkrzw — key–value databases VTD-XML — XML processing library wxWidgets — cross-platform GUI toolkit x265 — video encoding library for HEVC XGBoost — gradient boosting library Windows Template Library — Win32 development === Mathematical and numerical libraries === == Tools == Akonadi — a C++/Qt framework and storage service for personal information management BALL – framework and set of algorithms and data structures for molecular modelling and computational structural bioinformatics Boehm garbage collector – conservative garbage collector CEGUI — C++ GUI library ClanLib – video game SDK CMake — cross-platform build system for C++ projects Confidential Consortium Framework – blockchain infrastructure framework DaviX – WebDAV client Doxygen — documentation generator for C++ and other languages FLTK — Fast Light Toolkit, cross-platform GUI library Fox toolkit — C++ GUI toolkit GDB — GNU Project debugger, often used with C and C++ gtkmm — official C++ interface for the popular GUI library GTK HOOPS Visualize — 3D computer graphics HPX — partitioned global address space Parallel programming Runtime System JUCE — cross-platform C++ audio and GUI framework LessTif — free clone of Motif GUI toolkit MFC — Microsoft Foundation Class library Nana — modern C++ GUI toolkit PTK Toolkit — 2D rendering engine and SDK, and portability options. Qt — cross-platform C++ GUI toolkit Rogue Wave — C++ GUI toolkit TnFOX — C++ GUI toolkit Ultimate++ — cross-platform C++ GUI framework Valgrind — tool suite for debugging and profiling C/C++ programs wxWidgets — cross-platform C++ GUI toolkit x265 — encoder for creating digital video streams in the High Efficiency Vid

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  Bring your own encryption

  Bring your own encryption (BYOE), also known as bring your own key (BYOK), is a cloud computing security model that allows cloud service customers to use their own encryption software and manage their own encryption keys. == Overview == BYOE enables cloud service customers to utilize a virtual instance of their encryption software alongside their cloud-hosted business applications to encrypt their data. In this model, hosted business applications are configured to process all data through the encryption software. This software then writes the ciphertext version of the data to the cloud service provider's physical data store and decrypts ciphertext data upon retrieval requests. This approach provides enterprises with control over their keys and the ability to generate their own master key using internal hardware security modules (HSM), which are then transmitted to the cloud provider's HSM. When the data is no longer needed, such as when users discontinue the cloud service, the keys can be deleted, rendering the encrypted data permanently inaccessible. This practice is known as crypto-shredding. == Potential Advantages == Organizations can store data with unique encryption that only they can access. Multiple organizations can share the same hardware infrastructure via cloud services like Amazon Web Services (AWS) or Google Cloud while maintaining encryption to comply with regulations such as HIPAA. == Potential Challenges == Resource utilization may be higher compared to traditional encryption practices when multiple users share the same hardware and use their own encryption. Efforts to minimize resource utilization issues may potentially impact security benefits.

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  Similarity learning

  Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the goal is to learn a similarity function that measures how similar or related two objects are. It has applications in ranking, in recommendation systems, visual identity tracking, face verification, and speaker verification. == Learning setup == There are four common setups for similarity and metric distance learning. Regression similarity learning In this setup, pairs of objects are given ( x i 1 , x i 2 ) {\displaystyle (x_{i}^{1},x_{i}^{2})} together with a measure of their similarity y i ∈ R {\displaystyle y_{i}\in R} . The goal is to learn a function that approximates f ( x i 1 , x i 2 ) ∼ y i {\displaystyle f(x_{i}^{1},x_{i}^{2})\sim y_{i}} for every new labeled triplet example ( x i 1 , x i 2 , y i ) {\displaystyle (x_{i}^{1},x_{i}^{2},y_{i})} . This is typically achieved by minimizing a regularized loss min W ∑ i l o s s ( w ; x i 1 , x i 2 , y i ) + r e g ( w ) {\displaystyle \min _{W}\sum _{i}loss(w;x_{i}^{1},x_{i}^{2},y_{i})+reg(w)} . Classification similarity learning Given are pairs of similar objects ( x i , x i + ) {\displaystyle (x_{i},x_{i}^{+})} and non similar objects ( x i , x i − ) {\displaystyle (x_{i},x_{i}^{-})} . An equivalent formulation is that every pair ( x i 1 , x i 2 ) {\displaystyle (x_{i}^{1},x_{i}^{2})} is given together with a binary label y i ∈ { 0 , 1 } {\displaystyle y_{i}\in \{0,1\}} that determines if the two objects are similar or not. The goal is again to learn a classifier that can decide if a new pair of objects is similar or not. Ranking similarity learning Given are triplets of objects ( x i , x i + , x i − ) {\displaystyle (x_{i},x_{i}^{+},x_{i}^{-})} whose relative similarity obey a predefined order: x i {\displaystyle x_{i}} is known to be more similar to x i + {\displaystyle x_{i}^{+}} than to x i − {\displaystyle x_{i}^{-}} . The goal is to learn a function f {\displaystyle f} such that for any new triplet of objects ( x , x + , x − ) {\displaystyle (x,x^{+},x^{-})} , it obeys f ( x , x + ) > f ( x , x − ) {\displaystyle f(x,x^{+})>f(x,x^{-})} (contrastive learning). This setup assumes a weaker form of supervision than in regression, because instead of providing an exact measure of similarity, one only has to provide the relative order of similarity. For this reason, ranking-based similarity learning is easier to apply in real large-scale applications. Locality sensitive hashing (LSH) Hashes input items so that similar items map to the same "buckets" in memory with high probability (the number of buckets being much smaller than the universe of possible input items). It is often applied in nearest neighbor search on large-scale high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. A common approach for learning similarity is to model the similarity function as a bilinear form. For example, in the case of ranking similarity learning, one aims to learn a matrix W that parametrizes the similarity function f W ( x , z ) = x T W z {\displaystyle f_{W}(x,z)=x^{T}Wz} . When data is abundant, a common approach is to learn a siamese network – a deep network model with parameter sharing. == Metric learning == Similarity learning is closely related to distance metric learning. Metric learning is the task of learning a distance function over objects. A metric or distance function has to obey four axioms: non-negativity, identity of indiscernibles, symmetry and subadditivity (or the triangle inequality). In practice, metric learning algorithms ignore the condition of identity of indiscernibles and learn a pseudo-metric. When the objects x i {\displaystyle x_{i}} are vectors in R d {\displaystyle R^{d}} , then any matrix W {\displaystyle W} in the symmetric positive semi-definite cone S + d {\displaystyle S_{+}^{d}} defines a distance pseudo-metric of the space of x through the form D W ( x 1 , x 2 ) 2 = ( x 1 − x 2 ) ⊤ W ( x 1 − x 2 ) {\displaystyle D_{W}(x_{1},x_{2})^{2}=(x_{1}-x_{2})^{\top }W(x_{1}-x_{2})} . When W {\displaystyle W} is a symmetric positive definite matrix, D W {\displaystyle D_{W}} is a metric. Moreover, as any symmetric positive semi-definite matrix W ∈ S + d {\displaystyle W\in S_{+}^{d}} can be decomposed as W = L ⊤ L {\displaystyle W=L^{\top }L} where L ∈ R e × d {\displaystyle L\in R^{e\times d}} and e ≥ r a n k ( W ) {\displaystyle e\geq rank(W)} , the distance function D W {\displaystyle D_{W}} can be rewritten equivalently D W ( x 1 , x 2 ) 2 = ( x 1 − x 2 ) ⊤ L ⊤ L ( x 1 − x 2 ) = ‖ L ( x 1 − x 2 ) ‖ 2 2 {\displaystyle D_{W}(x_{1},x_{2})^{2}=(x_{1}-x_{2})^{\top }L^{\top }L(x_{1}-x_{2})=\|L(x_{1}-x_{2})\|_{2}^{2}} . The distance D W ( x 1 , x 2 ) 2 = ‖ x 1 ′ − x 2 ′ ‖ 2 2 {\displaystyle D_{W}(x_{1},x_{2})^{2}=\|x_{1}'-x_{2}'\|_{2}^{2}} corresponds to the Euclidean distance between the transformed feature vectors x 1 ′ = L x 1 {\displaystyle x_{1}'=Lx_{1}} and x 2 ′ = L x 2 {\displaystyle x_{2}'=Lx_{2}} . Many formulations for metric learning have been proposed. Some well-known approaches for metric learning include learning from relative comparisons, which is based on the triplet loss, large margin nearest neighbor, and information theoretic metric learning (ITML). In statistics, the covariance matrix of the data is sometimes used to define a distance metric called Mahalanobis distance. == Applications == Similarity learning is used in information retrieval for learning to rank, in face verification or face identification, and in recommendation systems. Also, many machine learning approaches rely on some metric. This includes unsupervised learning such as clustering, which groups together close or similar objects. It also includes supervised approaches like K-nearest neighbor algorithm which rely on labels of nearby objects to decide on the label of a new object. Metric learning has been proposed as a preprocessing step for many of these approaches. == Scalability == Metric and similarity learning scale quadratically with the dimension of the input space, as can easily see when the learned metric has a bilinear form f W ( x , z ) = x T W z {\displaystyle f_{W}(x,z)=x^{T}Wz} . Scaling to higher dimensions can be achieved by enforcing a sparseness structure over the matrix model, as done with HDSL, and with COMET. == Software == metric-learn is a free software Python library which offers efficient implementations of several supervised and weakly-supervised similarity and metric learning algorithms. The API of metric-learn is compatible with scikit-learn. OpenMetricLearning is a Python framework to train and validate the models producing high-quality embeddings. == Further information == For further information on this topic, see the surveys on metric and similarity learning by Bellet et al. and Kulis.

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  Radek Maneuver

  The Radek Maneuver is a scale-up-then-scale-down tactic used in the administration of web services, specifically those deployed under a cloud computing paradigm (by a provider e.g. Amazon Elastic Compute Cloud or Microsoft Azure). == History == Developed by Olivier "Radek" Dabrowski in the mid-2010s, the Radek Maneuver was originally conceived of in using and maintaining applications running on a PaaS system. == Execution == The Radek Maneuver consists of a series of steps, usually executed via the PaaS or web portal interface. The tactic should be used when a service is misbehaving or otherwise experiencing errors, and the suspected cause is the underlying cloud layer, rather than the application layer. This includes networking issues and other "bad box" problems. The steps are as follows: Identify the application or service which is misbehaving. Increase the compute resource (number of CPU cores, amount of ram) for the instance on which the application is running. This is also known as scaling up. Wait for the application to re-deploy and stabilize. Scale back down to the original instance size. == Principle of action == This scale-up-scale-down method is understood to shift the application to a different physical machine underlying the PaaS service or application virtual machine. While this layer of the cloud computing stack is generally out of the access of an application developer (instead in the hands of the cloud provider), the maneuver allows troubleshooting and dodging errors in that layer.

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  Boundary vector field

  The boundary vector field (BVF) is an external force for parametric active contours (i.e. Snakes). In the fields of computer vision and image processing, parametric active contours are widely used for segmentation and object extraction. The active contours move progressively towards its target based on the external forces. There are a number of shortcomings in using the traditional external forces, including the capture range problem, the concave object extraction problem, and high computational requirements. The BVF is generated by an interpolation scheme which reduces the computational requirement significantly, and at the same time, improves the capture range and concave object extraction capability. The BVF is also tested in moving object tracking and is proven to provide fast detection method for real time video applications.

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  Elements (toolchain)

  RemObjects Elements is a toolchain for software development, comprising six programming languages: C#, Swift, Go, Java, Oxygene (a form of modern Object Pascal), and Visual Basic .NET. All languages interoperate, meaning a single project can use any combination of languages, and they can all be compiled to .NET, the JVM, native, or WebAssembly. Elements supports Microsoft Windows, all Apple Inc. platforms (including iOS, visionOS and watchOS), Android, and Linux. Elements also supports language conversion, allowing source code in one language to be rewritten in another. Elements is supported in Visual Studio, but RemObjects also makes their own IDEs, Fire (on MacOS) and Water (on Windows.) == Background == RemObjects began in 2002, creating software for Delphi, but in 2005 in response to the growth of .NET and that Delphi was targeting only native Windows, they released Oxygene (known as Chrome at the time) as a new version of Object Pascal, with more modern syntax as well as being .NET-native. Since then, five other languages have been added to the suite, as well as compiling for the web via WebAssembly and to native architectures (eg Intel 32/64 or ARM64). Elements is primarily intended for developers who want to pull together libraries and codebases written in multiple languages, including legacy codebases in older languages while modernizing either with newer syntax and features or by adding in the use of newer or more popular languages. Because of the Oxygene flavour of Object Pascal, supporting Delphi apps is a primary focus, including allowing Pascal to be compiled for other architectures or providing language features that match other prominent languages. == Approach == New versions of the Elements come out approximately every week. RemObjects names its programming languages after chemical elements, sometimes with poetic or musical spelling, rather than referring to them directly. They are: C#: Hydrogene Object Pascal: Oxygene Java: Iodine Visual Basic: Mercury Go: Gold Swift: Silver == History == The Elements compiler was first introduced with version 1.0 in 2005 under the name "Chrome", with support for only the Oxygene language on the .NET platform, primarily as a response to the then-new and not well-received Delphi .NET compiler from Embarcadero. Chrome saw updates to version 1.5 'Floorshow' and Chrome 2.0 'Joyride' over the next few years, moving in parallel with major advancements on the .NET platform for .NET 2.0 (Generics) and .NET 3.x (LINQ), respectively. With the release of version 3.0 (code-named Oxygène after the Jean-Michel Jarre album of the same name) Chrome was rebranded to Oxygene in 2008, and also shipped co-branded by Embarcadero as Delphi Prism (later just Prism) as part of RAD Studio, replacing Embarcadero's own and now-defunct Delphi.NET compiler. 2010 saw the release of Oxygene 4 ("Echoes"), the last version to focus on just a single language and platform. With Oxygene 5 in 2011 and Oxygene 6 in 2013, RemObjects introduced new platform support for Java/Android (code-name "Cooper") and then Cocoa, the Apple development platform (code-name "Toffee"). Elements 7.0 was released at the beginning of 2014, adding the second programming language, C# to the compiler, and delegating Oxygene from the product name to merely branding the Object Pascal-based language. Over the subsequent years, Elements gained support for additional languages, with Apple Swift in 2015, Java in 2017, and subsequently Google's Go and Mercury, a revitalized Visual Basic.NET. Elements also gained support for its fourth target platform, "Island", for CPU-native compilation for Windows, Linux, and WebAssembly. In addition to the chemical elements-based names for the different languages, the "Elements" concept was carried on with the introduction of dedicated development environments alchemically named Fire (for the Mac, in 2015) and Water (for Windows, in 2018). == Fire and Water (IDEs) == Fire and Water are integrated development environments developed by RemObjects Software. They are designed specifically for use with the Elements Compiler. Fire is the version developed for macOS, while Water is intended for Microsoft Windows. Both IDEs are designed to work closely with the Elements compiler and are primarily intended for developers using the RemObjects language ecosystem. They support software development across multiple platforms, including .NET, Android, iOS, macOS, Windows, Linux, and WebAssembly. The IDEs include standard development tools such as syntax highlighting, code completion, debugging, and project navigation. Build operations are managed using a custom system known as EBuild, which is part of the broader Elements toolchain. The IDEs are distributed as part of the RemObjects Elements package and are updated in coordination with the compiler itself. == In media == Oxygene has been mentioned several times by Verity Stob in their Chronicles of Delphi series, currently living at The Register.

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  Computer Law & Security Review

  The Computer Law & Security Review is an international peer-reviewed journal published by Elsevier. It has been published six times a year since 1985 and is indexed in Scopus and SSCI. It is accessible to a wide range of professional legal and IT practitioners, businesses, academics, researchers, libraries and organisations in both the public and private sectors. The journal regularly covers: CLSR Briefing with special emphasis on UK/US developments European Union update National news from 10 European jurisdictions Pacific rim news column Refereed practitioner and academic papers on topics such as Web 2.0, IT security, Identity management, ID cards, RFID, interference with privacy, Internet law, telecoms regulation, online broadcasting, intellectual property, software law, e-commerce, outsourcing, data protection and freedom of information and many other topics. The Journal's Correspondent Panel includes more than 40 specialists in IT law and security. Each issue contains articles, case law analysis and current news on information and communications technology. Special Features High quality peer reviewed papers from internationally renowned practitioner and academic experts Latest developments reported in situ by more than 20 leading law firms from around the world Highly experienced and respected editor and correspondents panel Online access to all 23 volumes of CLSR with embedded web links to primary sources Contact details of all authors A pool of expertise that can collectively identify the key topics that need to be examined.

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  List & Label

  List & Label is a professional reporting tool for software developers. It provides comprehensive design, print and export functions. The software component runs on Microsoft Windows and can be implemented in desktop, cloud and web applications. List & Label can be used to create user-defined dashboards, lists, invoices, forms and labels. It supports many development environments, frameworks and programming languages such as Microsoft Visual Studio, Embarcadero RAD Studio, .NET Framework, .NET Core, ASP.NET, C++, Delphi, Java, C Sharp and some more. List & Label either retrieves data from various sources via data binding, or works database independent. Reports are designed and created in the so-called List & Label Designer and then exported into a multitude of formats like PDF, Excel, XHTML and RTF. Since version 27 a web report designer for ASP.NET MVC is available. == History == The product was first released in 1992 by combit. The current version is 30. A new major version of List & Label is released every fall, usually in October. Updates are available several times a year via Service Pack. == Features == === Report Designer === The Designer enables users to graphically layout the report. It offers report objects such as tables, charts, crosstabs, gauges, HTML, conditionally formatted text, barcodes, matrix codes, and graphics, and is extensible using third-party add-ons. User applications can interact with the report via the programmable object model of the report. The real-time preview functionality allows users to view changes instantly. Usability features include layer and appearance management, enabling conditional logic to dynamically control the visibility of objects in reports. The Designer also supports the inclusion of multiple report containers in a single project, accommodating complex layouts such as parallel tables and charts. A formula wizard and support for scripting languages such as C# facilitate advanced calculations and logic. The Designer's object model (DOM) provides developers with the ability to modify layouts and behaviors programmatically. === Web Report Designer === The web report designer works browser-based and independent from printer drivers and spoolers - that makes deployments to the cloud easier. Just like the use of the Visual Studio deployment pipeline. === Data Sources === Depending on the programming language, the product offers automatic support for data sources: Databases such as Microsoft SQL Server, Oracle, MySQL, PostgreSQL, IBM Db2, SQLite, MariaDB, MongoDB, Cosmos DB XML data, CSV Business objects Data sources that can be accessed via OLE DB, ODBC or ADO.NET LINQ data and data from web services GraphQL Additionally, the product offers support for unbound data and can be extended to support other data sources via interfaces. === Output Options === Printer Image Formats (JPEG, BMP, EMF, TIFF, PNG, SVG, HEIF, WebP) Document Formats: PDF, PDF/A, Word (DOCX), Excel (XLS), PowerPoint (PPTX) HTML, XHTML, MHTML Barcodes Plain Text, RTF, CSV, JSON XML, ZIP, Email, JSON List & Label preview file === Target Audience === List & Label can be used in Windows development environments. While it competes most notably on the Microsoft .NET platform with other products such as Crystal Reports, SQL Server Reporting Services, ActiveReports, there are few competing products for other programming languages (e.g. Progress, Alaska Xbase++, Visual DataFlex). == Awards == Reader's Choice Award 2005–2008 Stevie Awards 2021: Best Technology for Data Visualization Top 100 Publisher Award Component Source 2013-2014, 2014-2015,2016, 2018, 2019, 2020, 2021, 2022

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  Google Cloud Dataflow

  Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem. Dataflow provides a fully managed service for executing Apache Beam pipelines, offering features like autoscaling, dynamic work rebalancing, and a managed execution environment. Dataflow is suitable for large-scale, continuous data processing jobs, and is one of the major components of Google's big data architecture on the Google Cloud Platform. At its core, Dataflow's architecture is designed to abstract away infrastructure management, allowing developers to focus purely on the logic of their data processing tasks. When a pipeline written using the Apache Beam SDK is submitted, Dataflow translates this high-level definition into an optimized job graph. The service then provisions and manages a fleet of Google Compute Engine workers to execute this graph in a highly parallelized and fault-tolerant manner. This serverless approach, combined with intelligent autoscaling of both the number of workers (horizontal) and the resources per worker (vertical), ensures that jobs have the precise amount of computational power needed at any given time, optimizing both performance and cost. The service's deep integration with the Google Cloud ecosystem makes it a powerful tool for a variety of use cases beyond simple data movement. For real-time analytics, Dataflow can ingest unbounded streams of data from Cloud Pub/Sub, perform complex transformations, and load results into BigQuery for immediate querying. In machine learning workflows, it is commonly used to preprocess and transform massive datasets stored in Cloud Storage, preparing them for training models in Vertex AI. This versatility makes it the central processing engine for modern ETL (Extract, Transform, Load) operations, streaming analytics, and large-scale data preparation within the cloud. == History == Google Cloud Dataflow was announced in June, 2014 and released to the general public as an open beta in April, 2015. In January, 2016 Google donated the underlying SDK, the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. The donated code formed the original basis for Apache Beam. In August 2022, there was an incident where user timers were broken for certain Dataflow streaming pipelines in multiple regions, which was later resolved. Throughout 2023 and 2024, there have been various other updates and incidents affecting Google Cloud Dataflow, as documented in the release notes and service health history. The donation of the Dataflow SDK to the Apache Software Foundation was a pivotal moment, establishing Apache Beam as a unified, open-source programming model for defining both batch and streaming data pipelines. This strategic move decoupled the pipeline definition from the execution engine. As a result, developers could write portable data processing logic that was not locked into Google's ecosystem. A Beam pipeline can be executed on various runners, including Apache Flink, Apache Spark, and, of course, the highly optimized Google Cloud Dataflow service, providing flexibility and future-proofing data processing investments. == Features == Google Cloud Dataflow supports both batch and streaming data processing pipelines. It automatically handles resource provisioning, data sharding, and scaling according to workload, reducing manual configuration needed for large-scale data operations. == Use cases == Dataflow is used for ETL (Extract, Transform, Load) data pipelines, real-time analytics, and event stream processing for companies in industries such as finance, advertising, and IoT.

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  Amazon Elastic Compute Cloud

  Amazon Elastic Compute Cloud (EC2) is a part of Amazon's cloud-computing platform, Amazon Web Services (AWS), that allows users to rent virtual computers on which to run their own computer applications. EC2 encourages scalable deployment of applications by providing a web service through which a user can boot an Amazon Machine Image (AMI) to configure a virtual machine, which Amazon calls an "instance", containing any software desired. A user can create, launch, and terminate server-instances as needed, paying by the second for active servers – hence the term "elastic". EC2 provides users with control over the geographical location of instances that allows for latency optimization and high levels of redundancy. In November 2010, Amazon switched its own retail website platform to EC2 and AWS. == History == Amazon announced a limited public beta test of EC2 on August 25, 2006, offering access on a first-come, first-served basis. Amazon added two new instance types (Large and Extra-Large) on October 16, 2007. On May 29, 2008, two more types were added, High-CPU Medium and High-CPU Extra Large. There were twelve types of instances available. Amazon added three new features on March 27, 2008: static IP addresses, availability zones, and user-selectable kernels. On August 20, 2008, Amazon added Elastic Block Store (EBS). This provides persistent storage, a feature that had been lacking since the service was introduced. Amazon EC2 went into full production when it dropped the beta label on October 23, 2008. On the same day, Amazon announced the following features: a service level agreement for EC2, Microsoft Windows in beta form on EC2, Microsoft SQL Server in beta form on EC2, plans for an AWS management console, and plans for load balancing, autoscaling, and cloud monitoring services. These features were subsequently added on May 18, 2009. Amazon EC2 was developed mostly by a team in Cape Town, South Africa led by Chris Pinkham. Pinkham provided the initial architecture guidance for EC2 and then built the team and led the development of the project along with Willem van Biljon. == Instance types == Initially, EC2 used Xen virtualization exclusively. However, on November 6, 2017, Amazon announced the new C5 family of instances that were based on a custom architecture around the KVM hypervisor, called Nitro. Each virtual machine, called an "instance", functions as a virtual private server. Amazon sizes instances based on "Elastic Compute Units". The performance of otherwise identical virtual machines may vary. On November 28, 2017, AWS announced a bare-metal instance, a departure from exclusively offering virtualized instance types. As of January 2019, the following instance types were offered: General Purpose: A1, T3, T2, M5, M5a, M4, T3a Compute Optimized: C5, C5n, C4 Memory Optimized: R5, R5a, R4, X1e, X1, High Memory, z1d Accelerated Computing: P3, P2, G3, F1 Storage Optimized: H1, I3, D2 As of April 2018, the following payment methods by instance were offered: On-demand: pay by the hour without commitment. Reserved: rent instances with one-time payment receiving discounts on the hourly charge. Spot: bid-based service: runs the jobs only if the spot price is below the bid specified by bidder. The spot price is claimed to be supply-demand based, however a 2011 study concluded that the price was generally not set to clear the market, but was dominated by an undisclosed reserve price. In 2025, AWS expanded EC2 with the compute-optimized C8gn family, powered by Graviton4 and offering up to 600 Gbit/s network bandwidth (about 30% higher compute performance than C7gn), and introduced G6f fractional-GPU instances that let customers provision one-eighth, one-quarter, or one-half of an NVIDIA L4 GPU for right-sized graphics/ML workloads. === Cost === As of April 2018, Amazon charged about $0.0058 per hour ($4.176 per month) for the smallest "Nano Instance" (t2.nano) virtual machine running Linux or Windows. Storage-optimized instances cost as much as $4.992 per hour (i3.16xlarge). "Reserved" instances can go as low as $2.50 per month for a three-year prepaid plan. The data transfer charge ranges from free to $0.12 per gigabyte, depending on the direction and monthly volume (inbound data transfer is free on all AWS services). EC2 costs can be analyzed using the Amazon Cost and Usage Report. There are many different cost categories for EC2 including: hourly Instance Charges, Data Transfer, EBS Volumes, EBS Volume Snapshots, and Nat Gateway. === Free tier === As of December 2010 Amazon offered a bundle of free resource credits to new account holders. The credits are designed to run a "micro" sized server, storage (EBS), and bandwidth for one year. Unused credits cannot be carried over from one month to the next. === Reserved instances === Reserved instances enable EC2 or RDS service users to reserve an instance for one or three years. The corresponding hourly rate charged by Amazon to operate the instance is 35 to 75% lower than the rate charged for on-demand instances. Reserved instances can be purchased with three different payment options: All Upfront, Partial Upfront and No Upfront. The different purchase options allow for different structuring of payment models, with a larger discount given to customers that pay their reservation upfront. Reserved Instances are purchased based on a resource commitment. These reservations are made based on an instance type and a count of that instance type. For example, you could reserve 100 i3.large instances for a 3-year term. In September 2016, AWS announced several enhancements to Reserved instances, introducing a new feature called scope and a new reservation type called a Convertible. In October 2017, AWS announced the allowance to subdivide the instances purchased for more flexibility. === Spot instances === Cloud providers maintain large amounts of excess capacity they have to sell or risk incurring losses. Amazon EC2 Spot instances are spare compute capacity in the AWS cloud available at up to 90% discount compared to On-Demand prices. As a trade-off, AWS offers no SLA on these instances and customers take the risk that it can be interrupted with only two minutes of notification when Amazon needs the capacity back. Researchers from the Israeli Institute of Technology found that "they (Spot instances) are typically generated at random from within a tight price interval via a dynamic hidden reserve price". Some companies, like Spotinst, are using machine learning to predict spot interruptions up to 15 minutes in advance. === Savings Plans === In November 2019, Amazon announced Savings Plans. Savings Plans are an alternative to Reserved Instances that come in two different plan types: Compute Savings Plans and EC2 Instances Savings Plans. Compute Savings Plans allow an organization to commit to EC2 and Fargate usage with the freedom to change region, family, size, availability zone, OS and tenancy inside the lifespan of the commitment. EC2 Instance Savings plans provide a larger discount than Compute Savings Plans but are less flexible meaning a user must commit to individual instance families within a region to take advantage, but with the freedom to change instances within the family in that region. AWS uses the Cost Explorer to automatically calculate recommendations for the commitments you should make how that commitment will look like as a monthly charge on your AWS bill. AWS Savings Plans are purchased based on hourly spend commitment. This hourly commitment is made using the discounted pricing of the savings plan you are purchasing. For example, you could commit to spending $5 per hour, on a Compute Savings Plan, for a 3-year term. == Features == === Operating systems === When it launched in August 2006, the EC2 service offered Linux and later Sun Microsystems' OpenSolaris and Solaris Express Community Edition. In October 2008, EC2 added the Windows Server 2003 and Windows Server 2008 operating systems to the list of available operating systems. In March 2011, NetBSD AMIs became available. In November 2012, Windows Server 2012 support was added. Since 2006, Colin Percival, a FreeBSD developer and Security Officer, solicited Amazon to add FreeBSD. In November 2012, Amazon officially supported running FreeBSD in EC2. The FreeBSD/EC2 platform is maintained by Percival who also developed the secure deduplicating Amazon S3-cloud based backup service Tarsnap. Amazon has their own Linux distribution based on Fedora and Red Hat Enterprise Linux as a low cost offering known as the Amazon Linux AMI. Version 2013.03 included: Linux kernel, Java OpenJDK Runtime Environment and GNU Compiler Collection. On November 30, 2020, Amazon announced that it would be adding macOS to the EC2 service. Initial support was announced for macOS Mojave and macOS Catalina running on Mac Mini. === Managed Container and Kubernetes Services === Amazon Elastic Container Registry (ECR) is a Docker registry service for Amazon EC2

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  System integrity

  In telecommunications, the term system integrity has the following meanings: That condition of a system wherein its mandated operational and technical parameters are within the prescribed limits. The quality of an AIS when it performs its intended function in an unimpaired manner, free from deliberate or inadvertent unauthorized manipulation of the system. The state that exists when there is complete assurance that under all conditions an IT system is based on the logical correctness and reliability of the operating system, the logical completeness of the hardware and software that implement the protection mechanisms, and data integrity.

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  JDoodle

  JDoodle is a cloud-based online integrated development environment and compiler platform that supports execution of source code in 70+ programming languages including Java, Python, C/C++, PHP, Ruby, Perl, HTML, and more. It provides zero‑setup code for compilation, execution, and sharing via a web browser interface. == Features == Provides real‑time collaboration and code embedding via shareable URLs and APIs Offers an integrated terminal interface supporting database engines such as MySQL and MongoDB. JDroid — AI‑assistant to generate code snippets, optimize code, and assist debugging. == Languages and frameworks supported ==

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  24SevenOffice

  24SevenOffice is a Norwegian software company headquartered in Oslo, Norway, with offices in Stockholm, Sweden and London, United Kingdom. Founded in 1997, the company specializes in web-based (SaaS) ERP and CRM systems. == Company history == 24SevenOffice was founded in 1997 in Porsgrunn, Norway, as IKT Interactive AS and marketed as kontorplassen.no. The name "24SevenOffice" was introduced for the company's London branch when the company entered the British market in 2003. The company changed its name to 24SevenOffice in February 2005. Originally based in Skien, the company later moved to Oslo Innovation Center, then to Tjuvholmen in the waterfront Fjord City of Oslo, and now the headquarters are located in Inkognitogaten 33, Solli plass, Oslo. The idea for the company's product was developed in 1996, and 24SevenOffice was an early innovator in the Scandinavian market in web-based enterprise resource planning solutions (ERP). A British office was established at Surrey Business Park in May 2003, with the company launching its web-based (SaaS) utility computing system to the UK SME market in 2004. An office in Chennai, India, was established in 2005, and 24SevenOffice entered the Swedish market when they acquired the leading competitor and ERP-provider Start & Run in a cash deal. In August 2005, the company had an initial public offering that raised NOK 15 million, and the company entered The Norwegian Over the Counter Market list as of 5 October 2005 (the ticker was 24SO), reaching a market value of NOK 175 million, with 5000 customers in Norway. In 2006, the company signed a deal to sponsor rally driver Petter Solberg, at the time the largest private sponsorship in Norwegian sport. Instead of receiving NOK 5 million in cash, Solberg received a 2.9 per cent ownership in the company. The company entered the German-speaking market in April 2006 when an office in Frankfurt am Main was opened. In late August/early September, they established an office with ten sales agents plus a general manager in Stockholm for the Swedish market. 24SevenOffice initiated strategic cooperation with Active 24 in early 2006 to develop a common platform. During the summer, Active 24 was bought by 24SevenOffice's ERP/CRM competitor Mamut (company), and 24SevenOffice terminated the contract with Active 24 in October demanding NOK 200 million in compensation for lost revenue. After a breakdown of settlement negotiations in the Forliksråd in January 2007, 24SevenOffice filed a case against Active 24 for breach of agreement in the Oslo District Court in March. 24SevenOffice lost on all counts in the District Court in December 2007. In January 2008, 24SevenOffice appealed the case to the Borgarting Court of Appeal, reducing the cause of action from NOK 250 to 30 million. 24SevenOffice lost on all counts in the Court of Appeal in December 2008, and was ordered to cover the costs incurred by Active 24 in connection with the dispute totaling NOK 6.91 million. 24SevenOffice appealed the case to the Supreme Court of Norway, but the Supreme Court Appeals Committee in March 2008 unanimously rejected the appeal from 24SevenOffice over the Borgarting Appeal Court's unanimous judgment of December 2008. On a counterclaim from Active 24 and Mamut against 24SevenOffice, the Oslo District Court in May 2010 found, that 24SevenOffice should pay Active 24 NOK 12 million in compensation for wrongfully having terminated the agreement, and a further NOK 360.000 of the opponent's legal costs. 24SevenOffice disagreed with the court ruling, and appealed once again. The Borgarting Court of Appeal in November 2011, ruled to reduce the amount of damages to NOK 4.4 million plus NOK 900.000 in penal interest. With several scrip issues, 24SevenOffice raised 25 million NOK (about $4 million at the time) between October 2005 and July 2006. They entered into a strategic partnership with Bluegarden, who for 30 years had delivered digital services for payroll, human resource planning, recruitment and training, in March 2006, and they made a large-scale agreement in April 2006, with US telecommunications software company Webex, a competitor to Norwegian Tandberg videoconferencing equipment manufacturer. In September 2006, 24SevenOffice signed an agreement with Fokus Bank to provide their customers with extended functionality in Internet banking. 24SevenOffice had by 2007 reportedly 9000 customers, joined the OpenAjax Alliance, and entered into a strategic partnership with Dun & Bradstreet in May 2007, but despite getting listed on Oslo Axess on 22 June (ticker: TFSO), reaching a market capitalization of NOK 120 million, the company was still losing money. The company ended 2007 with a revenue of NOK 21.7 million. In 2008, 24SevenOffice bought 50% of the stocks in telecommunication company Oyatel, partnered with Nets Group to facilitate invoicing for businesses, and telecommunications company Telipol chose 24SevenOffice's second-generation Internet platform for its 8,000 users. They announced an increase in revenues in Q2 to 11.1 million, up from 4.7 million in the same period the year before. 24SevenOffice had a turnover of NOK 37 million in the first half of 2009, a doubling compared to the same period the previous year, and presented its first positive EBITDA in Q2. The Norwegian Association of Auditors signed an agreement with 24SevenOffice in 2011, whereby they only recommend 24SevenOffice as a system for their members to use. On 27 June 2013, the shareholders of 24SevenOffice took off from the stock exchange and privatized the company. In recent years, the company has invested heavily in finance and accounting – and got leading auditing companies such as PwC and KPMG on the customer list. == Products == 24SevenOffice is a web-based (SaaS) ERP system. It includes modules for CRM, accounting, invoicing, e-mail, file/document management and project management. == Awards == 24SevenOffice won the Seal of Excellence in Multimedia Award at the 2004 CeBIT, became Norwegian Gazelle Company of the year 2004, chosen by Dagens Næringsliv and Dun & Bradstreet, won Product of the Year in the Norwegian finance magazine Kapital, and the IKT Grenland Innovation Award in 2008.

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