AI Avatar Video Generator With Voice

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  • Anna Becker

    Anna Becker

    Anna Becker is an Israeli researcher known in the field of artificial intelligence and computer science within the financial field. == Early life and education == Becker was born in Russia and immigrated to Israel at 16 after graduating from a school in Moscow. At 17, she began her studies at Technion – Israel Institute of Technology. During her master's degree in computer science, she taught first-year students of the same course, and at 27, Becker completed her PhD in Computer Science and Artificial Intelligence. == Career == While pursuing her PhD, Becker resolved an NP-complete approximation algorithm that had been unresolved for over twenty years. This made her a recognized scholar in the field. After completing her PhD, she developed an approximation technique by a factor of two. This technique is widely used today in operating systems, database systems, and VLSI chip designs. She then founded and sold Strategy Runner, a fintech software. After this, she founded EndoTech, an algorithmic trading platform based on artificial intelligence and machine learning. EndoTech's trading strategies have been operating in live cryptocurrency markets since 2017. The platform's BTC Alpha strategy has reported an average annual return of 163% on fixed capital over eight years of live operation, with a maximum drawdown of 14% and a trade accuracy rate of approximately 83%. In 2026, EndoTech entered a partnership with Bit1 Exchange to make its BTC Alpha and ETH Alpha copy trading strategies accessible to retail investors with no minimum deposit requirement, through a full-custody model in which user funds remain in their own exchange wallets at all times.As of 2023, Becker is working on Fianchetto Fund, an AI-based investing analysis platform. Becker has also co-authored a book on Bayesian networks, which has been published widely in the field of computer science and artificial intelligence.

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  • Timeline of operating systems

    Timeline of operating systems

    This article presents a timeline of events in the history of computer operating systems from 1951 to the current day. For a narrative explaining the overall developments, see the History of operating systems. == 20th Century == == 1940s == 1949 EDSAC was considered the first operating system developed by Maurice Wilkes and manufactured by the University of Cambridge == 1950s == 1951 LEO I 'Lyons Electronic Office' was the commercial development of EDSAC computing platform, supported by British firm J. Lyons and Co. 1953 DYSEAC - an early machine capable of distributing computing 1955 General Motors Operating System made for IBM 701 MIT's Tape Director operating system made for UNIVAC 1103 1956 GM-NAA I/O for IBM 704, based on General Motors Operating System 1957 Atlas Supervisor (Manchester University) (Atlas computer project start) BESYS (Bell Labs), for IBM 704, later IBM 7090 and IBM 7094 1958 University of Michigan Executive System (UMES), for IBM 704, 709, and 7090 1959 SHARE Operating System (SOS), based on GM-NAA I/O == 1960s == 1960 IBSYS (IBM for its 7090 and 7094) 1961 CTSS demonstration (MIT's Compatible Time-Sharing System for the IBM 7094) MCP (Burroughs Master Control Program) for B5000 1962 Atlas Supervisor (Manchester University) (Atlas computer commissioned) BBN Time-Sharing System GCOS (GE's General Comprehensive Operating System, originally GECOS, General Electric Comprehensive Operating Supervisor) 1963 ADMIRAL AN/FSQ-32, another early time-sharing system begun CTSS becomes operational (MIT's Compatible Time-Sharing System for the IBM 7094) JOSS, an interactive time-shared system that did not distinguish between operating system and language Titan Supervisor, early time-sharing system begun 1964 Berkeley Timesharing System (for Scientific Data Systems' SDS 940) Chippewa Operating System (for CDC 6600 supercomputer) Dartmouth Time-Sharing System (Dartmouth College's DTSS for GE computers) EXEC 8 (UNIVAC) KDF9 Timesharing Director (English Electric) – an early, fully hardware secured, fully pre-emptive process switching, multi-programming operating system for KDF9 (originally announced in 1960) OS/360 (IBM's primary OS for its S/360 series) (announced) PDP-6 Monitor (DEC) descendant renamed TOPS-10 in 1970 SCOPE (CDC 3000 series) 1965 BOS/360 (IBM's Basic Operating System) DECsys TOS/360 (IBM's Tape Operating System) Livermore Time Sharing System (LTSS) Multics (MIT, GE, Bell Labs for the GE-645) (announced) Pick operating system SIPROS 66 (Simultaneous Processing Operating System) THE multiprogramming system (Technische Hogeschool Eindhoven) development TSOS (later VMOS) (RCA) 1966 DOS/360 (IBM's Disk Operating System) GEORGE 1 & 2 for ICT 1900 series Mod 1 Mod 2 Mod 8 MS/8 (Richard F. Lary's DEC PDP-8 system) MSOS (Mass Storage Operating System) OS/360 (IBM's primary OS for its S/360 series) PCP and MFT (shipped) RAX Remote Users of Shared Hardware (RUSH), a time-sharing system developed by Allen-Babcock for the IBM 360/50 SODA for Elwro's Odra 1204 Universal Time-Sharing System (XDS Sigma series) 1967 CP-40, predecessor to CP-67 on modified IBM System/360 Model 40 CP-67 (IBM, also known as CP/CMS) Conversational Programming System (CPS), an IBM time-sharing system under OS/360 Michigan Terminal System (MTS) (time-sharing system for the IBM S/360-67 and successors) ITS (MIT's Incompatible Timesharing System for the DEC PDP-6 and PDP-10) OS/360 MVT ORVYL (Stanford University's time-sharing system for the IBM S/360-67) TSS/360 (IBM's Time-sharing System for the S/360-67, never officially released, canceled in 1969 and again in 1971) WAITS (SAIL, Stanford Artificial Intelligence Laboratory, time-sharing system for DEC PDP-6 and PDP-10, later TOPS-10) 1968 Airline Control Program (ACP) (IBM) B1 (NCR Century series) CALL/360, an IBM time-sharing system for System/360 HP Real-Time Executive (HP RTE) – Hewlett-Packard HP Time-Shared BASIC (HP TSB) – Hewlett-Packard (time-sharing system for the HP 2000) THE multiprogramming system (Eindhoven University of Technology) publication TSS/8 (DEC for the PDP-8) VP/CSS 1969 B2 (NCR Century series) B3 (NCR Century series) GEORGE 3 For ICL 1900 series MINIMOP Multics (MIT, GE, Bell Labs for the GE-645 and later the Honeywell 6180) (opened for paying customers in October) RC 4000 Multiprogramming System (RC) TENEX (Bolt, Beranek and Newman for DEC systems, later TOPS-20) Unics (later Unix) (AT&T, initially on DEC computers) Xerox Operating System == 1970s == 1970 DOS-11 (PDP-11) 1971 EMAS Kronos RSTS-11 2A-19 (First released version; PDP-11) RSX-15 OS/8 1972 B4 (NCR Century series) COS-300 Data General RDOS Edos MUSIC/SP OS/4 OS 1100 OS/2000 (Honeywell 2000-series) Operating System/Virtual Storage 1 (OS/VS1) Operating System/Virtual Storage 2 R1 (OS/VS2 SVS) PRIMOS (written in FORTRAN IV, that didn't have pointers, while later versions, around version 18, written in a version of PL/I, called PL/P) Virtual Machine/Basic System Extensions Program Product (BSEPP or VM/SE) Virtual Machine/System Extensions Program Product (SEPP or VM/BSE) Virtual Machine Facility/370 (VM/370), sometimes known as VM/CMS 1973 Эльбрус-1 (Elbrus-1) – Soviet computer – created using high-level language uЭль-76 (AL-76/ALGOL 68) Alto OS CP-V (Control Program V) RSX-11D RT-11 VME – implementation language S3 (ALGOL 68) 1974 ACOS-2 (NEC) ACOS-4 ACOS-6 CP/M DOS-11 V09-20C (Last stable release, June 1974) Hydra – capability-based, multiprocessing OS kernel MONECS Multi-Programming Executive (MPE) – Hewlett-Packard Operating System/Virtual Storage 2 R2 (MVS) OS/7 OS/16 OS/32 Sintran III 1975 BS2000 V2.0 (First released version) COS-350 ISIS NOS (Control Data Corporation) OS/3 (Univac) VS/9 (formerly RCA's TSOS, later named VMOS) Version 6 Unix XVM/DOS XVM/RSX 1976 Cambridge CAP computer – all operating system procedures written in ALGOL 68C, with some closely associated protected procedures in BCPL Cray Operating System DX10 FLEX TOPS-20 TX990/TXDS Tandem Nonstop OS v1 Thoth 1977 1BSD AMOS KERNAL OASIS operating system OS68 OS4000 RMX-80 System 88 (Exec) System Support Program (IBM System/34 and System/36) TRSDOS Virtual Memory System (VMS) V1.0 (Initial commercial release, October 25) VRX (Virtual Resource eXecutive) VS Virtual Memory Operating System 1978 2BSD Apple DOS Control Program Facility (IBM System/38) Cray Time Sharing System (CTSS) DPCX (IBM) DPPX (IBM) HDOS KSOS – secure OS design from Ford Aerospace KVM/370 – security retro-fit of IBM VM/370 Lisp machine (CADR) MVS/System Extensions (MVS/SE) OS4 (Naked Mini 4) PTDOS TRIPOS UCSD p-System (First released version) Z80-RIO 1979 Atari DOS 3BSD CP-6 Idris MP/M MVS/System Extensions R2 (MVS/SE2) NLTSS POS Sinclair BASIC Transaction Processing Facility (TPF) (IBM) UCLA Secure UNIX – an early secure UNIX OS based on security kernel UNIX/32V DOS/VSE Version 7 Unix == 1980s == 1980 86-DOS AOS/VS (Data General) Business Operating System CTOS DOSPLUS (TRS-80) MVS/System Product (MVS/SP) V1 NewDos/80 OS-9 RMX-86 RS-DOS SOS Virtual Machine/System Product (VM/SP) Xenix 1981 Acorn MOS Aegis SR1 (First Apollo/DOMAIN systems shipped on March 27) CP/M-86 DRX (Distributed Resource Executive) iMAX – OS for Intel's iAPX 432 capability machine MCS (Multi-user Control System) MS-DOS PC DOS Pilot (Xerox Star operating system) UNOS UTS V VERSAdos VRTX VSOS (Virtual Storage Operating System) Xinu first release 1982 Commodore DOS LDOS (By Logical Systems, Inc. – for the Radio Shack TRS-80 Models I, II & III) PCOS (Olivetti M20) pSOS QNX Stratus VOS Sun UNIX (later SunOS) 0.7 Ultrix Unix System III VAXELN 1983 Coherent DNIX EOS GNU (project start) Lisa Office System 7/7 LOCUS – UNIX compatible, high reliability, distributed OS MVS/System Product V2 (MVS/Extended Architecture, MVS/XA) Novell NetWare (S-Net) PERPOS ProDOS RTU (Real-Time Unix) STOP – TCSEC A1-class, secure OS for SCOMP hardware SunOS 1.0 VSE/System Package (VSE/SP) Version 1 1984 AMSDOS CTIX (Unix variant) DYNIX Mac OS (System 1.0) MSX-DOS NOS/VE PANOS PC/IX ROS Sinclair QDOS SINIX UNICOS Venix 2.0 Virtual Machine/Extended Architecture Migration Assistance (VM/XA MA) 1985 AmigaOS Atari TOS DG/UX DOS Plus Graphics Environment Manager Harmony MacOS 2 MIPS RISC/os Oberon – written in Oberon SunOS 2.0 Version 8 Unix Virtual Machine/Extended Architecture System Facility (VM/XA SF) Windows 1.0 Windows 1.01 Xenix 2.0 1986 AIX 1.0 Cronus distributed OS FlexOS GEMSOS – TCSEC A1-class, secure kernel for BLACKER VPN & GTNP GEOS Genera 7.0 HP-UX MacOS 3 SunOS 3.0 TR-DOS TRIX Version 9 Unix 1987 Arthur (much improved version came in 1989 under the name RISC OS) BS2000 V9.0 IRIX (3.0 is first SGI version) MacOS 4 MacOS 5 MDOS MINIX 1.0 OS/2 (1.0) PC-MOS/386 Topaz – semi-distributed OS for DEC Firefly workstation written in Modula-2+ and garbage collected VxWorks Windows 2.0 1988 A/UX (Apple Computer) AOS/VS II (Data General) CP/M rebranded as DR-DOS Flex machine – tagged, capability machine with OS and other software written

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  • Rider Spoke

    Rider Spoke

    Rider Spoke developed by Blast Theory in collaboration with the Mixed Reality Lab was first staged at the Barbican, London in October 2007. Created for cyclists, it combines elements of theatre, performance, game play and state of the art technology. Rider Spoke was built in the IPerG project on the EQUIP architecture. Rider Spoke has since been presented in Athens (2008), Brighton (2008), Budapest (2008), Sydney (2009, Adelaide (2009) and Liverpool (2010).

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  • WebGL

    WebGL

    WebGL (short for Web Graphics Library) is a JavaScript API for rendering interactive 2D and 3D graphics within any compatible web browser without the use of plug-ins. WebGL is fully integrated with other web standards, allowing GPU-accelerated usage of physics, image processing, and effects in the HTML canvas. WebGL elements can be mixed with other HTML elements and composited with other parts of the page or page background. WebGL programs consist of control code written in JavaScript, and shader code written in OpenGL ES Shading Language (GLSL ES, sometimes referred to as ESSL), a language similar to C or C++. WebGL code is executed on a computer's GPU. WebGL is designed and maintained by the non-profit Khronos Group. On February 9, 2022, Khronos Group announced WebGL 2.0 support from all major browsers. From 2024, a new graphics API, WebGPU, is being developed to supersede WebGL. WebGPU provides extended capabilities, a more modern interface, and direct GPU access, which is useful for demanding graphics as well as AI applications. == Design == WebGL 1.0 is based on OpenGL ES 2.0 and provides an API for 3D graphics. It uses the HTML5 canvas element and is accessed using Document Object Model (DOM) interfaces. WebGL 2.0 is based on OpenGL ES 3.0. It guarantees the availability of many optional extensions of WebGL 1.0, and exposes new APIs. Automatic memory management is provided implicitly by JavaScript. Like OpenGL ES 2.0, WebGL lacks the fixed-function APIs introduced in OpenGL 1.0 and deprecated in OpenGL 3.0. This functionality, if required, has to be implemented by the developer using shader code and JavaScript. Shaders in WebGL are written in GLSL and passed to the WebGL API as text strings. The WebGL implementation compiles these strings to GPU code. This code is executed for each vertex sent through the API and for each pixel rasterized to the screen. == History == WebGL evolved out of the Canvas 3D experiments started by Vladimir Vukićević at Mozilla. Vukićević first demonstrated a Canvas 3D prototype in 2006. By the end of 2007, both Mozilla and Opera had made their own separate implementations. In early 2009, the non-profit technology consortium Khronos Group started the WebGL Working Group, with initial participation from Apple, Google, Mozilla, Opera, and others. Version 1.0 of the WebGL specification was released March 2011. An early application of WebGL was Zygote Body. In November 2012 Autodesk announced that they ported most of their applications to the cloud running on local WebGL clients. These applications included Autodesk Fusion and AutoCAD. Development of the WebGL 2 specification started in 2013 and finished in January 2017. The specification is based on OpenGL ES 3.0. First implementations are in Firefox 51, Chrome 56 and Opera 43. == Implementations == === Almost Native Graphics Layer Engine === Almost Native Graphics Layer Engine (ANGLE) is an open source graphic engine which implements WebGL 1.0 (2.0 which closely conforms to ES 3.0) and OpenGL ES 2.0 and 3.0 standards. It is a default backend for both Google Chrome and Mozilla Firefox on Windows platforms and works by translating WebGL and OpenGL calls to available platform-specific APIs. ANGLE currently provides access to OpenGL ES 2.0 and 3.0 to desktop OpenGL, OpenGL ES, Direct3D 9, and Direct3D 11 APIs. ″[Google] Chrome uses ANGLE for all graphics rendering on Windows, including the accelerated Canvas2D implementation and the Native Client sandbox environment.″ == Software == WebGL is widely supported by modern browsers. However, its availability depends on other factors, too, like whether the GPU supports it. The official WebGL website offers a simple test page. More detailed information (like what renderer the browser uses, and what extensions are available) can be found at third-party websites. === Desktop browsers === Source: Google Chrome – WebGL 1.0 has been enabled on all platforms that have a capable graphics card with updated drivers since version 9, released in February 2011. By default on Windows, Chrome uses the ANGLE (Almost Native Graphics Layer Engine) renderer to translate OpenGL ES to Direct X 9.0c or 11.0, which have better driver support. However, on Linux and Mac OS X, the default renderer is OpenGL. It is also possible to force OpenGL as the renderer on Windows. Since September 2013, Chrome also has a newer Direct3D 11 renderer, which requires a newer graphics card. Chrome 56+ supports WebGL 2.0. Firefox – WebGL 1.0 has been enabled on all platforms that have a capable graphics card with updated drivers since version 4.0. Since 2013 Firefox also uses DirectX on the Windows platform via ANGLE. Firefox 51+ supports WebGL 2.0. Safari – Safari 6.0 and newer versions installed on OS X Mountain Lion, Mac OS X Lion and Safari 5.1 on Mac OS X Snow Leopard implemented support for WebGL 1.0, which was disabled by default before Safari 8.0. Safari version 12 (available in MacOS Mojave) has available support for WebGL 2.0 as an "Experimental" feature. Safari 15 enables WebGL 2.0 for all users. Opera – WebGL 1.0 has been implemented in Opera 11 and 12, but was disabled by default in 2014. Opera 43+ supports WebGL 2.0. Internet Explorer – WebGL 1.0 is partially supported in Internet Explorer 11. Internet Explorer initially failed most of the official WebGL conformance tests, but Microsoft later released several updates. The latest 0.94 WebGL engine currently passes ≈97% of Khronos tests. WebGL support can also be manually added to earlier versions of Internet Explorer using third-party plugins such as IEWebGL. Microsoft Edge – For Microsoft Edge Legacy, the initial stable release supports WebGL version 0.95 (context name: "experimental-webgl") with an open source GLSL to HLSL transpiler. Version 10240+ supports WebGL 1.0 as prefixed. Latest Chromium-based Edge supports WebGL 2.0. === Mobile browsers === Google Chrome – WebGL 1.0 is supported on Android as of Chrome 25. WebGL 2.0 is supported on Android as of Chrome 58. Chrome is used for the Android system webview as of Android 5. Firefox for mobile – WebGL 1.0 is available for Android devices since Firefox 4. Safari on iOS – WebGL 1.0 is available for mobile Safari in iOS 8. WebGL 2.0 is available for mobile Safari in iOS 15. Microsoft Edge – Prefixed WebGL 1.0 was available on Windows 10 Mobile.. Latest Chromium-based Edge supports WebGL 2.0. Opera Mobile – Opera Mobile 12 supports WebGL 1.0 (on Android only). Sailfish OS – WebGL 1.0 is supported in the default Sailfish browser. Tizen – WebGL 1.0 is supported == Tools and ecosystem == === Utilities === The low-level nature of the WebGL API, which provides little on its own to quickly create desirable 3D graphics, motivated the creation of higher-level libraries that abstract common operations (e.g. loading scene graphs and 3D objects in certain formats; applying linear transformations to shaders or view frustums). Some such libraries were ported to JavaScript from other languages. Examples of libraries that provide high-level features include A-Frame (VR), BabylonJS, PlayCanvas, three.js, OSG.JS, Google’s model-viewer and CopperLicht. Web3D also made a project called X3DOM to make X3D and VRML content run on WebGL. === Games === There has been an emergence of 2D and 3D game engines for WebGL, such as Unreal Engine 4 and Unity. The Stage3D/Flash-based Away3D high-level library also has a port to WebGL via TypeScript. A more light-weight utility library that provides just the vector and matrix math utilities for shaders is sylvester.js. It is sometimes used in conjunction with a WebGL specific extension called glUtils.js. There are also some 2D libraries built atop WebGL, like Cocos2d-x or Pixi.js, which were implemented this way for performance reasons in a move that parallels what happened with the Starling Framework over Stage3D in the Flash world. The WebGL-based 2D libraries fall back to HTML5 canvas when WebGL is not available. Removing the rendering bottleneck by giving almost direct access to the GPU has exposed performance limitations in the JavaScript implementations. Some were addressed by asm.js and WebAssembly (similarly, the introduction of Stage3D exposed performance problems within ActionScript, which were addressed by projects like CrossBridge). === Content creation === As with any other graphics API, creating content for WebGL scenes requires using a 3D content creation tool and exporting the scene to a format that is readable by the viewer or helper library. Desktop 3D authoring software such as Blender, Autodesk Maya or SimLab Composer can be used for this purpose. In particular, Blend4Web allows a WebGL scene to be authored entirely in Blender and exported to a browser with a single click, even as a standalone web page. There are also some WebGL-specific software such as CopperCube and the online WebGL-based editor Clara.io. Online platforms such as Sketchfab and Clara.io allow users to directly upload their 3D models

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  • C3D Toolkit

    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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  • Bare machine

    Bare machine

    In information technology, a bare machine (or bare-metal computer) is a computer which has no operating system. The software executed by a bare machine, commonly called a bare metal program or bare metal application, is designed to interact directly with hardware. Bare machines are widely used in embedded systems, particularly in cases where resources are limited or high performance is required. == Bare machine computing == Bare Machine Computing is a computing paradigm in which application software runs directly on a bare machine as a single, stand-alone executable, without an operating system or device drivers. The application software has direct access to hardware resources, and there is typically no distinction between user and kernel mode. It is self-managed software that boots, loads and runs without using any other software components. Bare metal programs are typically written in a close-to-hardware language such as C or assembly language. == Advantages == Typically, a bare-metal application will run faster, use less memory and be more power efficient than an equivalent program that relies on an operating system, due to the inherent overhead imposed by system calls. For example, hardware inputs and outputs are directly accessible to bare metal software, whereas they must usually be accessed through system calls when using an OS. It has no OS and therefore has no OS-related vulnerabilities. == Disadvantages == Bare metal applications typically require more effort to develop because operating system services such as memory management and task scheduling are not available. Debugging a bare-metal program may be complicated by factors such as: Lack of a standard output. The target machine may differ from the hardware used for program development (e.g., emulator, simulator). This forces setting up a way to load the bare-metal program onto the target (flashing), start the program execution and access the target resources. == Examples == === Early computers === Early computers, such as the PDP-11, allowed programmers to load a program, supplied in machine code, to RAM. The resulting operation of the program could be monitored by lights, and output derived from magnetic tape, print devices, or storage. Amdahl UTS's performance improves by 25% when run on bare metal without VM, the company said in 1986. === Embedded systems === Bare machine programming is a common practice in embedded systems, in which microcontrollers or microprocessors boot directly into monolithic, single-purpose software without loading an operating system. Such embedded software can vary in structure. For example, one such program paradigm, known as foreground-background or superloop architecture, consists of an infinite main loop in which each task is executed sequentially and must voluntarily return control back to the loop. The loop runs these cooperative background processes that are not time-critical, while interrupt service routines momentarily interrupt the loop to handle time-critical foreground tasks.

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  • Verge3D

    Verge3D

    Verge3D is a real-time renderer and a toolkit used for creating interactive 3D experiences running on websites. == Overview == Verge3D enables users to convert content from 3D modelling tools (Blender, 3ds Max, and Maya are currently supported) to view in a web browser. Verge3D was created by the same core group of software engineers that previously created the Blend4Web framework. == Features == Verge3D uses WebGL for rendering. It incorporates components of the Three.js library and exposes its API to application developers. Puzzles Application functionality can be added via JavaScript, either by writing code directly or by using Puzzles, Verge3D’s visual programming environment based on Google Blockly. Puzzles is aimed primarily at non-programmers allowing quick creation of interactive scenarios in a drag-and-drop fashion. App Manager and web publishing App Manager is a lightweight web-based tool for creating, managing and publishing Verge3D projects, running on top of the local development server. Verge3D Network service integrated in the App Manager allows for publishing Verge3D applications via Amazon S3 and EC2 cloud services. PBR For purposes of authoring materials, a glTF 2.0-compliant physically based rendering pipeline is offered alongside the standard shader-based approach. PBR textures can be authored using external texturing software such as Substance Painter for which Verge3D offers the corresponding export preset. Besides the glTF 2.0 model, Verge3D supports physical materials of 3ds Max and Maya (with Autodesk Arnold as reference), and Blender's real-time Eevee materials. glTF and DCC software integration Verge3D integrates directly with Blender, 3ds Max, and Maya, enabling users to create 3D geometry, materials, and animations inside the software, then export them in the JSON-based glTF format. The Sneak Peek feature allows for exporting and viewing scenes from the DCC tool environment. Facebook 3D posts For Facebook publishing, Verge3D offers a specific GLB export option. The exported GLB files are displayed and can be opened in the App Manager. Asset compression Exported files can optionally use LZMA compression, resulting in a reduction in file size of up to 6x. UI and website layouts Interface layouts, created using external WYSIWYG editors, can be linked with Puzzles to trigger changes to a 3D scene being rendered in the browser and vice versa. Animation Verge3D supports skeletal animation, including animation of bipeds and character rigs, and allows for animation of material parameters. Model parts can also be set up to be dragged by the user. Physics The physics module can be linked separately to enable collision detection, dynamically moving objects, support for characters and vehicles, springs, ropes and cloth simulation. As of version 2.11, simple physics simulations can be created and controlled without coding via Puzzles, the visual programming system used by Verge3D. AR/VR The 2.10 update added support for WebXR, an in-development open technology designed to enable virtual reality and augmented reality experiences to be displayed in web browsers. It works with both headsets with controllers, like the HTC Vive and Oculus Rift, and those without, like Google Cardboard. AR/VR experiences can enabled via Puzzles or JavaScript. == Workflow == Verge3D's workflow differs substantially from other mainstream WebGL frameworks. Development of a new Verge3D application is usually started from modeling, texturing and animating 3D objects. The models are assembled in the 3D authoring tool. The scene file is then used as a basis for a Verge3D project initialized from the App Manager. An interactive scenario is optionally added using the Puzzles editor. A Verge3D application can be previewed in the web browser at any development stage using the App Manager. The finished web application can be deployed on the Verge3D Network, on Facebook or on the user's website. == Notable uses == NASA's Jet Propulsion Laboratory used Verge3D to create an interactive 3D visualization of the Mars InSight lander. The web application allows for exploring and interacting with the real-time model of the spacecraft, with the possibility to move different parts and unfurl the solar panels. NASA's older interactive web application Experience Curiosity was ported to Verge3D from Blend4Web. The application makes it possible to operate the rover, control its cameras and the robotic arm and reproduces some of the prominent events of the Mars Science Laboratory mission. Route 66 Digital's Escape Room used Verge3D and Blender. This interactive short explores how users can navigate 3D spaces and interact with objects without the need for instruction.

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  • The Dodo (website)

    The Dodo (website)

    The Dodo is an American online publisher focused on animals. The website was launched in January 2014 by Izzie Lerer, the daughter of media executive Kenneth Lerer, and journalist Kerry Lauerman. The Dodo has become one of the most popular Facebook publishers, garnering 1 billion video views from the social network in November 2015. The Dodo is headquartered in New York, New York. == History == The company—named after the first recorded species that humans drove to extinction—was founded by Lerer out of "a personal passion for the subject manner". Lerer has a PhD in animal studies with a focus on animal ethics and human relationships from Columbia University, launching the website after noticing the viral success of animal videos online but seeing no one "really owned the space." The Dodo's editorial and video production staff unionized with the Writers Guild of America, East in April 2018.

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  • List & Label

    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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  • Blue check

    Blue check

    A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official

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  • Full30

    Full30

    Full30 was an American online video-sharing platform primarily dedicated to firearms and shooting sports-related content. The service was established in 2014 by Tim Harmsen and Mark Hammonds as a result of YouTube's increasing restrictions on gun-related videos. == History == After the 2018 Parkland high school shooting, many companies attempted to distance themselves from any association with the firearms industry. As a result, YouTube began demonetizing and sometimes outright deleting firearms-related videos, and in one case, popular YouTube poster Hickok45's channel was completely deleted but later restored. In response, Harmsen, who operates the Military Arms Channel on YouTube, decided to create his own video-hosting website to allow himself and other firearms content creators a platform free from such restrictions; he named the website Full30 — a reference to the popular 30-round STANAG magazine. In July 2020, site representatives announced the site had new ownership. By the end of 2022, the site began to be redirected to a series of other websites. By 2025, it was largely deactivated with the front page replaced by a form to be filled out to receive "updates", with no other explanation. == Contributors == Hickok45 Military Arms Channel Forgotten Weapons Bavarian Shooter Liberty Doll CloverTac

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  • Deluxe Media

    Deluxe Media

    Deluxe Media Inc., also known simply as Deluxe and formerly Deluxe Entertainment Services Group, Inc., is an American multinational multimedia and entertainment service provisions company owned by Platinum Equity, founded in 1915 by Hungarian-born American film producer William Fox and headquartered in Burbank, California. The company services multiple clients in the film, television, digital content and advertising industries across the globe, and has been recognized with 10 Academy Awards for scientific and technical achievements, including developments in CinemaScope pictures (as part of 20th Century Fox) and more recently for a process of creating archival separations from digital image data. == History == Deluxe began as a film processing laboratory established in 1915 by William Fox under the name De Luxe as part of his eponymous film conglomerate corporation in Fort Lee, New Jersey. In 1916, Fox Film Corporation opened its studio in Hollywood on 13 acres at Sunset and Western. The first Deluxe film laboratory on the west coast was built on the south side of the lot (Fernwood and Serrano), and the laboratory was moved to the new Fox studios building on Manhattan's west side in 1919, where it remained for over 40 years. The "business manager" (later president) of the laboratory was Alan E. Freedman, who guided the company into the 1960s. In 1927, Fox (Deluxe) received a patent for sound-on-film, the Fox Movietone system. In 1927, "Sunrise: A Song of Two Humans," an early Movietone film, opened. Fox Movietone News, ran weekly in theaters until 1963. During the Great Depression, Fox Film Corporation encountered financial difficulties. Among the actions taken to maintain liquidity, Fox sold the laboratories in 1932 to Freedman, who renamed the operation Deluxe. Under Freedman's leadership, Deluxe added two more plants in Chicago and Toronto. In January 1934, Fox was granted an option to rebuy DeLuxe before December 31, 1938. On 31 May 1935, under Sidney Kent, Fox merged his film company with Twentieth Century Pictures to form The Twentieth Century-Fox Film Corporation following a bank-infused reorganisation. The merged company then exercised this option in July 1936, with Freedman remaining as president. In 1953, Deluxe developed the widescreen format CinemaScope. Titles included "There's No Business Like Show Business" (1954) and "The Seven Year Itch" (1955). Other innovations included the processing and sound striping of CinemaScope, and were patented and/or received Academy awards. In 1962 Freedman retired. In the 1960s, Deluxe closed its New York plant, followed by its plants in Chicago and Toronto, as motion picture production declined on the East Coast. In 1972, Deluxe began large volume videocassette production, with a billion by 1996. In 1990, The Rank Organisation acquired Deluxe from Fox. In 2000, Deluxe began large volume DVD production. In 2006, The Rank Organisation sold Deluxe Film Group to MacAndrews & Forbes, renamed Deluxe Entertainment Services Group. On 9 February 2012, Deluxe acquired Hong Kong–based visual effects and post-production company, Centro Digital Pictures, with its founder John Chu remaining as president while reporting to Alaric McAusland, managing director for Deluxe in Australia. In May 2014, Deluxe shut down its Los Angeles plant at Sunset & Western Studios complex, where other studios themselves were demolished way back in 1971. Also that same year, Deluxe closed the Hollywood film labs, and they gave thousands of orphaned film elements to the Academy Film Archive. The Deluxe Laboratories Collection at the Academy Film Archive consists of over 7,500 35mm and 16mm film elements of various motion pictures dating back to the early 1960s. On 22 April 2015, Deluxe and its longtime competitor, Technicolor S.A., announced that they had entered into a binding agreement to create a new joint venture known as Deluxe Technicolor Digital Cinema which will specialize in cinema mastering, distribution and management services. Deluxe got acquired on 4 September 2019 by creditors in a debt-for-equity swap to avoid bankruptcy. On 3 October 2019, Deluxe filed for bankruptcy, pending in the Southern District of New York. The same month on the 24th, the company received court approval to emerge from bankruptcy with a comprehensive restructuring plan. On July 1, 2020, Platinum Equity agreed to acquire the distribution division of Deluxe and re-unite with former CEO Cyril Drabinsky who would merge CineVizion, a film distribution company he founded after leaving Deluxe in 2016, into it. The companies Company 3 and Method Studios which formed the creative divisions of Deluxe were sold to Framestore in November 2020.

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  • Scikit-learn

    Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project. == Overview == The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project derives from its role as a "scientific toolkit for machine learning", originally developed and distributed as a third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in Computer Science and Automation in Saclay, France, took leadership of the project and released the first public version of the library on February 1, 2010. In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries. In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub. At that time, the project had over 1,400 contributors and the documentation received 42 million visits in 2018. According to a 2022 Kaggle survey of nearly 24,000 respondents from 173 countries, scikit-learn was identified as the most widely used machine learning framework. == Features == Large catalogue of well-established machine learning algorithms and data pre-processing methods (i.e. feature engineering) Utility methods for common data-science tasks, such as splitting data into train and test sets, cross-validation and grid search Consistent way of running machine learning models (estimator.fit() and estimator.predict()), which libraries can implement Declarative way of structuring a data science process (the Pipeline), including data pre-processing and model fitting == Examples == Fitting a random forest classifier: == Implementation == scikit-learn is largely written in Python, and uses NumPy extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in Cython to improve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR. In such cases, extending these methods with Python may not be possible. scikit-learn integrates well with many other Python libraries, such as Matplotlib and plotly for plotting, NumPy for array vectorization, Pandas dataframes, SciPy, and many more. == History == scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010. The project released its first stable version, 1.0.0, on September 24, 2021. The release was the result of over 2,100 merged pull requests, approximately 800 of which were dedicated to improving documentation. Development continues to focus on bug fixes, efficiency and feature expansion. The latest version, 1.8, was released on December 10, 2025. This update introduced native Array API support, enabling the library to perform GPU computations by directly using PyTorch and CuPy arrays. This version also included bug fixes, improvements and new features, such as efficiency improvements to the fit time of linear models. == Applications == Scikit-learn is widely used across industries for a variety of machine learning tasks such as classification, regression, clustering, and model selection. The following are real-world applications of the library: === Finance and Insurance === AXA uses scikit-learn to speed up the compensation process for car accidents and to detect insurance fraud. Zopa, a peer-to-peer lending platform, employs scikit-learn for credit risk modelling, fraud detection, marketing segmentation, and loan pricing. BNP Paribas Cardif uses scikit-learn to improve the dispatching of incoming mail and manage internal model risk governance through pipelines that reduce operational and overfitting risks. J.P. Morgan reports broad usage of scikit-learn across the bank for classification tasks and predictive analytics in financial decision-making. === Retail and E-Commerce === Booking.com uses scikit-learn for hotel and destination recommendation systems, fraudulent reservation detection, and workforce scheduling for customer support agents. HowAboutWe uses it to predict user engagement and preferences on a dating platform. Lovely leverages the library to understand user behaviour and detect fraudulent activity on its platform. Data Publica uses it for customer segmentation based on the success of past partnerships. Otto Group integrates scikit-learn throughout its data science stack, particularly in logistics optimization and product recommendations. === Media, Marketing, and Social Platforms === Spotify applies scikit-learn in its recommendation systems. Betaworks uses the library for both recommendation systems (e.g., for Digg) and dynamic subspace clustering applied to weather forecasting data. PeerIndex used scikit-learn for missing data imputation, tweet classification, and community clustering in social media analytics. Bestofmedia Group employs it for spam detection and ad click prediction. Machinalis utilizes scikit-learn for click-through rate prediction and relational information extraction for content classification and advertising optimization. Change.org applies scikit-learn for targeted email outreach based on user behaviour. === Technology === AWeber uses scikit-learn to extract features from emails and build pipelines for managing large-scale email campaigns. Solido applies it to semiconductor design tasks such as rare-event estimation and worst-case verification using statistical learning. Evernote, Dataiku, and other tech companies employ scikit-learn in prototyping and production workflows due to its consistent API and integration with the Python ecosystem. === Academia === Télécom ParisTech integrates scikit-learn in hands-on coursework and assignments as part of its machine learning curriculum. == Awards == 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize: Awarded in recognition of scikit-learn's impact as a major free software breakthrough in machine learning and its role in the digital transformation of science and industry. 2022 Open Science Award for Open Source Research Software: Awarded by the French Ministry of Higher Education and Research as part of the second National Plan for Open Science. The project was recognized in the "Community" category for its technical quality, its large international contributor network, and the quality of its documentation.

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  • Kimchi (software)

    Kimchi (software)

    Kimchi is a web management tool to manage Kernel-based Virtual Machine (KVM) infrastructure. Developed with HTML5, Kimchi is developed to intuitively manage KVM guests, create storage pools, manage network interfaces (bridges, VLANs, NAT), and perform other related tasks. The name is an extended acronym for KVM infrastructure management. It is an Apache-licensed project hosted on GitHub, and incubated by oVirt.org.

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  • Digital history

    Digital history

    Digital history is the use of digital media to further historical analysis, presentation, and research. It is a branch of the digital humanities and an extension of quantitative history, cliometrics, and computing. Digital history is commonly known as digital public history, concerned primarily with engaging online audiences with historical content, or digital research methods, that further academic research. Digital history outputs include: digital archives, online presentations, data and information visualizations, interactive maps, timelines, audio files, and virtual worlds. These outputs are designed to enhance accessibility to users, facilitating engagement with historical content. Recent digital history projects focus on creativity, collaboration, and technical innovation, text mining, corpus linguistics, network analysis, 3D modeling, and big data analysis. By utilizing these resources, the user can rapidly develop new analyses that can link to, extend, and bring to life existing histories. == History == Rooted in earlier social science history work, particularly around the history of enslavement in the United States, early digital history in the 1960s and 70s focused on using computers to conduct quantitative analyses, primarily of demographic and social history data - censuses, election returns, city directories, and other tabular or countable data. - with the aim of producing defensible research findings These early computers could be programmed to conduct statistical analyses of these records, creating tallies, or seeking trends across records. This research into historical demography was rooted in the rise of social history as a field of historical interest. The historians involved in this work sought to quantify past societies, to come to new conclusions about communities and population. Computers proved capable tools for that type of work. By the late 1970s younger historians turned to cultural studies, most of these studies involved online databases that were checked by Professionals in Great Britain about once a year. The outpouring of quantitative studies by established scholars continued. Since then, quantitative history and cliometrics have been used primarily by historically minded economists and political scientists. In the late 1980s quantifiers founded the Association for History and Computing. This movement provided some of the impetus for the rise of digital history in the 1990s. The more recent roots of digital history were in software rather than online networks. In 1982, the Library of Congress embarked on its Optical Disk Pilot Project, which placed text and images from its collection on to laserdiscs and CD-ROMs. The library started offering online exhibits in 1992 when it launched Selected Civil War Photographs. In 1993, Roy Rosenzweig, along with Steve Brier and Josh Brown, produced their award-winning CD-ROM Who Built America? From the Centennial Exposition of 1876 to the Great War of 1914, designed for Apple, Inc. that integrated images, text, film and sound clips, displayed in a visual interface that supported a text narrative. Among the earliest online digital history projects were The Heritage Project of the University of Kansas, and medieval historian Dr. Lynn Nelson's World History Index and History Central Catalogue. Another was The Valley of the Shadow, conceived in 1991 by current University of Richmond professor of humanities and president emeritus, Edward L. Ayers, who was then at the University of Virginia. The Institute for Advanced Technology in the Humanities (IATH) at the University of Virginia adopted the Valley Project and partnered with IBM to collect and transcribe historical sources into digital files. The project collected data related to Augusta County in Virginia and Franklin County in Pennsylvania during the American Civil War. In 1996, William G. Thomas III joined Ayers on the Valley Project. Together, they produced an online article entitled "The Differences Slavery Made: A Close Analysis of Two American Communities," which also appeared in The American Historical Review in 2003. A CD-ROM also accompanied the Valley Project, published by W. W. Norton and Company in 2000. Rosenzweig, who died October 11, 2007, founded the Center for History and New Media (CHNM) at George Mason University in 1994. Today, CHNM boasts several digital tools available to historians, such as Zotero, Omeka or Tropy. In 1997, Ayers and Thomas used the term "digital history" when they proposed and founded the Virginia Center for Digital History (VCDH) at the University of Virginia, the earliest center devoted exclusively to history. Several other institutions promoting digital history include the Center for Humane Arts, Letters, and Social Sciences Online (MATRIX) at Michigan State University, Maryland's Institute for Technology in the Humanities, and the Center for Digital Research in the Humanities at the University of Nebraska. In 2004, Emory University launched Southern Spaces, a "peer-reviewed Internet journal and scholarly forum" examining the history of the South. == Applications == There are many potential benefits to the use of digital history when combined with traditional historical methods. Some of these applications include: Combining traditional historical methods and new research methods in order to come to new conclusions. Using different tools to extract and analyse larger amounts of data that would not be manageable otherwise. Create models and maps of data extracted to create a visualisation of the data. Data extracted and analysed can be placed alongside existing historiography to increase combined historical knowledge. By adding new research methods to existing historical method, historians can benefit greatly from the ability to work with larger amounts of data and develop new interpretations from this. == Notable Projects == The collaborative nature of most digital history endeavors has meant that the discipline has developed primarily at institutions with the resources to sponsor content research and technical innovation. Two of the first centers, George Mason University's Center for History and New Media and the Virginia Center for Digital History at the University of Virginia have been among the leaders in the development of digital history projects and the education of digital historians. Some of the noteworthy projects emerging from these pioneering centers are The Geography of Slavery, The Texas Slavery Project, and The Countryside Transformed at VCDH and Liberty, Equality, Fraternity: Exploring the French Revolution and The Lost Museum at the CHNM. In each of these projects, mediated archives holding multiple types of sources are combined with digital tools to analyze and illuminate an historical question to a varying degree; this integration of content and tools with analysis is one of the hallmarks of digital history—projects move beyond archives or collections and into scholarly analysis and the use of digital tools to develop that analysis. The differences between the ways projects incorporate these integrations are a measure of the development of the field and point to the ongoing debates over what digital history can and should be. While many of the projects at VCDH, CHNM, and other university's centers have been geared towards academics and post-secondary education, the University of Victoria (British Columbia), in conjunction with the Université de Sherbrooke and the Ontario Institute for Studies in Education at the University of Toronto, has created as series of projects for all ages, "Great Unsolved Mysteries in Canadian History." Laden with instructional aids, this site asks teachers to introduce students to historical research methods to help them develop analytical skills and a sense of the complexities of their national history. Issues of race, religion, and gender are addressed in carefully constructed modules that cover incidents in Canadian history from Viking exploration through the 1920s. One of the original co-creators of the project, John Lutz has also developed Victoria's Victoria with the University of Victoria and Malaspina University-College. In addition to Ayers, Thomas, Lutz, and Rosenzweig, numerous other individual scholars work with digital history techniques and have made and/or continue to make important contributions to the field. Robert Darnton's 2000 article, "An Early Information Society: News and the Media in Eighteenth-Century Paris" was supplemented with electronic resources and is an early model of the discussions around digital history and its future in the humanities. One of the first major digital projects to be reviewed by the American Historical Review (AHR) was Philip Ethington's "Los Angeles and the Problem of Urban Historical Knowledge"—a multimedia exploration of changes to Los Angeles' physical profile over the course of several decades. In this essay, he also expresses his beliefs that historians have major power in

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