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
80 Million Tiny Images
80 Million Tiny Images is a dataset intended for training machine-learning systems constructed by Antonio Torralba, Rob Fergus, and William T. Freeman in a collaboration between MIT and New York University. It was published in 2008. The dataset has size 760 GB. It contains 79,302,017 32×32-pixel color images, scaled down from images scraped from the World Wide Web over 8 months. The images are classified into 75,062 classes. Each class is a non-abstract noun in WordNet. Images may appear in more than one class. The dataset was motivated by non-parametric models of neural activations in the visual cortex upon seeing images. The CIFAR-10 dataset uses a subset of the images in this dataset, but with independently generated labels, as the original labels were not reliable. The CIFAR-10 set has 6000 examples of each of 10 classes, and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. == Construction == It was first reported in a technical report in April 2007, during the middle of the construction process, when there were only 73 million images. The full dataset was published in 2008. They began with all 75,846 non-abstract nouns in WordNet, and then for each of these nouns, they scraped 7 image search engines: Altavista, Ask.com, Flickr, Cydral, Google, Picsearch, and Webshots. After 8 months of scraping, they obtained 97,245,098 images. Since they did not have enough storage, they downsized the images to 32×32 as they were scraped. After gathering, they removed images with zero variance and intra-word duplicate images, resulting in the final dataset. Out of the 75,846 nouns, only 75,062 classes had any results, so the other nouns did not appear in the final dataset. The number of images per noun follows a Zipf-like distribution, with 1056 images per noun on average. To prevent a few nouns taking up too many images, they put an upper bound of at most 3000 images per noun. == Retirement == The 80 Million Tiny Images dataset was retired from use by its creators in 2020, after a paper by researchers Abeba Birhane and Vinay Prabhu found that some of the labeling of several publicly available image datasets, including 80 Million Tiny Images, contained racist and misogynistic slurs which were causing models trained on them to exhibit racial and sexual bias. The dataset also contained offensive images. Following the release of the paper, the dataset's creators removed the dataset from distribution, and requested that other researchers not use it for further research and to delete their copies of the dataset.
ImageNet
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. ImageNet contains more than 20,000 categories, with a typical category, such as "balloon" or "strawberry", consisting of several hundred images. The database of annotations of third-party image URLs is freely available directly from ImageNet, though the actual images are not owned by ImageNet. Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), where software programs compete to correctly classify and detect objects and scenes. The challenge uses a "trimmed" list of one thousand non-overlapping classes. == History == AI researcher Fei-Fei Li began working on the idea for ImageNet in 2006. At a time when most AI research focused on models and algorithms, Li wanted to expand and improve the data available to train AI algorithms. In 2007, Li met with Princeton professor Christiane Fellbaum, one of the creators of WordNet, to discuss the project. As a result of this meeting, Li went on to build ImageNet starting from the roughly 22,000 nouns of WordNet and using many of its features. She was also inspired by a 1987 estimate that the average person recognizes roughly 30,000 different kinds of objects. As an assistant professor at Princeton, Li assembled a team of researchers to work on the ImageNet project. They used Amazon Mechanical Turk to help with the classification of images. Labeling started in July 2008 and ended in April 2010. It took 49K workers from 167 countries filtering and labeling over 160M candidate images. They had enough budget to have each of the 14 million images labelled three times. The original plan called for 10,000 images per category, for 40,000 categories at 400 million images, each verified 3 times. They found that humans can classify at most 2 images/sec. At this rate, it was estimated to take 19 human-years of labor (without rest). They presented their database for the first time as a poster at the 2009 Conference on Computer Vision and Pattern Recognition (CVPR) in Florida, titled "ImageNet: A Preview of a Large-scale Hierarchical Dataset". The poster was reused at Vision Sciences Society 2009. In 2009, Alex Berg suggested adding object localization as a task. Li approached PASCAL Visual Object Classes contest in 2009 for a collaboration. It resulted in the subsequent ImageNet Large Scale Visual Recognition Challenge starting in 2010, which has 1000 classes and object localization, as compared to PASCAL VOC which had just 20 classes and 19,737 images (in 2010). === Significance for deep learning === On 30 September 2012, a convolutional neural network (CNN) called AlexNet achieved a top-5 error of 15.3% in the ImageNet 2012 Challenge, more than 10.8 percentage points lower than that of the runner-up. Using convolutional neural networks was feasible due to the use of graphics processing units (GPUs) during training, an essential ingredient of the deep learning revolution. According to The Economist, "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole." In 2015, AlexNet was outperformed by Microsoft's very deep CNN with over 100 layers, which won the ImageNet 2015 contest, having 3.57% error on the test set. Andrej Karpathy estimated in 2014 that with concentrated effort, he could reach 5.1% error rate, and ~10 people from his lab reached ~12-13% with less effort. It was estimated that with maximal effort, a human could reach 2.4%. == Dataset == ImageNet crowdsources its annotation process. Image-level annotations indicate the presence or absence of an object class in an image, such as "there are tigers in this image" or "there are no tigers in this image". Object-level annotations provide a bounding box around the (visible part of the) indicated object. ImageNet uses a variant of the broad WordNet schema to categorize objects, augmented with 120 categories of dog breeds to showcase fine-grained classification. In 2012, ImageNet was the world's largest academic user of Mechanical Turk. The average worker identified 50 images per minute. The original plan of the full ImageNet would have roughly 50M clean, diverse and full resolution images spread over approximately 50K synsets. This was not achieved. The summary statistics given on April 30, 2010: Total number of non-empty synsets: 21841 Total number of images: 14,197,122 Number of images with bounding box annotations: 1,034,908 Number of synsets with SIFT features: 1000 Number of images with SIFT features: 1.2 million === Categories === The categories of ImageNet were filtered from the WordNet concepts. Each concept, since it can contain multiple synonyms (for example, "kitty" and "young cat"), so each concept is called a "synonym set" or "synset". There were more than 100,000 synsets in WordNet 3.0, majority of them are nouns (80,000+). The ImageNet dataset filtered these to 21,841 synsets that are countable nouns that can be visually illustrated. Each synset in WordNet 3.0 has a "WordNet ID" (wnid), which is a concatenation of part of speech and an "offset" (a unique identifying number). Every wnid starts with "n" because ImageNet only includes nouns. For example, the wnid of synset "dog, domestic dog, Canis familiaris" is "n02084071". The categories in ImageNet fall into 9 levels, from level 1 (such as "mammal") to level 9 (such as "German shepherd"). === Image format === The images were scraped from online image search (Google, Picsearch, MSN, Yahoo, Flickr, etc) using synonyms in multiple languages. For example: German shepherd, German police dog, German shepherd dog, Alsatian, ovejero alemán, pastore tedesco, 德国牧羊犬. ImageNet consists of images in RGB format with varying resolutions. For example, in ImageNet 2012, "fish" category, the resolution ranges from 4288 x 2848 to 75 x 56. In machine learning, these are typically preprocessed into a standard constant resolution, and whitened, before further processing by neural networks. For example, in PyTorch, ImageNet images are by default normalized by dividing the pixel values so that they fall between 0 and 1, then subtracting by [0.485, 0.456, 0.406], then dividing by [0.229, 0.224, 0.225]. These are the mean and standard deviations for ImageNet, so this whitens the input data. === Labels and annotations === Each image is labelled with exactly one wnid. Dense SIFT features (raw SIFT descriptors, quantized codewords, and coordinates of each descriptor/codeword) for ImageNet-1K were available for download, designed for bag of visual words. The bounding boxes of objects were available for about 3000 popular synsets with on average 150 images in each synset. Furthermore, some images have attributes. They released 25 attributes for ~400 popular synsets: Color: black, blue, brown, gray, green, orange, pink, red, violet, white, yellow Pattern: spotted, striped Shape: long, round, rectangular, square Texture: furry, smooth, rough, shiny, metallic, vegetation, wooden, wet === ImageNet-21K === The full original dataset is referred to as ImageNet-21K. ImageNet-21k contains 14,197,122 images divided into 21,841 classes. Some papers round this up and name it ImageNet-22k. The full ImageNet-21k was released in Fall of 2011, as fall11_whole.tar. There is no official train-validation-test split for ImageNet-21k. Some classes contain only 1-10 samples, while others contain thousands. === ImageNet-1K === There are various subsets of the ImageNet dataset used in various context, sometimes referred to as "versions". One of the most highly used subsets of ImageNet is the "ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012–2017 image classification and localization dataset". This is also referred to in the research literature as ImageNet-1K or ILSVRC2017, reflecting the original ILSVRC challenge that involved 1,000 classes. ImageNet-1K contains 1,281,167 training images, 50,000 validation images and 100,000 test images. Each category in ImageNet-1K is a leaf category, meaning that there are no child nodes below it, unlike ImageNet-21K. For example, in ImageNet-21K, there are some images categorized as simply "mammal", whereas in ImageNet-1K, there are only images categorized as things like "German shepherd", since there are no child-words below "German shepherd". === Later developments === In the WordNet they built ImageNet on, there were 2832 synsets in the "person" subtree. During 2018--2020 period, they removed the download of the ImageNet-21k as they went through extensive filtering in these person synsets. Out of these 2832 synsets, 1593 were deemed "potentially offensive". Out of the remaining 1239, 1081 were deemed not really "visual". The result was that only 158 syn
ISO/IEC JTC 1/SC 24
ISO/IEC JTC 1/SC 24 Computer graphics, image processing and environmental data representation is a standardization subcommittee of the joint subcommittee ISO/IEC JTC 1 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of computer graphics, image processing, and environmental data representation. The international secretariat of ISO/IEC JTC 1/SC 24 is the British Standards Institute (BSI) located in the United Kingdom. == History == ISO/IEC JTC 1/SC 24 was formed in 1987 from ISO/TC 97 as a result of Resolution 21 at the ISO/IEC JTC 1 plenary. The group's origins began in computer graphics, the standardization of which was originally under ISO/IEC JTC 1/SC 21/WG 2. However, when ISO/IEC JTC 1/SC 24 was created, the standardization activity of ISO/IEC JTC 1/SC 21/WG 2 was carried over to the new subcommittee. The initial five working groups of ISO/IEC JTC 1/SC 24 were titled, “Architecture,” “Application programming interfaces,” “Metafiles and interfaces,” “Language bindings,” and “Validation, testing and registration.” The work of ISO/IEC JTC 1/SC 24 began with the Graphical Kernel System (GKS), which was adopted from ISO/IEC JTC 1/SC 21/WG 2. However, since GKS only addressed 2D functionality, attention turned to the standardization of 3D functionality. This resulted in two standards being published: GKS-3D in 1988 and PHIGS in 1989, both of which addressed 3D functionality. Since 1991, ISO/IEC JTC 1/SC 24 has held plenaries in a number of countries, including the Netherlands, Germany, United States, France, Canada, Japan, Sweden, Korea, United Kingdom, Australia, and Czech Republic. == Scope == The scope of ISO/IEC JTC 1/SC 24 is the “Standardization of interfaces for information technology based applications relating to”: Computer graphics Image processing Environmental data representation Support for the Mixed and Augmented Reality (MAR) Interaction with, and visual representation of, information Included are the following related areas: Modeling and simulation and related reference models Virtual reality with accompanying augmented reality/augmented virtuality aspects and related reference models Application program interfaces Functional specifications Representation models Interchange formats, encodings and their specifications, including metafiles Device interfaces Testing methods Registration procedures Presentation and support for creation of multimedia, hypermedia, and mixed reality documents Excluded are the following areas: Character and image coding Coding of multimedia, hypermedia, and mixed reality document interchange formats JTC 1 work in user system interfaces and document presentation ISO/TC 207 work on ISO 14000 environment management, ISO/TC 211 work on geographic information and geomatics Software environments as described by ISO/IEC JTC 1/SC 22 == Structure == ISO/IEC JTC 1/SC 24 is made up of four active working groups, each of which carries out specific tasks in standards development within the field of computer graphics, image processing and environmental data representation, together with ITU-T Study Group 16. As a response to changing standardization needs, working groups of ISO/IEC JTC 1/SC 24 can be disbanded if their area of work is no longer applicable, or established if new working areas arise. The focus of each working group is described in the group's terms of reference. Active working groups of ISO/IEC JTC 1/SC 24 are: == Collaborations == ISO/IEC JTC 1/SC 24 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC, in order to avoid conflicting or duplicative work. Organizations internal to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: ISO/IEC JTC 1/WG 7, Sensor Networks ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 32, Data management and interchange ISO/TAG 14, Imagery and technology ISO/TC 130, Graphic Technology ISO/TC 184/SC 4, Industrial data ISO/TC 211, Geographic information/Geomatics ISO/TC 215, Health informatics IEC TC 100, Audio, video and multimedia system and equipment Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: Defence Geospatial Information Working Group (DGIWG) Digital Imaging and Communications in Medicine (DICOM) International Hydrographic Organization (IHO) The Khronos Group NATO - Joint Intelligence Surveillance and Reconnaissance Capability Group (JISRCG) OMG Robotics DTF Open CGM Open Geospatial Consortium (OGC) SEDRIS Organization Simulation Interoperability Standards Organization (SISO) US National Imagery Transmission Format Standard (NITFS) Technical Board (US NTB) Web3D Consortium World Intellectual Property Organization (WIPO) World Wide Web Consortium (W3C) == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 11 "P" (participating) members of ISO/IEC JTC 1/SC 24 are: Australia, China, Egypt, France, India, Japan, Republic of Korea, Portugal, Russian Federation, United Kingdom, and United States. The 22 "O" (observer) members of ISO/IEC JTC 1/SC 24 are: Argentina, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Cuba, Czech Republic, Finland, Ghana, Hungary, Iceland, Indonesia, Islamic Republic of Iran, Italy, Kazakhstan, Malaysia, Poland, Romania, Serbia, Slovakia, Switzerland, and Thailand. == Published standards == ISO/IEC JTC 1/SC 24 currently has 80 published standards under their direct responsibility within the field of computer graphics, image processing, and environmental data representation, including:
Shader lamps
Shader lamps is a computer graphic technique used to change the appearance of physical objects. The still or moving objects are illuminated, using one or more video projectors, by static or animated texture or video stream. The method was invented at University of North Carolina at Chapel Hill by Ramesh Raskar, Greg Welch, Kok-lim Low and Deepak Bandyopadhyay in 1999 [1] as a follow on to Spatial Augmented Reality [2] also invented at University of North Carolina at Chapel Hill in 1998 by Ramesh Raskar, Greg Welch and Henry Fuchs. A 3D graphic rendering software is typically used to compute the deformation caused by the non perpendicular, non-planar or even complex projection surface. Complex objects (or aggregation of multiple simple objects) create self shadows that must be compensated by using several projectors. The objects are typically replaced by neutral color ones, the projection giving all its visual properties, thus the name shader lamps. The technique can be used to create a sense of invisibility, by rendering transparency. The object is illuminated not by a replacement of its own visual properties, but by the corresponding visual surface placed behind the object as seen from an arbitrary viewing point.
Microsoft Forms
Microsoft Forms (formerly Office 365 Forms) is an online survey creator, part of Microsoft 365. == Usage == Forms allows users to create surveys and quizzes with automatic marking. The data can be exported to Microsoft Excel, Power BI dashboards and viewed live using the Present feature. == Phishing and fraud == Due to a wave of phishing attacks utilizing Microsoft 365 in early 2021, Microsoft uses algorithms to automatically detect and block phishing attempts with Microsoft Forms. Also, Microsoft advises Forms users not to submit personal information, such as passwords, in a form or survey. It also place a similar advisory underneath the “Submit” button in every form created with Forms, warning users not to give out their password.
Thunderspy
Thunderspy is a type of security vulnerability, based on the Intel Thunderbolt 3 port, first reported publicly on 10 May 2020, that can result in an evil maid (i.e., attacker of an unattended device) attack gaining full access to a computer's information in about five minutes, and may affect millions of Apple, Linux and Windows computers, as well as any computers manufactured before 2019, and some after that. According to Björn Ruytenberg, the discoverer of the vulnerability, "All the evil maid needs to do is unscrew the backplate, attach a device momentarily, reprogram the firmware, reattach the backplate, and the evil maid gets full access to the laptop. All of this can be done in under five minutes." The malicious firmware is used to clone device identities which makes classical DMA attack possible. == History == The Thunderspy security vulnerabilities were first publicly reported by Björn Ruytenberg of Eindhoven University of Technology in the Netherlands on 10 May 2020. Thunderspy is similar to Thunderclap, another security vulnerability, reported in 2019, that also involves access to computer files through the Thunderbolt port. == Impact == The security vulnerability affects millions of Apple, Linux and Windows computers, as well as all computers manufactured before 2019, and some after that. However, this impact is restricted mainly to how precise a bad actor would have to be to execute the attack. Physical access to a machine with a vulnerable Thunderbolt controller is necessary, as well as a writable ROM chip for the Thunderbolt controller's firmware. Additionally, part of Thunderspy, specifically the portion involving re-writing the firmware of the controller, requires the device to be in sleep, or at least in some sort of powered-on state, to be effective. Machines that force power-off when the case is open may assist in resisting this attack to the extent that the feature (switch) itself resists tampering. Due to the nature of attacks that require extended physical access to hardware, it's unlikely the attack will affect users outside of a business or government environment. == Mitigation == The researchers claim there is no easy software solution, and may only be mitigated by disabling the Thunderbolt port altogether. However, the impacts of this attack (reading kernel level memory without the machine needing to be powered off) are largely mitigated by anti-intrusion features provided by many business machines. Intel claims enabling such features would substantially restrict the effectiveness of the attack. Microsoft's official security recommendations recommend disabling sleep mode while using BitLocker. Using hibernation in place of sleep mode turns the device off, mitigating potential risks of attack on encrypted data.