AI Assistant Unblocked

AI Assistant Unblocked — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  JDoodle

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

  Read more →
• 

  Period-tracking app

  Period-tracking apps are mobile applications used to track the menstrual cycle. They may be used to predict menstruation, to plan fertility, and to track health. Examples include Clue, Glow, and Flo. == Function == Users enter their dates of menstruation, and frequently other experiences such as vaginal discharge and spotting; premenstrual syndrome; changes in mood; menstrual cramps and other pain; and other symptoms such as appetite changes, bloating, and acne. The apps predict the date of users' next period, and often also their ovulation and fertile window. Some apps have additional features such as contraceptive reminders, educational content, tracking modes for use during pregnancy, or the ability to share one's menstrual cycle data with a partner. == Privacy == Period-tracking apps collect personal health data, potentially raising concerns about privacy. Researchers have warned that data may be transferred to third parties and used for consumer profiling and targeted advertising, used for employment and health insurance discrimination, or used to prosecute users for seeking abortions. After the 2022 decision by the United States Supreme Court to overturn Roe v. Wade, and the bans and restrictions on abortion in many US states that followed, many American women uninstalled the apps amidst fear that the data could be accessed by law enforcement and used to prosecute users. WIRED published a ranking of several period-tracking apps by data privacy.

  Read more →
• 

  User-defined function

  A user-defined function (UDF) is a function provided by the user of a program or environment, in a context where the usual assumption is that functions are built into the program or environment. UDFs are usually written for the requirement of its creator. == BASIC language == In some old implementations of the BASIC programming language, user-defined functions are defined using the "DEF FN" syntax. More modern dialects of BASIC are influenced by the structured programming paradigm, where most or all of the code is written as user-defined functions or procedures, and the concept becomes practically redundant. == COBOL language == In the COBOL programming language, a user-defined function is an entity that is defined by the user by specifying a FUNCTION-ID paragraph. A user-defined function must return a value by specifying the RETURNING phrase of the procedure division header and they are invoked using the function-identifier syntax. See the ISO/IEC 1989:2014 Programming Language COBOL standard for details. As of May 2022, the IBM Enterprise COBOL for z/OS 6.4 (IBM COBOL) compiler contains support for user-defined functions. == Databases == In relational database management systems, a user-defined function provides a mechanism for extending the functionality of the database server by adding a function, that can be evaluated in standard query language (usually SQL) statements. The SQL standard distinguishes between scalar and table functions. A scalar function returns only a single value (or NULL), whereas a table function returns a (relational) table comprising zero or more rows, each row with one or more columns. User-defined functions in SQL are declared using the CREATE FUNCTION statement. For example, a user-defined function that converts Celsius to Fahrenheit (a temperature scale used in USA) might be declared like this: Once created, a user-defined function may be used in expressions in SQL statements. For example, it can be invoked where most other intrinsic functions are allowed. This also includes SELECT statements, where the function can be used against data stored in tables in the database. Conceptually, the function is evaluated once per row in such usage. For example, assume a table named Elements, with a row for each known chemical element. The table has a column named BoilingPoint for the boiling point of that element, in Celsius. The query would retrieve the name and the boiling point from each row. It invokes the CtoF user-defined function as declared above in order to convert the value in the column to a value in Fahrenheit. Each user-defined function carries certain properties or characteristics. The SQL standard defines the following properties: Language - defines the programming language in which the user-defined function is implemented; examples include SQL, C, C# and Java. Parameter style - defines the conventions that are used to pass the function parameters and results between the implementation of the function and the database system (only applicable if language is not SQL). Specific name - a name for the function that is unique within the database. Note that the function name does not have to be unique, considering overloaded functions. Some SQL implementations require that function names are unique within a database, and overloaded functions are not allowed. Determinism - specifies whether the function is deterministic or not. The determinism characteristic has an influence on the query optimizer when compiling a SQL statement. SQL-data access - tells the database management system whether the function contains no SQL statements (NO SQL), contains SQL statements but does not access any tables or views (CONTAINS SQL), reads data from tables or views (READS SQL DATA), or actually modifies data in the database (MODIFIES SQL DATA). User-defined functions should not be confused with stored procedures. Stored procedures allow the user to group a set of SQL commands. A procedure can accept parameters and execute its SQL statements depending on those parameters. A procedure is not an expression and, thus, cannot be used like user-defined functions. Some database management systems allow the creation of user defined functions in languages other than SQL. Microsoft SQL Server, for example, allows the user to use .NET languages including C# for this purpose. DB2 and Oracle support user-defined functions written in C or Java programming languages. === SQL Server 2000 === There are three types of UDF in Microsoft SQL Server 2000: scalar functions, inline table-valued functions, and multistatement table-valued functions. Scalar functions return a single data value (not a table) with RETURNS clause. Scalar functions can use all scalar data types, with exception of timestamp and user-defined data types. Inline table-valued functions return the result set of a single SELECT statement. Multistatement table-valued functions return a table, which was built with many TRANSACT-SQL statements. User-defined functions can be invoked from a query like built‑in functions such as OBJECT_ID, LEN, DATEDIFF, or can be executed through an EXECUTE statement like stored procedures. Performance Notes: User-defined functions are subroutines made of one or more Transact-SQL statements that can be used to encapsulate code for reuse. It takes zero or more arguments and evaluates a return value. Has both control-flow and DML statements in its body similar to stored procedures. Does not allow changes to any Global Session State, like modifications to database or external resource, such as a file or network. Does not support output parameter. DEFAULT keyword must be specified to pass the default value of parameter. Errors in UDF cause UDF to abort which, in turn, aborts the statement that invoked the UDF. === Apache Hive === Apache Hive defines, in addition to the regular user-defined functions (UDF), also user-defined aggregate functions (UDAF) and table-generating functions (UDTF). Hive enables developers to create their own custom functions with Java. === Apache Doris === Apache Doris, an open-source real-time analytical database, allows external users to contribute their own UDFs written in C++ to it.

  Read more →
• 

  Path tracing

  Path tracing is a rendering algorithm in computer graphics that simulates how light interacts with objects and participating media to generate realistic (physically plausible) images. It is based on earlier, more limited, ray tracing algorithms. Path tracing is used to create photorealistic images for artistic purposes, and for applications such as architectural rendering and product design. It is also used to render frames for animated films, and visual effects for film and television. Because it can be very accurate and unbiased, it is commonly used to generate reference images when testing the quality of other rendering algorithms. The technique uses the Monte Carlo method to compute estimates of global illumination and simulate the ways different materials reflect (or scatter), transmit, absorb, and emit light. It can incorporate simple modeling of the effects of aperture and lens (depth of field, and bokeh) and shutter speed (motion blur), or more realistic simulation of the optical components in a camera. The algorithm works by describing illumination in a scene using the rendering equation, or light transport equation, and finding an approximate solution using Monte Carlo integration. An inefficient (but accurate) version of the algorithm can be very simple, and involves tracing a ray from the camera, allowing this ray to bounce in random directions as it hits different objects in the scene, and computing the amount of light transmitted along the path to the camera whenever the path encounters a light source. This process is repeated many times for each pixel (each repetition, with generated path and transmitted light, is called a sample), and the results are averaged. One main difference between this algorithm and standard ray tracing is that a single unbranching path is traced each time, while "Whitted-style" or "Cook-style" ray tracing recursively samples branching paths (e.g. when light is both reflected and refracted by a glass object). More practical versions incorporate improvements such as quasi-Monte Carlo methods (techniques that distribute samples more evenly), importance sampling (take more samples of paths that are likely to transport more light), and next event estimation (allow a very limited form of branching, and sample additional paths that connect to the lights more directly). Because path tracing uses random samples there is noise in the final image, which decreases as more samples are taken. Images commonly require many thousands of samples per pixel (spp) to reduce noise to an acceptable level, and denoising techniques (e.g. based on neural networks) are often used. Denoising is usually necessary when path tracing is used for real-time rendering in video games, because relatively few samples can be taken. Many alternative algorithms for path tracing have been developed, although they do not always outperform more straightforward implementations. These include bidirectional path tracing (which traces paths forwards from the light source as well as backwards from the camera), Metropolis light transport, and ways of combining path tracing with photon mapping. Video games often use biased versions of path tracing to improve performance (e.g. limiting the number of bounces in each path). A family of techniques called ReSTIR has been developed that can help real-time path tracing by sharing data between nearby pixels and consecutive frames. == History == Like all ray tracing methods, path tracing is based on ray casting, which Arthur Appel used for computer graphics rendering in the late 1960s. In 1980, John Turner Whitted published a recursive ray tracing algorithm that allows rendering images of scenes containing mirrored surfaces and refractive transparent objects. In 1984, Cook et al. described a form of ray tracing called distributed ray tracing, which uses Monte Carlo integration to render effects such as depth of field, motion blur, reflection from rough surfaces, and area lights. The same year, the radiosity method (not a ray tracing method) was published, which was the first physically based method for rendering diffuse global illumination. In 1986, Jim Kajiya published a paper exploring how to use distributed ray tracing to render physically-based global illumination, and this paper also introduced and named the method called "path tracing". Path tracing and other distributed ray tracing techniques were further refined in the late 1980s and early 1990s by researchers such as James Arvo and Peter Shirley, and by Greg Ward in the open source Radiance software. Despite being theoretically able to render any lighting, the original form of path tracing can sometimes be very inefficient (or noisy) for rendering light that is reflected or refracted before illuminating a visible surface, including diffuse global illumination where light enters an area through narrow gaps, because it traces paths only from the camera. To address this, variations of path tracing that trace paths from both the camera and from light sources, called bidirectional path tracing, were published in 1993 by Eric Lafortune and Yves Willems, and in 1997 by Eric Veach and Leonidas Guibas. In 1997 Veach and Guibas also published an alternative method called Metropolis light transport, which combines bidirectional path tracing with the Metropolis method. Veach's lengthy Ph.D. dissertation described both techniques, along with the theoretical background of path tracing; later, the book Physically Based Rendering (which won an Academy Award for Technical Achievement in 2014) helped to make information about path tracing more widely available. Path tracing requires tracing a large number of paths of light in order to produce an image with a visually acceptable amount of noise. This made path tracing very slow on computers available in the 1980s and 1990s, and noise remained a problem when trying to reproduce the style of earlier computer graphics animated films. Most animated films produced until around 2010, by studios such as Pixar, used rasterization-based rendering, with ray tracing used selectively for reflections (and later for precomputed or cached global illumination). However the speed of computers rapidly increased during the 1990s. Blue Sky Studios pioneered using Monte Carlo ray tracing for global illumination in animation, including in the 1998 short film "Bunny", but they did not disclose the precise techniques used. Path tracing gradually become more practical for film production in the early 2000s. The Arnold renderer, developed by Marcos Fajardo, was used by Sony Pictures Imageworks to produce the feature-length film Monster House, released in 2006. Pixar rewrote their RenderMan software to use path tracing, and released their first feature-length path-traced film Finding Dory in 2016. Although path tracing still had a large computational cost, animation studios discovered that less human labor was required when using it, for example because global illumination no longer needed to be faked by manually placing lights. The amount of noise present in path traced images still caused difficulties, particularly when rendering motion blur (which was used extensively by earlier animated films) but denoising techniques were developed to address this. New techniques were also needed for rendering hair and fur, and to handle the extremely large scenes sometimes required by films. Renderers such as Arnold, and Disney's Hyperion, originally only used CPUs for rendering, but as GPUs became more capable (and APIs such as CUDA, OpenCL, and OptiX were released) researchers and developers began adapting algorithms and implementations to use GPUs. GPUs can dramatically reduce rendering time: for example using a high-end GPU to accelerate portions of the rendering code can make it over 30 times faster than using only a high-end CPU. == Description == Kajiya's 1986 paper defined a recursive integral equation called the rendering equation, which describes a simplified form of light transport. Using Monte Carlo integration for the integral on the right side of the equation leads fairly directly to the path tracing algorithm: I ( x , x ′ ) = g ( x , x ′ ) [ ϵ ( x , x ′ ) + ∫ S ρ ( x , x ′ , x ″ ) I ( x ′ , x ″ ) d x ″ ] {\displaystyle I(x,x')=g(x,x')\left[\epsilon (x,x')+\int _{S}\rho (x,x',x'')I(x',x'')dx''\right]} This expresses I(x,x'), the light arriving at point x from point x', as the product of a geometry term, g(x,x'), which is 0 if there is something blocking the light between the two points and 1 otherwise, and the amount of light leaving point x' and traveling towards x. The light leaving point x' is the sum of the light emitted by the surface at x', and the integral of the light arriving at x' from all other points in the scene (the integration domain S) and being reflected towards x. The factor ρ(x,x',x''), which calculates how much light is reflected, must take into account the angles at which the light is arriving and leaving, and

  Read more →
• 

  History of machine translation

  Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another. In the 1950s, machine translation became a reality in research, although references to the subject can be found as early as the 17th century. The Georgetown experiment, which involved successful fully automatic translation of more than sixty Russian sentences into English in 1954, was one of the earliest recorded projects. Researchers of the Georgetown experiment asserted their belief that machine translation would be a solved problem within a few years. In the Soviet Union, similar experiments were performed shortly after. Consequently, the success of the experiment ushered in an era of significant funding for machine translation research in the United States. The achieved progress was much slower than expected; in 1966, the ALPAC report found that ten years of research had not fulfilled the expectations of the Georgetown experiment and resulted in dramatically reduced funding. Interest grew in statistical models for machine translation, which became more common and also less expensive in the 1980s as available computational power increased. Although there exists no autonomous system of "fully automatic high quality translation of unrestricted text," there are many programs now available that are capable of providing useful output within strict constraints. Several of these programs are available online, such as Google Translate and the SYSTRAN system that powers AltaVista's BabelFish (which was replaced by Microsoft Bing translator in May 2012). == The beginning == The origins of machine translation can be traced back to the work of Al-Kindi, a 9th-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation. The idea of machine translation later appeared in the 17th century. In 1629, René Descartes proposed a universal language, with equivalent ideas in different tongues sharing one symbol. In the mid-1930s the first patents for "translating machines" were applied for by Georges Artsrouni, for an automatic bilingual dictionary using punched tape. Russian Peter Troyanskii submitted a more detailed proposal that included both the bilingual dictionary and a method for dealing with grammatical roles between languages, based on the grammatical system of Esperanto. This system was separated into three stages: stage one consisted of a native-speaking editor in the source language to organize the words into their logical forms and to exercise the syntactic functions; stage two required the machine to "translate" these forms into the target language; and stage three required a native-speaking editor in the target language to normalize this output. Troyanskii's proposal remained unknown until the late 1950s, by which time computers were well-known and utilized. == The early years == The first set of proposals for computer based machine translation was presented in 1949 by Warren Weaver, a researcher at the Rockefeller Foundation, "Translation memorandum". These proposals were based on information theory, successes in code breaking during the Second World War, and theories about the universal principles underlying natural language. A few years after Weaver submitted his proposals, research began in earnest at many universities in the United States. On 7 January 1954 the Georgetown–IBM experiment was held in New York at the head office of IBM. This was the first public demonstration of a machine translation system. The demonstration was widely reported in the newspapers and garnered public interest. The system itself, however, was no more than a "toy" system. It had only 250 words and translated 49 carefully selected Russian sentences into English – mainly in the field of chemistry. Nevertheless, it encouraged the idea that machine translation was imminent and stimulated the financing of the research, not only in the US but worldwide. Early systems used large bilingual dictionaries and hand-coded rules for fixing the word order in the final output which was eventually considered too restrictive in linguistic developments at the time. For example, generative linguistics and transformational grammar were exploited to improve the quality of translations. During this period operational systems were installed. The United States Air Force used a system produced by IBM and Washington University in St. Louis, while the Atomic Energy Commission and Euratom, in Italy, used a system developed at Georgetown University. While the quality of the output was poor it met many of the customers' needs, particularly in terms of speed. At the end of the 1950s, Yehoshua Bar-Hillel was asked by the US government to look into machine translation, to assess the possibility of fully automatic high-quality translation by machines. Bar-Hillel described the problem of semantic ambiguity or double-meaning, as illustrated in the following sentence: Little John was looking for his toy box. Finally he found it. The box was in the pen. The word pen may have two meanings: the first meaning, something used to write in ink with; the second meaning, a container of some kind. To a human, the meaning is obvious, but Bar-Hillel claimed that without a "universal encyclopedia" a machine would never be able to deal with this problem. At the time, this type of semantic ambiguity could only be solved by writing source texts for machine translation in a controlled language that uses a vocabulary in which each word has exactly one meaning. == The 1960s, the ALPAC report and the seventies == Research in the 1960s in both the Soviet Union and the United States concentrated mainly on the Russian–English language pair. The objects of translation were chiefly scientific and technical documents, such as articles from scientific journals. The rough translations produced were sufficient to get a basic understanding of the articles. If an article discussed a subject deemed to be confidential, it was sent to a human translator for a complete translation; if not, it was discarded. A great blow came to machine-translation research in 1966 with the publication of the ALPAC report. The report was commissioned by the US government and delivered by ALPAC, the Automatic Language Processing Advisory Committee, a group of seven scientists convened by the US government in 1964. The US government was concerned that there was a lack of progress being made despite significant expenditure. The report concluded that machine translation was more expensive, less accurate and slower than human translation, and that despite the expenditures, machine translation was not likely to reach the quality of a human translator in the near future. The report recommended, however, that tools be developed to aid translators – automatic dictionaries, for example – and that some research in computational linguistics should continue to be supported. The publication of the report had a profound impact on research into machine translation in the United States, and to a lesser extent the Soviet Union and United Kingdom. Research, at least in the US, was almost completely abandoned for over a decade. In Canada, France and Germany, however, research continued. In the US the main exceptions were the founders of SYSTRAN (Peter Toma) and Logos (Bernard Scott), who established their companies in 1968 and 1970 respectively and served the US Department of Defense. In 1970, the SYSTRAN system was installed for the United States Air Force, and subsequently by the Commission of the European Communities in 1976. The METEO System, developed at the Université de Montréal, was installed in Canada in 1977 to translate weather forecasts from English to French, and was translating close to 80,000 words per day or 30 million words per year until it was replaced by a competitor's system on 30 September 2001. While research in the 1960s concentrated on limited language pairs and input, demand in the 1970s was for low-cost systems that could translate a range of technical and commercial documents. This demand was spurred by the increase of globalisation and the demand for translation in Canada, Europe, and Japan. == The 1980s and early 1990s == By the 1980s, both the diversity and the number of installed systems for machine translation had increased. A number of systems relying on mainframe technology were in use, such as SYSTRAN, Logos, Ariane-G5, and Metal. As a result of the improved availability of microcomputers, there was a market for lower-end machine translation systems. Many companies took advantage of this in Europe, Japan, and the USA. Systems were also brought onto the market in China, Eastern Europe, Korea, and the Soviet Union. During the 1980s there was a lot of activity in MT in Japan especially. With the fifth-generation co

  Read more →
• 

  Control-flow integrity

  Control-flow integrity (CFI) is a general term for computer security techniques that prevent a wide variety of malware attacks from redirecting the flow of execution (the control flow) of a program. == Background == A computer program commonly changes its control flow to make decisions and use different parts of the code. Such transfers may be direct, in that the target address is written in the code itself, or indirect, in that the target address itself is a variable in memory or a CPU register. In a typical function call, the program performs a direct call, but returns to the caller function using the stack – an indirect backward-edge transfer. When a function pointer is called, such as from a virtual table, we say there is an indirect forward-edge transfer. Attackers seek to inject code into a program to make use of its privileges or to extract data from its memory space. Before executable code was commonly made read-only, an attacker could arbitrarily change the code as it is run, targeting direct transfers or even do with no transfers at all. After W^X became widespread, an attacker wants to instead redirect execution to a separate, unprotected area containing the code to be run, making use of indirect transfers: one could overwrite the virtual table for a forward-edge attack or change the call stack for a backward-edge attack (return-oriented programming). CFI is designed to protect indirect transfers from going to unintended locations. == Techniques == Associated techniques include code-pointer separation (CPS), code-pointer integrity (CPI), stack canaries, shadow stacks (SS), and vtable pointer verification. These protections can be classified into either coarse-grained or fine-grained based on the number of targets restricted. A coarse-grained forward-edge CFI implementation, could, for example, restrict the set of indirect call targets to any function that may be indirectly called in the program, while a fine-grained one would restrict each indirect call site to functions that have the same type as the function to be called. Similarly, for a backward edge scheme protecting returns, a coarse-grained implementation would only allow the procedure to return to a function of the same type (of which there could be many, especially for common prototypes), while a fine-grained one would enforce precise return matching (so it can return only to the function that called it). == Implementations == Related implementations are available in Clang (LLVM front-end),, GNU Compiler Collection, Microsoft's Control Flow Guard and Return Flow Guard, Google's Indirect Function-Call Checks and Reuse Attack Protector (RAP). === LLVM/Clang === The LLVM compiler's C/C++ front-end Clang provides a number of "CFI" schemes that works on the forward edge by checking for errors in virtual tables and type casts. Not all of the schemes are supported on all platforms and most of them, the exception being two "kcfi" schemes intended for low-level kernel software, depends on link-time optimization (LTO) to know what functions are supposed to be called in normal cases. Also provided is a separate "shadow call stack" (SCS) instrumentation pass that defends on the backward edge by checking for call stack modifications, available only for the aarch64 and RISC-V ISAs. And due to use of a shared processor register SCS is only enforceable on certain ABIs or if in other ways it is ensured that any other software using the register set (thread/processor) does not interfere with this use. Google has shipped Android with the Linux kernel compiled by Clang with link-time optimization (LTO) and CFI enabled since 2018. Even though SCS is available for the Linux kernel as an option, and support is also available for Android's system components it is recommended only to enable it for components for which it can be ensured that no third party code is loaded. === GCC === The GNU Compiler Collection implemented a "shadow call stack" compatible with Clang for aarch64 in v12 released in 2022. This feature is primarily intended for building the Linux kernel as support is missing from GCC user space libraries. === Intel Control-flow Enforcement Technology === Intel Control-flow Enforcement Technology (CET) detects compromises to control flow integrity with a shadow stack (SS) and indirect branch tracking (IBT). The kernel must map a region of memory for the shadow stack not writable to user space programs except by special instructions. The shadow stack stores a copy of the return address of each CALL. On a RET, the processor checks if the return address stored in the normal stack and shadow stack are equal. If the addresses are not equal, the processor generates an INT #21 (Control Flow Protection Fault). Indirect branch tracking detects indirect JMP or CALL instructions to unauthorized targets. It is implemented by adding a new internal state machine in the processor. The behavior of indirect JMP and CALL instructions is changed so that they switch the state machine from IDLE to WAIT_FOR_ENDBRANCH. In the WAIT_FOR_ENDBRANCH state, the next instruction to be executed is required to be the new ENDBRANCH instruction (ENDBR32 in 32-bit mode or ENDBR64 in 64-bit mode), which changes the internal state machine from WAIT_FOR_ENDBRANCH back to IDLE. Thus every authorized target of an indirect JMP or CALL must begin with ENDBRANCH. If the processor is in a WAIT_FOR_ENDBRANCH state (meaning, the previous instruction was an indirect JMP or CALL), and the next instruction is not an ENDBRANCH instruction, the processor generates an INT #21 (Control Flow Protection Fault). On processors not supporting CET indirect branch tracking, ENDBRANCH instructions are interpreted as NOPs and have no effect. === Microsoft Control Flow Guard === Control Flow Guard (CFG) was first released for Windows 8.1 Update 3 (KB3000850) in November 2014. Developers can add CFG to their programs by adding the /guard:cf linker flag before program linking in Visual Studio 2015 or newer. As of Windows 10 Creators Update (Windows 10 version 1703), the Windows kernel is compiled with CFG. The Windows kernel uses Hyper-V to prevent malicious kernel code from overwriting the CFG bitmap. CFG operates by creating a per-process bitmap, where a set bit indicates that the address is a valid destination. Before performing each indirect function call, the application checks if the destination address is in the bitmap. If the destination address is not in the bitmap, the program terminates. This makes it more difficult for an attacker to exploit a use-after-free by replacing an object's contents and then using an indirect function call to execute a payload. ==== Implementation details ==== For all protected indirect function calls, the _guard_check_icall function is called, which performs the following steps: Convert the target address to an offset and bit number in the bitmap. The highest 3 bytes are the byte offset in the bitmap The bit offset is a 5-bit value. The first four bits are the 4th through 8th low-order bits of the address. The 5th bit of the bit offset is set to 0 if the destination address is aligned with 0x10 (last four bits are 0), and 1 if it is not. Examine the target's address value in the bitmap If the target address is in the bitmap, return without an error. If the target address is not in the bitmap, terminate the program. ==== Bypass techniques ==== There are several generic techniques for bypassing CFG: Set the destination to code located in a non-CFG module loaded in the same process. Find an indirect call that was not protected by CFG (either CALL or JMP). Use a function call with a different number of arguments than the call is designed for, causing a stack misalignment, and code execution after the function returns (patched in Windows 10). Use a function call with the same number of arguments, but one of pointers passed is treated as an object and writes to a pointer-based offset, allowing overwriting a return address. Overwrite the function call used by the CFG to validate the address (patched in March 2015) Set the CFG bitmap to all 1's, allowing all indirect function calls Use a controlled-write primitive to overwrite an address on the stack (since the stack is not protected by CFG) === Microsoft eXtended Flow Guard === eXtended Flow Guard (XFG) has not been officially released yet, but is available in the Windows Insider preview and was publicly presented at Bluehat Shanghai in 2019. XFG extends CFG by validating function call signatures to ensure that indirect function calls are only to the subset of functions with the same signature. Function call signature validation is implemented by adding instructions to store the target function's hash in register r10 immediately prior to the indirect call and storing the calculated function hash in the memory immediately preceding the target address's code. When the indirect call is made, the XFG validation function compares the value in r10 to the target

  Read more →
• 

  Cybersecurity in space

  Cybersecurity in space involves the defense of all space assets (e.g. navigation systems, satellites, ground antennas, networks, etc.). The security of space can be affected by attacks such as disruption, corruption as well as the destruction of depended-upon assets/collected data. Government (e.g. militaries) and non-government sectors (e.g. financial industries) have started to become more reliant on numerous space-based services. Due to the criticality of these services, space security experts have identified these assets as high-value targets (HVT) that can cause detrimental consequences to all of Earth. == Scope and definitions == Space assets are broken down by three sub-sectors: the space component, the ground component, and the individual user component. The architecture of space assets is extremely complex and allows for a frequent attack vector utilized, the disruption by radio frequency (RF) cyber-attacks. In 2020, a memorandum was published by President Donald Trump, Space Policy Directive‑5 (SPD‑5). It established principles to ensure the safeguarding of all space assets. In 2023, the National Institute of Standards and Technology’s (NIST) published IR 8270, Introduction to Cybersecurity for Commercial Satellite Operations. This report established a baseline risk-management framework (RMF) to be implemented into space operations. == History == During the Cold War in the 1950s-1960s, the United States and Russia entered what was called the “Space Race”. By 1957, the Soviet Union successfully launched the first satellite into space named Sputnik. By 1961, the first key milestone was accomplished when the Soviet Union’s Yuri Gagarin became the first human to orbit Earth. This was later followed by the first American, Alan Shepard, to be launched into space; this was followed by John Glenn becoming the first American to orbit Earth in 1962. In 1969, a pinnacle milestone was reached when Apollo 11 launched into space and Neil Armstrong became the first man to walk on the moon. As space operations furthered, Commercial off-the-shelf products became increasingly popular but resulted in a rapid increase to the cyber-attack surface. Public awareness of space security did not increase until 2022, when the Viasat KA-SAT incident occurred, resulting in the disruption of a large number of modems across Europe. The attack was later accredited to Russia by the U.S. and the U.K. Policy and standards started to rapidly increase by 2020. The establishment of SPD-5 was released in 2020 followed by asset hardening instructions in 2022, and NIST’s IR 8270 in 2023. It was not until 2025 that Europe published their own findings in the Space Threat Landscape 2025 Report. This document led to the EU’s security proposals and standards. == Threats == === Radio-frequency Interference and Global Navigation Satellite Systems (GNSS) Spoofing === Space services are highly dependent on RF links for systems such as GNSS, however, a consequence of this dependency on RF is denial of service and deception. In 2017, the Black Sea maritime event occurred when numerous ships were subject to spoofing. Space services depend on RF links susceptible to jamming (denial) and spoofing (deception), including for GNSS/Positioning, Navigation, and Timing (PNT). Annotated incidents include the 2017 Black Sea maritime spoofing event affecting numerous ships, and extensive aviation GNSS spoofing patterns surveyed in various regions during 2024–2025. === Network intrusion and malware === Cyber threats can intrude and infect assets with malware. They do this by finding misconfiguration vulnerabilities, remote-management interfaces, and/or supply-chain vulnerabilities mainly in ground networks and user terminals. When KA-SAT occurred, it resulted from bulk modem disturbances. Forensic analysts later suggested malicious management controls and wiper malware as the root cause. === Supply-chain and lifecycle risks === The outsource of COTS components, external vendors, and software defined payloads allowed for vulnerabilities to emerge in the System/Product Lifecycle. In response, EU recommended the implementation of lifecycle-wide controls as mitigating factors. === Espionage, disruption, and influence === As Advanced Persistent Threats (APTs), Global Positioning System (GPS) intervention, and information warfare increased, assets like transponders became more frequent targets of attack. == Noteworthy incidents == The Viasat KA‑SAT incident of 2022, where a large number of modems in Europe were disrupted, resulted in the loss of telemetry access to a significant amount of wind turbines in Germany. The mass GNSS deception of the Black Sea in 2017 affected numerous ships when they started to convey fake central locations in Russia. Between 2024 and 2025, there was a mass, repetitive aviation GNSS spoofing that affected the aircraft of various regions. == Standards, guidelines, and best practices == SPD‑5 (U.S.) – This established risk-based engineering, verifying and ensuring positive control, and the implementation of risk mitigation controls. NIST IR 8270 – This created a RMF for COTS satellites. CISA/FBI SATCOM Advisory (AA22‑076) – Provided guidance on hardening techniques such as least-privileged, access control, encryption, etc.). ENISA Space Threat Landscape 2025 – It established the categorization of assets to organize threats, ensuring the observation of system/product lifecycle, and an RMF for COTS satellites. ECSS‑E‑ST‑80C (2024) – This established a standard for securing lifecycles in space, covering all segments (e.g. ground, launch, etc.). == Regulation and governance == As of 2025, there is no international regulations established for space assets, but the U.S., EU, and ESA institutional initiatives have published standards to address security concerns. The U.S. implemented SPD-5 and the Federal Communications Commission (FCC); the FCC addressed orbital debris. While the EU created standards to address technological mandates and support the implementation of NIS2. Lastly, the ESA created a special operations center to safeguard their satellites. International governance is still evolving, but forums have been held by the United Nations Committee on the Peaceful Uses of Outer Space. International conversations under forums such as the UN Committee on the Peaceful Uses of Outer Space (COPUOS) progressively note the cyber–space safety relationship, though formal global norms specific to space cybersecurity continue evolving. == Risk management approaches == Through RMF, mitigation controls have been implemented to reduce the risk of exploitation while increasing the security of space. Controls addressing mitigation include proper configuration, system hardening, zero-trust architectures, encryption, etc. Both the government and industries have placed an emphasis on incident response procedures to identify, contain, and remediate breaches.

  Read more →
• 

  Vinted

  Vinted Group UAB is a Lithuanian technology company best known for its online marketplace Vinted. Vinted is the leading second-hand fashion marketplace in Europe and a go-to destination for all kinds of second-hand items. According to the company, its mission is to make second-hand the first choice worldwide. The company operates as an ecosystem of businesses, including the Vinted Marketplace (its peer-to-peer resale platform), Vinted Go (logistics and shipping services), Vinted Pay (in-app payment solutions), and Vinted Ventures (an investment arm supporting the circular economy). Headquartered in Vilnius, Lithuania, it also has offices in Germany and the Netherlands and employs more than 2,200 people. == History == Vinted was co-founded in 2008 by Milda Mitkute and Justas Janauskas in Vilnius, Lithuania. The idea originated when Mitkute was moving house and wanted a way to sell clothes she no longer needed. Janauskas helped her create a website where users could trade clothing items. In 2016, Dutch entrepreneur Thomas Plantenga joined Vinted as a strategy consultant and later became Chief Executive Officer, leading the company through a period of international growth. In 2019, Vinted became Lithuania’s first technology unicorn after raising €128 million at a €1 billion valuation in a funding round led by Lightspeed Venture Partners. In October 2020, it acquired United Wardrobe, a Dutch competitor, and in November 2020 German Kleiderkreisel and Mamikreisel were officially merged into the Vinted platform. In 2024 it acquired Trendsales, a Danish resale platform. According to Vogue Business, Vinted’s revenue grew 61% between 2022 and 2023 and the company posted a net profit of €17.8 million in 2023. Usage of Vinted in the UK has grown from 1.2 million users in 2021, to 8 million in 2023. In 2024, the group reported consolidated revenue of €813.4 million (up 36% from 2023) and a net profit of €76.7 million, up 330% from 2023. As of 2024, Vinted was valued at approximately €5 billion, operating in more than 26 markets worldwide and announcing plans to launch in Ireland, Greece, Latvia, Slovenia, and Estonia in 2025. As of 2025 the company employed more than 2,200 people. In April 2026, Vinted completed a secondary share transaction of €880m, valuing the company at €8bn. == Products and operations == Vinted primarily resells clothing but now supports multiple categories including homeware, kidswear, electronics, books, collectibles, and high-value fashion. Vinted has worked with public figures such as Paul Mescal and Alexa Chung on exclusive wardrobe sales and has also partnered directly with charities including Oxfam on initiatives which promote the social and environmental value of second-hand fashion, such as the Style for Change fashion show at London Fashion Week. In 2025, Vinted produced its first television format, the second-hand fashion competition series RE/Style, hosted by Emma Willis. The show features emerging fashion designers from across Europe creating runway-ready looks from second-hand garments and aired on Prime Video UK. In 2025, Vinted was reported as France’s top clothing retailer by sales volume. == Criticism == Vinted has faced scrutiny from European data protection authorities in France, Lithuania, and Poland following complaints regarding GDPR compliance and account blocking practices. In July 2024, the Lithuanian authority fined the company €2,375,276. The case was coordinated by a dedicated Vinted Working Group under the European Data Protection Board. In early 2024, Swedish police reported around 300 fraud cases linked to the platform, in which users’ bank accounts were targeted by scammers. In October 2024, Channel 4 in the United Kingdom aired a documentary examining safety and privacy concerns related to the platform, including the sexualisation of underage users’ images and risks associated with second-hand baby products lacking safety certification. In November 2025, BBC News reported that Vinted’s update to its sizing system in the United Kingdom led to widespread user criticism. Vinted said the update was intended to standardise sizing across international brands.

  Read more →
• 

  BabyCenter

  BabyCenter is an online media company based in San Francisco, New York City, Chicago, and Los Angeles that provides information on conception, pregnancy, birth, and early childhood development for parents and expecting parents. BabyCenter operates 8 country and region specific properties including websites, apps, emails, print publications, and an online community where parents can connect on a variety of topics. The visitors of website and the users of the app can sign up for free weekly email newsletters that guide them through pregnancy and their child's development. In addition to publishing detailed, medically reviewed information about pregnancy and parenting, BabyCenter, under its Mission Motherhood initiative, ran numerous social programs and has participated in public health initiatives in partnership with hospitals, healthcare agencies, nonprofits, NGOs, and government agencies to provide pregnancy and parenting advice. It also annually publishes the most popular baby names. BabyCenter LLC is part of the Everyday Health Group, a division of Ziff Davis. == History == BabyCenter was founded in October 1997 by Stanford University MBA graduates Matt Glickman and Mark Selcow, who recognized a need for information about pregnancy and parenting on the internet. BabyCenter was initially funded through $13.5 million in startup capital funding from venture capital firms, including Bessemer Venture Partners, Intel, and Trinity Ventures. The funds were used to open the BabyCenter Store in October 1998. In the early years of its operation, BabyCenter offered multiple resources and services for parents, including a website that provided medically reviewed information and guidance to new and expectant parents on such topics as fertility, labor, and childcare; a weekly email for pregnant women tailored to their week of pregnancy (based on their pregnancy due date); and community groups and chat rooms for pregnant couples and parents to discuss pregnancy and child-rearing strategies. The site grew quickly, and by early 1999 had 175 employees and an annual revenue of $35 million. In April of that year, the two founders sold BabyCenter to another website, eToys.com, for $190 million in stock. Twenty-three months later, in 2001, shortly before declaring bankruptcy, eToys sold the site to Johnson & Johnson for $10 million. During the eToys ownership, BabyCenter launched its first international E-commerce site in the UK during the spring of 2000. Starting in 2005, BabyCenter launched an expansion plan, extending its global network to Australia, Canada and other countries, staffing each outpost with local editors. In 2007, BabyCenter debuted a Mandarin-language site in China, initiated operations in India, launched a Spanish language website, and introduced its first mobile site. BabyCenter released My Pregnancy Today, its first mobile app, to Apple's App Store in August 2010 and to the Android market in April 2011. The app provided daily information, nutrition tips, advice relevant to the user's week of pregnancy, and 3-D animated videos showcasing a baby's development in utero. The My Pregnancy app was joined by a My Baby Today app in October 2011. In 2015, BabyCenter released Mom Feed, its first mobile app for parents of toddlers and older children (ages 1 to 8). Mom Feed offered personalized, stage-based information as well as content from the BabyCenter Community and Blog in a real-time stream. In 2016, BabyCenter launched its web-based Baby Names Finder. In 2018, Mom Feed was discontinued and BabyCenter replaced that experience with a separate Child Health content area on its website. Also in 2018, BabyCenter launched its mobile baby name generator, the Baby Names app, which, like the web-based Baby Names Finder, leverages data from hundreds of thousands of parents that culminates in its annual most popular Baby Names Report. In 2019, Johnson & Johnson sold Baby Center to Everyday Health Group, a division of New York-based parent company of Ziff Davis, Inc. Neither side disclosed terms of the deal. == Popular research == BabyCenter's most popular baby names is released annually and often cited by the media. In March 2024, BabyCenter did a review of the app Temu and said that the website has found products that have been recalled, could be counterfeit or circumvent U.S. safety standards and features that are important in preventing issues like choking. In 2025, BabyCenter released a report about the cost of raising a newborn baby in the first year. == Content and products == === Websites === BabyCenter has 8 country and region-specific websites around the world, including sites for the United States, Canada, Australia, Brazil, India, Germany, the United Kingdom, and Latin America. Users can find parenting and pregnancy advice in seven languages: English, Spanish, Portuguese, Arabic, French, German, and Hindi BabyCenter content for each country- or region-specific site is written by an editorial team based in that country or region. Medical and health content for each site is reviewed by a medical advisory board based there and adheres to that country or region's medical standards. For example, the U.S. site works with and follows the recommendations of such U.S. medical authorities as the American Academy of Pediatrics, the American Congress of Obstetrics & Gynecology and the Society for Maternal-Fetal Medicine. BabyCenter regularly conducts research and provides thought leadership on pregnancy and parenting topics, popularly cited by major media outlets including The Wall Street Journal, Forbes, The Washington Post, BuzzFeed, Insider, MarketWatch, Axios. === Community, blogs and social === From its earliest days, BabyCenter has had a community area that allows people to join a group of parents with children born in the same month, known as a Birth Club. BabyCenter launched a blog called Momformation in 2007. Eventually, the name was changed to BabyCenter Blog. In April 2021, the BabyCenter Community was identified in a research article within the journal PLOS Computational Biology as facilitating "unobstructed communication" between parents, which avoids the "strong echo chamber phenomena" that can foster and perpetuate vaccine misinformation. === My Pregnancy and Baby Today App === The app is available in six languages, although not all features are supported for every market. Initially the apps only featured pregnancy articles that could be found on the BabyCenter website, but over the years the feature set has expanded to include a growing list of app-specific tools such as weekly fetal development information, a kick tracker, a birth plan worksheet, a contraction timer, a baby growth tracker, a photo journal for pregnant women to record their pregnancy bellies, and a photo journal for documenting a baby's first year. === Mission Motherhood™ === BabyCenter was a cofounder of the Mobile Alliance for Maternal Action (MAMA), a public-private partnership between USAID, Johnson & Johnson, the UN Foundation, and BabyCenter from 2011 to-to 2015. The MAMA program sparked the creation of MomConnect, an initiative of the South African Department of Health for which BabyCenter developed SMS messages with health information about pregnancy and a child's first year of life. BabyCenter helped develop similar messages for mMitra, a voice messaging program in India. A research article in the Maternal and Child Health Journal stated the mMitra program offered strong evidence "that tailored mobile phone voice messages can improve key infant care knowledge and practices that lead to improved infant health outcomes in low-resource settings. BabyCenter's Mission Motherhood Messages were available to qualifying organizations on the BabyCenter website. BabyCenter contributed websites for Free Basics. These websites featured age and stage-based pregnancy and baby articles targeted to low-income, lower-education women who would not otherwise have access to health information. Content developed for this program was also used to support a UNICEF SMS program during the 2016 Zika outbreak. == Awards and recognition == In 1998, BabyCenter won a Webby Award for Best Home Site. Since then, it has been nominated for a Webby Award 19 times and won either a Webby or a People's Choice Webby Award 12 times – including a People's Voice win in 2021 for Lifestyle websites and mobile sites. In 2002, it won Service Journalism award from Online Journalism Awards (OJA). In 2015, BabyCenter won five Digital Health Awards for content about autism in children. In 2016, BabyCenter won seven Digital Health Awards: four for videos about the aches and pains of pregnancy, baby sleep, and the walking milestone in child development; two for articles about baby sleep training and sleep apnea in babies; and one for the BabyCenter mobile app My Pregnancy & Baby Today. In 2021, Forbes Health chose My Pregnancy & Baby Today as the best pregnancy app of 2021, and Women's Health identified it

  Read more →
• 

  Key frame

  In animation and filmmaking, a key frame (or keyframe) is a drawing or shot that defines the starting and ending points of a smooth transition. These are called frames because their position in time is measured in frames on a strip of film or on a digital video editing timeline. A sequence of key frames defines which movement the viewer will see, whereas the position of the key frames on the film, video, or animation defines the timing of the movement. Because only two or three key frames over the span of a second do not create the illusion of movement, the remaining frames are filled with "inbetweens". == Use of key frames as a means to change parameters == In software packages that support animation, especially 3D graphics, there are many parameters that can be changed for any one object. One example of such an object is a light. In 3D graphics, lights function similarly to real-world lights. They cause illumination, cast shadows, and create specular highlights. Lights have many parameters, including light intensity, beam size, light color, and the texture cast by the light. Supposing that an animator wants the beam size to change smoothly from one value to another within a predefined period of time, that could be achieved by using key frames. At the start of the animation, a beam size value is set. Another value is set for the end of the animation. Thus, the software program automatically interpolates the two values, creating a smooth transition. == Video editing == In non-linear digital video editing, as well as in video compositing software, a key frame is a frame used to indicate the beginning or end of a change made to a parameter. For example, a key frame could be set to indicate the point at which audio will have faded up or down to a certain level. == Video compression == In video compression, a key frame, also known as an intra-frame, is a frame in which a complete image is stored in the data stream. In video compression, only changes that occur from one frame to the next are stored in the data stream, in order to greatly reduce the amount of information that must be stored. This technique capitalizes on the fact that most video sources (such as a typical movie) have only small changes in the image from one frame to the next. Whenever a drastic change to the image occurs, such as when switching from one camera shot to another or at a scene change, a key frame must be created. The entire image for the frame must be output when the visual difference between the two frames is so great that representing the new image incrementally from the previous frame would require more data than recreating the whole image. Because video compression only stores incremental changes between frames (except for key frames), it is not possible to fast-forward or rewind to any arbitrary spot in the video stream. That is because the data for a given frame only represents how that frame was different from the preceding one. For that reason, it is beneficial to include key frames at arbitrary intervals while encoding video. For example, a key frame may be output once for each 10 seconds of video, even though the video image does not change enough visually to warrant the automatic creation of the key frame. That would allow seeking within the video stream at a minimum of 10-second intervals. The downside is that the resulting video stream will be larger in disk size because many key frames are added when they are not necessary for the frame's visual representation. This drawback, however, does not produce significant compression loss when the bitrate is already set at a high value for better quality (as in the DVD MPEG-2 format).

  Read more →
• 

  Pray.com

  Pray.com is a Christian social networking service and mobile application designed to facilitate religious communities. Launched in 2016, it was founded by Steve Gatena, Michael Lynn, Ryan Beck and Matthew Potter. The platform offers features for social networking, daily prayers, sermons, biblical content, and podcasts. The COVID-19 pandemic significantly increased Pray.com's user base, with downloads surging by 955%. During this period, the platform collaborated with churches to support virtual ministry services as in-person gatherings were restricted. The Federal Election Commission issued an opinion in 2021 that allows the platform to feature members of the United States Congress. Pray.com serves as a specialized social media platform for religious groups. Congregations can establish their own groups where members and leaders can participate in discussions, livestream services, and manage donations. Additionally, users can join "prayer communities" to post and respond to prayer requests. For those who subscribe to premium services, the platform provides access to biblically-inspired meditations and bedtime stories, and Bible stories for children. Pray.com also produces Radio drama-style productions with notable actors such as Kristen Bell and Blair Underwood narrating biblical stories. == History == === Funding and development === Pray.com has secured significant funding to support its development and growth. In 2017, the platform raised $2 million in seed funding from Science Inc., Greylock Partners, and Spark Capital. This was followed by a Series A funding round in March 2018, in which the company secured an additional $14 million from TPG Growth, Science Inc., and Greylock Partners. Founder Steve Gatena has highlighted difficulties in securing funding, noting some venture capitalists' negative attitudes towards faith-based technology. === Clinical studies === There have been clinical studies on Pray.com. In one study, the app was found to be acceptable and easy to use among racial and ethnic minority groups, with participants reporting improved mental health and well-being. Greater app use was associated with better outcomes, though low and variable usage suggests the need for further research to fully understand its impact. Another study examined Pray.com's impact on mental health by assigning 192 participants to use the app freely, use its meditative prayer function, or not use it at all. Over two months, participants reported overall improvements in mental health and well-being. Although no significant differences were found between groups, greater app usage correlated with better mental health outcomes. This suggests that religiously based mobile apps may help improve mental health and well-being. Another study of pray.com had similar findings. === National Day of Prayer === Pray first hosted a National Day of Prayer event in 2020 when it streamed to nearly one million viewers on Facebook. In 2021, Pray hosted a virtual event for the National Day of Prayer in the United States. The event featured remarks from public figures including United States President Joe Biden and former Vice President Mike Pence. President Biden spoke of his faith and prayed for an end to the COVID-19 pandemic. Biden remarked: "It means the world to me to know that there are people across the country who include Jill and me in their prayers. And I hope you know that you and your families are in our prayers as well. Today I am praying for the end of this great COVID crisis." The event featured musical performances from Gary Valenciano, Brooke Ligertwood from the Christian band Hillsong Worship, Lecrae, Heather Headley and Michael Neale. Other notable speakers included Ronnie Floyd, Ed Young, Mark Driscoll, and Samuel Rodriguez. Pray.com partnered with Sirius XM, DirecTV and Facebook to stream the event across multiple platforms. Pray.com was featured as a pop-up channel on Sirius XM, channel 154, to host the prayer event and celebrate people of all faith. === Partnerships and sponsorships === In 2024, Pray.com partnered with Sting Ray Robb as the primary sponsor for his No. 41 Chevrolet in the 2024 NTT IndyCar Series. The partnership, highlighting Robb's Christian faith, aims to engage younger audiences with faith-based content. The car, featuring Pray.com's branding, was set to debut at the Firestone Grand Prix of St. Petersburg. A partnership with Palantir Technologies for use of its AI systems was also announced in 2024. === Censorship in China === The app was removed from Apple's App Store in China as part of the country's broader efforts to restrict access to religious content. The app was targeted due to China's stringent regulations on religious material, particularly content distributed through digital platforms. The removal aligns with China's ongoing campaign to control online religious expression and maintain state-approved religious activities.

  Read more →
• 

  Sprite (computer graphics)

  In computer graphics, a sprite is a two-dimensional bitmap that is integrated into a larger scene, most often in a 2D video game. Originally, the term sprite referred to fixed-sized objects composited together, by hardware, with a background. Use of the term has since become more general. Systems with hardware sprites include arcade video games of the 1970s and 1980s; game consoles including as the Atari VCS (1977), ColecoVision (1982), Famicom (1983), Genesis/Mega Drive (1988); and home computers such as the TI-99/4 (1979), Atari 8-bit computers (1979), Commodore 64 (1982), MSX (1983), Amiga (1985), and X68000 (1987). Hardware varies in the number of sprites supported, the size and colors of each sprite, and special effects such as scaling or reporting pixel-precise overlap. Hardware composition of sprites occurs as each scan line is prepared for the video output device, such as a cathode-ray tube, without involvement of the main CPU and without the need for a full-screen frame buffer. Sprites can be positioned or altered by setting attributes used during the hardware composition process. The number of sprites which can be displayed per scan line is often lower than the total number of sprites a system supports. For example, the Texas Instruments TMS9918 chip supports 32 sprites, but only four can appear on the same scan line. The CPUs in modern computers, video game consoles, and mobile devices are fast enough that bitmaps can be drawn into a frame buffer without special hardware assistance. Beyond that, GPUs can render vast numbers of scaled, rotated, anti-aliased, partially translucent, very high resolution images in parallel with the CPU. == Etymology == According to Karl Guttag, one of two engineers for the 1979 Texas Instruments TMS9918 video display processor, this use of the word sprite came from David Ackley, a manager at TI. It was also used by Danny Hillis at Texas Instruments in the late 1970s. The term was derived from the fact that sprites "float" on top of the background image without overwriting it, much like a ghost or mythological sprite. Some hardware manufacturers used different terms, especially before sprite became common: Player/Missile Graphics was a term used by Atari, Inc. for hardware sprites in the Atari 8-bit computers (1979) and Atari 5200 console (1982). The term reflects the use for both characters ("players") and smaller associated objects ("missiles") that share the same color. The earlier Atari Video Computer System and some Atari arcade games used player, missile, and ball. Stamp was used in some arcade hardware in the early 1980s, including Ms. Pac-Man. Movable Object Block, or MOB, was used in MOS Technology's graphics chip literature. Commodore, the main user of MOS chips and the owner of MOS for most of the chip maker's lifetime, instead used the term sprite for the Commodore 64. OBJs (short for objects) is used in the developer manuals for the NES, Super NES, and Game Boy. The region of video RAM used to store sprite attributes and coordinates is called OAM (Object Attribute Memory). This also applies to the Game Boy Advance and Nintendo DS. == History == === Arcade video games === The use of sprites originated with arcade video games. Nolan Bushnell came up with the original concept when he developed the first arcade video game, Computer Space (1971). Technical limitations made it difficult to adapt the early mainframe game Spacewar! (1962), which performed an entire screen refresh for every little movement, so he came up with a solution to the problem: controlling each individual game element with a dedicated transistor. The rockets were essentially hardwired bitmaps that moved around the screen independently of the background, an important innovation for producing screen images more efficiently and providing the basis for sprite graphics. The earliest video games to represent player characters as human player sprites were arcade sports video games, beginning with Taito's TV Basketball, released in April 1974 and licensed to Midway Manufacturing for release in North America. Designed by Tomohiro Nishikado, he wanted to move beyond simple Pong-style rectangles to character graphics, by rearranging the rectangle shapes into objects that look like basketball players and basketball hoops. Ramtek released another sports video game in October 1974, Baseball, which similarly displayed human-like characters. The Namco Galaxian arcade system board, for the 1979 arcade game Galaxian, displays animated, multi-colored sprites over a scrolling background. It became the basis for Nintendo's Radar Scope and Donkey Kong arcade hardware and home consoles such as the Nintendo Entertainment System. According to Steve Golson from General Computer Corporation, the term "stamp" was used instead of "sprite" at the time. === Home systems === Signetics devised the first chips capable of generating sprite graphics (referred to as objects by Signetics) for home systems. The Signetics 2636 video processors were first used in the 1978 1292 Advanced Programmable Video System and later in the 1979 Elektor TV Games Computer. The Atari VCS, released in 1977, has a hardware sprite implementation where five graphical objects can be moved independently of the game playfield. The term sprite was not in use at the time. The VCS's sprites are called movable objects in the programming manual, further identified as two players, two missiles, and one ball. These each consist of a single row of pixels that are displayed on a scan line. To produce a two-dimensional shape, the sprite's single-row bitmap is altered by software from one scan line to the next. The 1979 Atari 400 and 800 home computers have similar, but more elaborate, circuitry capable of moving eight single-color objects per scan line: four 8-bit wide players and four 2-bit wide missiles. Each is the full height of the display—a long, thin strip. DMA from a table in memory automatically sets the graphics pattern registers for each scan line. Hardware registers control the horizontal position of each player and missile. Vertical motion is achieved by moving the bitmap data within a player or missile's strip. The feature was called player/missile graphics by Atari. Texas Instruments developed the TMS9918 chip with sprite support for its 1979 TI-99/4 home computer. An updated version is used in the 1981 TI-99/4A. === In 2.5D and 3D games === Sprites remained popular with the rise of 2.5D games (those which recreate a 3D game space from a 2D map) in the late 1980s and early 1990s. A technique called billboarding allows 2.5D games to keep onscreen sprites rotated toward the player view at all times. Some 2.5D games, such as 1993's Doom, allow the same entity to be represented by different sprites depending on its rotation relative to the viewer, furthering the illusion of 3D. Fully 3D games usually present world objects as 3D models, but sprites are supported in some 3D game engines, such as GoldSrc and Unreal, and may be billboarded or locked to fixed orientations. Sprites remain useful for small details, particle effects, and other applications where the lack of a third dimension is not a major detriment. == Systems with hardware sprites == These are base hardware specs and do not include additional programming techniques, such as using raster interrupts to repurpose sprites mid-frame.

  Read more →
• 

  MegaHAL

  MegaHAL is a computer conversation simulator, or "chatterbot", created by Jason Hutchens. == Background == In 1996, Jason Hutchens entered the Loebner Prize Contest with HeX, a chatterbot based on ELIZA. HeX won the competition that year and took the $2000 prize for having the highest overall score. In 1998, Hutchens again entered the Loebner Prize Contest with his new program, MegaHAL. MegaHAL made its debut in the 1998 Loebner Prize Contest. Like many chatterbots, the intent is for MegaHAL to appear as a human fluent in a natural language. As a user types sentences into MegaHAL, MegaHAL will respond with sentences that are sometimes coherent and at other times complete gibberish. MegaHAL learns as the conversation progresses, remembering new words and sentence structures. It will even learn new ways to substitute words or phrases for other words or phrases. Many would consider conversation simulators like MegaHAL to be a primitive form of artificial intelligence. However, MegaHAL doesn't understand the conversation or even the sentence structure. It generates its conversation based on sequential and mathematical relationships. In the world of conversation simulators, MegaHAL is based on relatively old technology and could be considered primitive. However, its popularity has grown due to its humorous nature; it has been known to respond with twisted or nonsensical statements that are often amusing. == Theory of Operation == MegaHal is based at least in part on a so-called "hidden Markov Model", so that the first thing that Megahal does when it "trains" on a script or text is to build a database of text fragments encompassing every possible subset of perhaps 4, 5, or even 6 consecutive words, so that for example - if MegaHal trains on the Declaration of Independence, then MegaHal will build a database containing text fragments such as "When in the course", "in the course of", "the course of human", "course of human events", "of human events, one", "human events, one people", and so on. Then if Megahal is fed another text, such has "Superman, Yes! It's Superman - he can change the course of mighty rivers, bend steel with his bare hands - and who disguised at Clark Kent …" IT MIGHT induce Megahal to apparently bemuse itself to proffer whether Superman can change the course of human events, or something else altogether - such as some rambling about "when in the course of mighty rivers", and so on. Thus likewise - if a phrase like "the White house said" comes up a lot in some text; then Megahal's ability to switch randomly between different contexts which otherwise share some similarity can result at times in some surprising lucidity, or else it might otherwise seem quite bizarre. == Examples == There are some sentences that MegaHAL generated: CHESS IS A FUN SPORT, WHEN PLAYED WITH SHOT GUNS. and COWS FLY LIKE CLOUDS BUT THEY ARE NEVER COMPLETELY SUCCESSFUL. == Distribution == MegaHAL is distributed under the Unlicense. Its source code can be downloaded from the Github repository.

  Read more →
• 

  Image-based modeling and rendering

  In computer graphics and computer vision, image-based modeling and rendering (IBMR) methods rely on a set of two-dimensional images of a scene to generate a three-dimensional model and then render some novel views of this scene. The traditional approach of computer graphics has been used to create a geometric model in 3D and try to reproject it onto a two-dimensional image. Computer vision, conversely, is mostly focused on detecting, grouping, and extracting features (edges, faces, etc.) present in a given picture and then trying to interpret them as three-dimensional clues. Image-based modeling and rendering allows the use of multiple two-dimensional images in order to generate directly novel two-dimensional images, skipping the manual modeling stage. == Light modeling == Instead of considering only the physical model of a solid, IBMR methods usually focus more on light modeling. The fundamental concept behind IBMR is the plenoptic illumination function which is a parametrisation of the light field. The plenoptic function describes the light rays contained in a given volume. It can be represented with seven dimensions: a ray is defined by its position ( x , y , z ) {\displaystyle (x,y,z)} , its orientation ( θ , ϕ ) {\displaystyle (\theta ,\phi )} , its wavelength ( λ ) {\displaystyle (\lambda )} and its time ( t ) {\displaystyle (t)} : P ( x , y , z , θ , ϕ , λ , t ) {\displaystyle P(x,y,z,\theta ,\phi ,\lambda ,t)} . IBMR methods try to approximate the plenoptic function to render a novel set of two-dimensional images from another. Given the high dimensionality of this function, practical methods place constraints on the parameters in order to reduce this number (typically to 2 to 4). == IBMR methods and algorithms == View morphing generates a transition between images Panoramic imaging renders panoramas using image mosaics of individual still images Lumigraph relies on a dense sampling of a scene Space carving generates a 3D model based on a photo-consistency check

  Read more →
• 

  Color clock

  The color clock, or color timer, is a part of the video circuitry of computer graphics hardware that works with analog color television systems. The clock is timed to match the timing of the color standard it works with, typically NTSC or PAL, ensuring that the data being read from the computer memory to create the image on-screen is in sync with the display. Depending on the speed of the color clock, the product of the resolution and number of colors is defined. Slow color clocks of many early games consoles and home computers resulted in limited color palettes at the highest resolutions.

  Read more →