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In mathematics, a morphism is a concept of category theory that generalizes structure-preserving maps such as homomorphism between algebraic structures, functions from a set to another set, and continuous functions between topological spaces. Although many examples of morphisms are structure-preserving maps, morphisms ...	Wikipedia/Morphism_(category_theory)
This is a glossary of arithmetic and diophantine geometry in mathematics, areas growing out of the traditional study of Diophantine equations to encompass large parts of number theory and algebraic geometry. Much of the theory is in the form of proposed conjectures, which can be related at various levels of generality....	Wikipedia/Function_field_analogy
Sora is a text-to-video model developed by OpenAI. The model generates short video clips based on user prompts, and can also extend existing short videos. Sora was released publicly for ChatGPT Plus and ChatGPT Pro users in December 2024. == History == Several other text-to-video generating models had been created pr...	Wikipedia/Sora_(text-to-video_model)
Veo is a text-to-video model developed by Google DeepMind and announced in May 2024. As a generative AI model, it creates videos based on user prompts. Veo 3, released in May 2025, can also generate accompanying audio. == Development == In May 2024, a multimodal video generation model called Veo was announced at Goog...	Wikipedia/Veo_(text-to-video_model)
Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular drug design. Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form the edges. In addition...	Wikipedia/Graph_neural_network
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure and functions of biological neural networks. A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in ...	Wikipedia/Neural_network_(machine_learning)
Kuaishou Technology (Chinese: 快手; lit. 'quick hand') is a Chinese publicly traded partly state-owned holding company based in Haidian District, Beijing, that was founded in 2011 by Hua Su (宿华) and Cheng Yixiao (程一笑). The company, listed on the Hong Kong Stock Exchange, is known for developing a mobile app for sharing u...	Wikipedia/Kling_(text-to-video_model)
Robotic control is the system that contributes to the movement of robots. This involves the mechanical aspects and programmable systems that makes it possible to control robots. Robotics can be controlled by various means including manual, wireless, semi-autonomous (a mix of fully automatic and wireless control), and f...	Wikipedia/Robot_control
In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the non-negative part of its argument, i.e., the ramp function: ReLU ⁡ ( x ) = x ...	Wikipedia/Rectifier_(neural_networks)
Chinchilla is a family of large language models (LLMs) developed by the research team at Google DeepMind, presented in March 2022. == Models == It is named "chinchilla" because it is a further development over a previous model family named Gopher. Both model families were trained in order to investigate the scaling l...	Wikipedia/Chinchilla_(language_model)
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step,...	Wikipedia/Expectation–maximization_algorithm
Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". == Definition and motivation == In multi-agent reinforcement learning experiments, researchers try to optimize the performance of a learning ...	Wikipedia/Self-play_(reinforcement_learning_technique)
CURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances. == Drawbacks of traditional algorithms == The popular K-means clustering a...	Wikipedia/CURE_algorithm
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zer...	Wikipedia/Generative_adversarial_network
Vapnik–Chervonenkis theory (also known as VC theory) was developed during 1960–1990 by Vladimir Vapnik and Alexey Chervonenkis. The theory is a form of computational learning theory, which attempts to explain the learning process from a statistical point of view. == Introduction == VC theory covers at least four part...	Wikipedia/Vapnik–Chervonenkis_theory
Reasoning language models (RLMs) are large language models that have been further trained to solve multi-step reasoning tasks. These models perform better on logical, mathematical or programmatic tasks than traditional autoregressive LLMs, have the ability to backtrack, and employ test-time compute as an additional sca...	Wikipedia/Reasoning_language_model
Gemini is a family of multimodal large language models (LLMs) developed by Google DeepMind, and the successor to LaMDA and PaLM 2. Comprising Gemini Ultra, Gemini Pro, Gemini Flash, and Gemini Nano, it was announced on December 6, 2023, positioned as a competitor to OpenAI's GPT-4. It powers the chatbot of the same nam...	Wikipedia/Gemini_(language_model)
In machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by long short-term memory (LSTM) recurrent neural...	Wikipedia/Highway_network
Ideogram is a freemium text-to-image model developed by Ideogram, Inc. using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The model is capable of generating legible text in the images compared to other text-to-image models. == History == Ideogram was foun...	Wikipedia/Ideogram_(text-to-image_model)
Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving ...	Wikipedia/Meta-learning_(computer_science)
A generative pre-trained transformer (GPT) is a type of large language model (LLM) and a prominent framework for generative artificial intelligence. It is an artificial neural network that is used in natural language processing by machines. It is based on the transformer deep learning architecture, pre-trained on large...	Wikipedia/Generative_pre-trained_transformer
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are op...	Wikipedia/Multi-agent_reinforcement_learning
Imagen is a series of text-to-image models developed by Google DeepMind. They were developed by Google Brain until the company's merger with DeepMind in April 2023. Imagen is primarily used to generate images from text prompts, similar to Stability AI's Stable Diffusion, OpenAI's DALL-E, or Midjourney. The original ver...	Wikipedia/Imagen_(text-to-image_model)
A sigmoid function is any mathematical function whose graph has a characteristic S-shaped or sigmoid curve. A common example of a sigmoid function is the logistic function, which is defined by the formula σ ( x ) = 1 ...	Wikipedia/Sigmoid_function
Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves training agents to make decisions by interacting with an environment to maximize cumulative rewards, while using deep neural networks to represent policies, value fu...	Wikipedia/End-to-end_reinforcement_learning
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special...	Wikipedia/Bayesian_network
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). Given ...	Wikipedia/Loss_functions_for_classification
A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition, and won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) of that yea...	Wikipedia/Residual_neural_network
Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves training agents to make decisions by interacting with an environment to maximize cumulative rewards, while using deep neural networks to represent policies, value fu...	Wikipedia/Deep_reinforcement_learning
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process. The goal of diffusion models is ...	Wikipedia/Diffusion_model
The Latent Diffusion Model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images. The LDM is an impro...	Wikipedia/Latent_diffusion_model
Dream Machine is a text-to-video model created by Luma Labs and launched in June 2024. It generates video output based on user prompts or still images. Dream Machine has been noted for its ability to realistically capture motion, while some critics have remarked upon the lack of transparency about its training data. Up...	Wikipedia/Dream_Machine_(text-to-video_model)
In economics, a random utility model (RUM), also called stochastic utility model, is a mathematical description of the preferences of a person, whose choices are not deterministic, but depend on a random state variable. == Background == A basic assumption in classic economics is that the choices of a rational person...	Wikipedia/Random_utility_model
In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition probability distribution (or transition mo...	Wikipedia/Model-free_(reinforcement_learning)
The softmax function, also known as softargmax: 184 or normalized exponential function,: 198 converts a tuple of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and is used in multinomial logistic regression. The softmax fu...	Wikipedia/Softmax_function
Claude is a family of large language models developed by Anthropic. The first model was released in March 2023. The Claude 3 family, released in March 2024, consists of three models: Haiku, optimized for speed; Sonnet, which balances capability and performance; and Opus, designed for complex reasoning tasks. These mode...	Wikipedia/Claude_(language_model)
Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights to obtain outputs (inputs-to-output): feedforward. Recurrent neural networks, or neural networks with loops allow information from later processing stages to fee...	Wikipedia/Feedforward_neural_network
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning. In classical reinforcement learning, an i...	Wikipedia/Reinforcement_learning_from_human_feedback
Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of large language models (LLMs) released by Meta AI starting in February 2023. The latest version is Llama 4, released in April 2025. Llama models come in different sizes, ranging from 1 billion to 2 trillion parameters. Initially only a found...	Wikipedia/Llama_(language_model)
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs was founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts. == History == Bl...	Wikipedia/Flux_(text-to-image_model)
Grok is a generative artificial intelligence chatbot developed by xAI. Based on the large language model (LLM) of the same name, it was launched in November 2023 as an initiative by Elon Musk. Grok is integrated on the social media platform X, formerly known as Twitter, and has apps for iOS and Android. The chatbot was...	Wikipedia/Aurora_(text-to-image_model)
Policy gradient methods are a class of reinforcement learning algorithms. Policy gradient methods are a sub-class of policy optimization methods. Unlike value-based methods which learn a value function to derive a policy, policy optimization methods directly learn a policy function π ...	Wikipedia/Policy_gradient_method
The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous problems, with either a static or noisy objective. The method approximates the optimal importance sampling estimator by repeating two phases: Draw a sample from a probab...	Wikipedia/Cross-entropy_method
The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the c...	Wikipedia/Transformer_(machine_learning_model)
A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. Convolution-based networks are th...	Wikipedia/Convolutional_neural_network
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improved the state-of-the-art ...	Wikipedia/BERT_(language_model)
Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented in 1999 by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander. Its basic idea is similar to DBSCAN, but it addresses one of DBSCAN's major weaknesses: the...	Wikipedia/OPTICS_algorithm
Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series, where the order of elements is important. Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the outpu...	Wikipedia/Recurrent_neural_network
The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear. Modern activation functions include the logistic (sig...	Wikipedia/Activation_function
A text-to-video model is a machine learning model that uses a natural language description as input to produce a video relevant to the input text. Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models. == Models =...	Wikipedia/Text-to-video_model
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership. An obje...	Wikipedia/K-nearest_neighbors_algorithm
In behavioral psychology, reinforcement refers to consequences that increase the likelihood of an organism's future behavior, typically in the presence of a particular antecedent stimulus. For example, a rat can be trained to push a lever to receive food whenever a light is turned on; in this example, the light is the ...	Wikipedia/Reinforcement
The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the c...	Wikipedia/Transformer_(deep_learning_architecture)
A vision transformer (ViT) is a transformer designed for computer vision. A ViT decomposes an input image into a series of patches (rather than text into tokens), serializes each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication. These vector embeddings are then processed by a ...	Wikipedia/Vision_transformer
The value function of an optimization problem gives the value attained by the objective function at a solution, while only depending on the parameters of the problem. In a controlled dynamical system, the value function represents the optimal payoff of the system over the interval [t, t1] when started at the time-t sta...	Wikipedia/Value_function
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields...	Wikipedia/Statistical_learning_theory
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. Like the original Transformer model, T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text. T5 models are usually pretrained ...	Wikipedia/T5_(language_model)
The actor-critic algorithm (AC) is a family of reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods, and value-based RL algorithms such as value iteration, Q-learning, SARSA, and TD learning. An AC algorithm consists of two main components: an "actor" that deter...	Wikipedia/Actor-critic_algorithm
BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. BLOOM was trained on approximately 366 billion (1....	Wikipedia/BLOOM_(language_model)
An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can p...	Wikipedia/Echo_state_network
Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes as the main information carrier. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons i...	Wikipedia/Spiking_neural_network
In mathematics, the Shimizu L-function, introduced by Hideo Shimizu (1963), is a Dirichlet series associated to a totally real algebraic number field. Michael Francis Atiyah, H. Donnelly, and I. M. Singer (1983) defined the signature defect of the boundary of a manifold as the eta invariant, the value as s=0 of their e...	Wikipedia/Shimizu_L-function
In mathematics, the explicit formulae for L-functions are relations between sums over the complex number zeroes of an L-function and sums over prime powers, introduced by Riemann (1859) for the Riemann zeta function. Such explicit formulae have been applied also to questions on bounding the discriminant of an algeb...	Wikipedia/Explicit_formulae_for_L-functions
In mathematics, the logarithmic integral function or integral logarithm li(x) is a special function. It is relevant in problems of physics and has number theoretic significance. In particular, according to the prime number theorem, it is a very good approximation to the prime-counting function, which is defined as the ...	Wikipedia/Logarithmic_integral_function
In mathematics, the floor function is the function that takes as input a real number x, and gives as output the greatest integer less than or equal to x, denoted ⌊x⌋ or floor(x). Similarly, the ceiling function maps x to the least integer greater than or equal to x, denoted ⌈x⌉ or ceil(x). For example, for floor: ⌊2.4...	Wikipedia/Floor_function
Oppermann's conjecture is an unsolved problem in mathematics on the distribution of prime numbers. It is closely related to but stronger than Legendre's conjecture, Andrica's conjecture, and Brocard's conjecture. It is named after Danish mathematician Ludvig Oppermann, who announced it in an unpublished lecture in Marc...	Wikipedia/Oppermann's_conjecture
In mathematics, the explicit formulae for L-functions are relations between sums over the complex number zeroes of an L-function and sums over prime powers, introduced by Riemann (1859) for the Riemann zeta function. Such explicit formulae have been applied also to questions on bounding the discriminant of an algeb...	Wikipedia/Explicit_formulae_(L-function)
The Meissel–Lehmer algorithm (after Ernst Meissel and Derrick Henry Lehmer) is an algorithm that computes exact values of the prime-counting function. == Description == The problem of counting the exact number of primes less than or equal to x, without actually listing them all, dates from Legendre. He observed from...	Wikipedia/Meissel–Lehmer_algorithm
In mathematics, the Chebyshev function is either a scalarising function (Tchebycheff function) or one of two related functions. The first Chebyshev function ϑ (x) or θ (x) is given by ϑ ( x ) = ∑ p ≤ ...	Wikipedia/Chebyshev_function
In number theory, the totient summatory function Φ ( n ) {\displaystyle \Phi (n)} is a summatory function of Euler's totient function defined by Φ ( n ) := ∑ ...	Wikipedia/Totient_summatory_function
In mathematics, Carmichael's totient function conjecture concerns the multiplicity of values of Euler's totient function φ(n), which counts the number of integers less than and coprime to n. It states that, for every n there is at least one other integer m ≠ n such that φ(m) = φ(n). Robert Carmichael first stated this...	Wikipedia/Carmichael's_totient_function_conjecture
In number theory, a branch of mathematics, the Carmichael function λ(n) of a positive integer n is the smallest positive integer m such that a m ≡ 1 ( mod n ...	Wikipedia/Carmichael_function
In number theory, an arithmetic, arithmetical, or number-theoretic function is generally any function whose domain is the set of positive integers and whose range is a subset of the complex numbers. Hardy & Wright include in their definition the requirement that an arithmetical function "expresses some arithmetical pro...	Wikipedia/Arithmetical_function
In number theory, the Dedekind psi function is the multiplicative function on the positive integers defined by ψ ( n ) = n ∏ p \| n ...	Wikipedia/Dedekind_psi_function
In abstract algebra, a subset S {\displaystyle S} of a field L {\displaystyle L} is algebraically independent over a subfield K {\displaystyle K} if the elements of ...	Wikipedia/Algebraic_independence
In mathematics, specifically in transcendental number theory and Diophantine approximation, Siegel's lemma refers to bounds on the solutions of linear equations obtained by the construction of auxiliary functions. The existence of these polynomials was proven by Axel Thue; Thue's proof used what would be translated fro...	Wikipedia/Siegel's_lemma
Cantor's first set theory article contains Georg Cantor's first theorems of transfinite set theory, which studies infinite sets and their properties. One of these theorems is his "revolutionary discovery" that the set of all real numbers is uncountably, rather than countably, infinite. This theorem is proved using Cant...	Wikipedia/Georg_Cantor's_first_set_theory_article
In number theory, Euler's totient function counts the positive integers up to a given integer n that are relatively prime to n. It is written using the Greek letter phi as φ ( n ) {\displaystyle \varphi (n)} or ϕ ( ...	Wikipedia/Euler_totient_function
In field theory, a simple extension is a field extension that is generated by the adjunction of a single element, called a primitive element. Simple extensions are well understood and can be completely classified. The primitive element theorem provides a characterization of the finite simple extensions. == Definition...	Wikipedia/Primitive_element_(field_theory)
In mathematics, the study of special values of L-functions is a subfield of number theory devoted to generalising formulae such as the Leibniz formula for π, namely 1 − 1 3 + ...	Wikipedia/Tamagawa_number_conjecture
The Riemann zeta function or Euler–Riemann zeta function, denoted by the Greek letter ζ (zeta), is a mathematical function of a complex variable defined as ζ ( s ) = ∑ n = 1 ...	Wikipedia/Riemann_zeta-function
In automata theory, a tree is a particular way of representing a tree structure as sequences of natural numbers. For example, each node of the tree is a word over set of natural numbers ( N {\displaystyle \mathbb {N} } ), which helps this definition to be ...	Wikipedia/Tree_(automata_theory)
In the theory of formal languages, the pumping lemma for regular languages is a lemma that describes an essential property of all regular languages. Informally, it says that all sufficiently long strings in a regular language may be pumped—that is, have a middle section of the string repeated an arbitrary number of tim...	Wikipedia/Pumping_lemma_for_regular_languages
Transition refers to a computer science paradigm in the context of communication systems which describes the change of communication mechanisms, i.e., functions of a communication system, in particular, service and protocol components. In a transition, communication mechanisms within a system are replaced by functional...	Wikipedia/Transition_(computer_science)
Processor design is a subfield of computer science and computer engineering (fabrication) that deals with creating a processor, a key component of computer hardware. The design process involves choosing an instruction set and a certain execution paradigm (e.g. VLIW or RISC) and results in a microarchitecture, which mig...	Wikipedia/Hardware_design
Static application security testing (SAST) is used to secure software by reviewing the source code of the software to identify sources of vulnerabilities. Although the process of checking programs by reading their code (modernly known as static program analysis) has existed as long as computers have existed, the techn...	Wikipedia/Static_application_security_testing
In physical security and information security, access control (AC) is the action of deciding whether a subject should be granted or denied access to an object (for example, a place or a resource). The act of accessing may mean consuming, entering, or using. It is often used interchangeably with authorization, although ...	Wikipedia/Access_control
Threat modeling is a process by which potential threats, such as structural vulnerabilities or the absence of appropriate safeguards, can be identified and enumerated, and countermeasures prioritized. The purpose of threat modeling is to provide defenders with a systematic analysis of what controls or defenses need to ...	Wikipedia/Threat_model
Dynamic application security testing (DAST) represents a non-functional testing process to identify security weaknesses and vulnerabilities in an application. This testing process can be carried out either manually or by using automated tools. Manual assessment of an application involves human intervention to identify ...	Wikipedia/Dynamic_application_security_testing
A web application firewall (WAF) is a specific form of application firewall that filters, monitors, and blocks HTTP traffic to and from a web service. By inspecting HTTP traffic, it can prevent attacks exploiting a web application's known vulnerabilities, such as SQL injection, cross-site scripting (XSS), file inclusio...	Wikipedia/Web_application_firewall
A modeling language is any artificial language that can be used to express data, information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure of a programming language. == Overview == A modeling lan...	Wikipedia/Modeling_languages
Computer facial animation is primarily an area of computer graphics that encapsulates methods and techniques for generating and animating images or models of a character face. The character can be a human, a humanoid, an animal, a legendary creature or character, etc. Due to its subject and output type, it is also rela...	Wikipedia/Face_Modeling_Language
Generative Modelling Language (GML) in computer graphics and generative computer programming is a very simple programming language for the concise description of complex 3D shapes. It follows the "Generative Modelling" paradigm, where complex datasets are represented by "lists of operations" rather than by lists of obj...	Wikipedia/Generative_Modelling_Language
The Lifecycle Modeling Language (LML) is an open-standard modeling language designed for systems engineering. It supports the full lifecycle: conceptual, utilization, support and retirement stages. Along with the integration of all lifecycle disciplines including, program management, systems and design engineering, ver...	Wikipedia/Lifecycle_Modeling_Language
The term process model is used in various contexts. For example, in business process modeling the enterprise process model is often referred to as the business process model. == Overview == Process models are processes of the same nature that are classified together into a model. Thus, a process model is a descriptio...	Wikipedia/Process_Modeling
A modeling perspective in information systems is a particular way to represent pre-selected aspects of a system. Any perspective has a different focus, conceptualization, dedication and visualization of what the model is representing. The traditional way to distinguish between modeling perspectives is structural, func...	Wikipedia/Modeling_perspective
A design pattern is the re-usable form of a solution to a design problem. The idea was introduced by the architect Christopher Alexander and has been adapted for various other disciplines, particularly software engineering. == Details == An organized collection of design patterns that relate to a particular field is...	Wikipedia/Design_patterns
In computer science, the process calculi (or process algebras) are a diverse family of related approaches for formally modelling concurrent systems. Process calculi provide a tool for the high-level description of interactions, communications, and synchronizations between a collection of independent agents or processes...	Wikipedia/Process_algebra
Fundamental modeling concepts (FMC) provide a framework to describe software-intensive systems. It strongly emphasizes the communication about software-intensive systems by using a semi-formal graphical notation that can easily be understood. == Introduction == FMC distinguishes three perspectives to look at a softwa...	Wikipedia/Fundamental_Modeling_Concepts
Analogical models are a method of representing a phenomenon of the world, often called the "target system" by another, more understandable or analysable system. They are also called dynamical analogies. Two open systems have analog representations (see illustration) if they are black box isomorphic systems. == Explan...	Wikipedia/Analogical_models

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