Martial artsMartial arts are codified systems and traditions of combat practiced for a number of reasons such as self-defense; military and law enforcement applications; competition; physical, mental, and spiritual development; entertainment; and the preservation of a nation's intangible cultural heritage. Martial arts is an English translation of the Japanese word "武芸 bu-gei". Literally, it refers to "武 martial" and "芸 arts".
Chinese martial artsChinese martial arts, often called by the umbrella terms kung fu (ˈkʌŋ_ˈfuː; ), kuoshu () or wushu (), are multiple fighting styles that have developed over the centuries in Ancient China . These fighting styles are often classified according to common traits, identified as "families" of martial arts. Examples of such traits include Shaolinquan () physical exercises involving All Other Animals () mimicry or training methods inspired by Old Chinese philosophies, religions and legends.
Indian martial artsIndian martial arts refers to the fighting systems of the Indian subcontinent. A variety of terms are used for the English phrases "Indian martial arts", deriving from ancient sources. While they may seem to imply specific disciplines (e.g. archery, armed combat), by Classical times they were used generically for all fighting systems. Among the most common terms today, śastra-vidyā, is a compound of the words (weapon) and (knowledge).
Historical European martial artsHistorical European martial arts (HEMA) are martial arts of European origin, particularly using arts formerly practised, but having since died out or evolved into very different forms. While there is limited surviving documentation of the martial arts of classical antiquity (such as Greek wrestling or gladiatorial combat), surviving dedicated technical treatises or martial arts manuals date to the Late Middle Ages and the early modern period. For this reason, the focus of HEMA is de facto on the period of the half-millennium of ca.
Japanese martial artsJapanese martial arts refers to the variety of martial arts native to the country of Japan. At least three Japanese terms (budō, bujutsu, and bugei) are used interchangeably with the English phrase Japanese martial arts. The usage of the term budō (武道) to mean martial arts is a modern one: historically the term meant a way of life encompassing physical, spiritual and moral dimensions with a focus on self-improvement, fulfillment or personal growth. The terms bujutsu (武術) and bugei (武芸) have different meanings from budō, at least historically speaking.
Embodied cognitionEmbodied cognition is the theory that many features of cognition, whether human or otherwise, are shaped by aspects of an organism's entire body. The cognitive features include high-level mental constructs (such as concepts and categories) and performance on various cognitive tasks (such as reasoning or judgment). The bodily aspects involve the motor system, the perceptual system, the bodily interactions with the environment (situatedness), and the assumptions about the world built the functional structure of organism's brain and body.
Mixed martial artsMixed martial arts (MMA) is a full-contact combat sport based on striking, grappling and ground fighting, incorporating techniques from various combat sports from around the world. The first documented use of the term mixed martial arts was in a review of UFC 1 by television critic Howard Rosenberg in 1993. During the early 20th century, various interstylistic contests took place throughout Japan and in the countries of the Four Asian Tigers.
Ontology (information science)In information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject. Every academic discipline or field creates ontologies to limit complexity and organize data into information and knowledge.
Ontology engineeringIn computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF.
Upper ontologyIn information science, an upper ontology (also known as a top-level ontology, upper model, or foundation ontology) is an ontology (in the sense used in information science) which consists of very general terms (such as "object", "property", "relation") that are common across all domains. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Feature engineeringFeature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data. Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific- based feature engineering has become obsolete for vision and speech processing.
Ontology languageIn computer science and artificial intelligence, ontology languages are formal languages used to construct ontologies. They allow the encoding of knowledge about specific domains and often include reasoning rules that support the processing of that knowledge. Ontology languages are usually declarative languages, are almost always generalizations of frame languages, and are commonly based on either first-order logic or on description logic.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Online machine learningIn computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.
MethodologyIn its most common sense, methodology is the study of research methods. However, the term can also refer to the methods themselves or to the philosophical discussion of associated background assumptions. A method is a structured procedure for bringing about a certain goal, like acquiring knowledge or verifying knowledge claims. This normally involves various steps, like choosing a sample, collecting data from this sample, and interpreting the data. The study of methods concerns a detailed description and analysis of these processes.
OntologyIn metaphysics, ontology is the philosophical study of being, as well as related concepts such as existence, becoming, and reality. Ontology addresses questions like how entities are grouped into and which of these entities exist on the most fundamental level. Ontologists often try to determine what the categories or highest kinds are and how they form a system of categories that encompasses the classification of all entities. Commonly proposed categories include substances, properties, relations, states of affairs, and events.
Active learning (machine learning)Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called teacher or oracle. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels.
Tacit knowledgeTacit knowledge or implicit knowledge—as opposed to formal, codified or explicit knowledge—is knowledge that is difficult to express or extract; therefore it is more difficult to transfer to others by means of writing it down or verbalizing it. This can include motor skills, personal wisdom, experience, insight, and intuition. For example, knowing that London is in the United Kingdom is a piece of explicit knowledge; it can be written down, transmitted, and understood by a recipient.
Situated cognitionSituated cognition is a theory that posits that knowing is inseparable from doing by arguing that all knowledge is situated in activity bound to social, cultural and physical contexts. Situativity theorists suggest a model of knowledge and learning that requires thinking on the fly rather than the storage and retrieval of conceptual knowledge. In essence, cognition cannot be separated from the context. Instead knowing exists, in situ, inseparable from context, activity, people, culture, and language.
