Directed Acyclic Graph (DAG) in computer science and mathematics, a directed acyclic graph is a finite directed graph with no directed cycles. It consists of finitely many vertices and edges, with each edge directed from one vertex to another, such that there is no way to start at any vertex and follow a consistently directed sequence of edges that eventually loops back to that starting vertex again[408 - Directed Acyclic Graph (DAG) [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Directed_acyclic_graph (дата обращения: 10.05.2023)].
Disaster tolerance is the ability of a system to restore an application on an alternate cluster when the primary cluster fails. Disaster tolerance is based on data replication and failover. Data replication is the copying of data from a primary cluster to a backup or secondary cluster[409 - Disaster tolerance [Электронный ресурс] https://docs.oracle.com URL: https://docs.oracle.com/cd/E19050-01/sun.cluster31/817-6543/auto29/index.html (дата обращения: 07.07.2022)].
Disclosure of information constituting a commercial secret is an action or inaction as a result of which information constituting a commercial secret, in any possible form (oral, written, other form, including using technical means) becomes known to third parties without the consent of the owner of such information, or contrary to an employment or civil law contract[410 - Разглашение информации, составляющей коммерческую тайну [Электронный ресурс] http://www.kremlin.ru URL: http://www.kremlin.ru/acts/bank/21227 Федеральный закон от 29 июля 2004 г. N 98-ФЗ «О коммерческой тайне», статья 3. Основные понятия, п.9 (дата обращения: 29.06.2023)].
Discrete feature is a feature with a finite set of possible values. For example, a feature whose values may only be animal, vegetable, or mineral is a discrete (or categorical) feature. Contrast with continuous feature[411 - Discrete feature [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#discrete-feature (дата обращения 22.03.2022)].
Discrete system is any system with a countable number of states. Discrete systems may be contrasted with continuous systems, which may also be called analog systems. A final discrete system is often modeled with a directed graph and is analyzed for correctness and complexity according to computational theory. Because discrete systems have a countable number of states, they may be described in precise mathematical models. A computer is a finite state machine that may be viewed as a discrete system. Because computers are often used to model not only other discrete systems but continuous systems as well, methods have been developed to represent real-world continuous systems as discrete systems. One such method involves sampling a continuous signal at discrete time intervals[412 - Discrete system [Электронный ресурс] www.semanticscholar.org URL: https://www.semanticscholar.org/topic/Discrete-system/272487 (дата обращения 22.03.2022)].
Discriminative model is a model that predicts labels from a set of one or more features. More formally, discriminative models define the conditional probability of an output given the features and weights; that is (output|features, weights). For example, a model that predicts whether an email is spam from features and weights is a discriminative model. The vast majority of supervised learning models, including classification and regression models, are discriminative models. Contrast with generative model[413 - Discriminative model [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#discriminative_model (дата обращения: 09.04.2023)].
Discriminator is a system that determines whether examples are real or fake. The subsystem within a generative adversarial network that determines whether the examples created by the generator are real or fake[414 - Discriminator [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#discriminator (дата обращения 22.03.2022)].
Disparate impact – making decisions about people that impact different population subgroups disproportionately. This usually refers to situations where an algorithmic decision-making process harms or benefits some subgroups more than others[415 - Disparate impact [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#disparate-impact (дата обращения: 11.05.2023)].
Disparate treatment – factoring subjects’ sensitive attributes into an algorithmic decision-making process such that different subgroups of people are treated differently[416 - Disparate treatment [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#disparate-treatment (дата обращения: 11.05.2023)].
Dissemination of information – actions aimed at obtaining information by an indefinite circle of persons or transferring information to an indefinite circle of persons[417 - Распространение информации [Электронный ресурс] http://www.kremlin.ru URL: http://www.kremlin.ru/acts/bank/24157 Федеральный закон от 27.07.2006 №149-ФЗ «Об информации, информационных технологиях и о защите информации», Статья 2. Основные понятия, п.9 (дата обращения: 29.06.2023)].
Dissemination of personal data – actions aimed at disclosing personal data to an indefinite circle of persons[418 - Распространение персональных данных [Электронный ресурс] http://letters.kremlin.ru URL: http://letters.kremlin.ru/info-service/acts/9 Федеральный закон от 27 июля 2006 г. №152-ФЗ «О персональных данных», Статья 3. Основные понятия, п. 5 (дата обращения: 29.06.2023)].
Distributed artificial intelligence (DAI) (also decentralized artificial intelligence) is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems[419 - Distributed artificial intelligence (DAI) [Электронный ресурс] https://ru.knowledgr.com URL: http://ru.knowledgr.com/00164495/ (дата обращения: 14.02.2022)].
Distributed registry technologies (Blockchain) are algorithms and protocols for decentralized storage and processing of transactions structured as a sequence of linked blocks without the possibility of their subsequent change[420 - Технологии распределенного реестра (блокчейн) [Электронный ресурс] https://dzen.ru URL: https://dzen.ru/a/Y_yfdHIFHgahdc-6 (дата обращения 04.07.2023)].
Distribution series are series of absolute and relative numbers that characterize the distribution of population units according to a qualitative (attributive) or quantitative attribute. Distribution series built on a quantitative basis are called variational[421 - Ряды распределения [Электронный ресурс] https://studref.com URL: https://studref.com/365279/pravo/ponyatie_ryadah_raspredeleniya_absolyutnyh_otnositelnyh_velichin (дата обращения: 30.06.2023)].
Divisive clustering – see hierarchical clustering[422 - Divisive clustering [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#divisive-clustering (дата обращения: 29.06.2023)],[423 - Divisive clustering [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/divisive-clustering (дата обращения: 29.06.2023)].
Documentation generically, any information on the structure, contents, and layout of a data file. Sometimes called «technical documentation» or «a codebook». Documentation may be considered a specialized form of metadata[424 - Documentation [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (дата обращения: 07.07.2022)].
Documented information – information recorded on a material carrier by means of documentation with details that make it possible to determine such information, or, in cases established by the legislation of the Russian Federation, its material carrier[425 - Документированная информация [Электронный ресурс] https://safe-surf.ru URL: https://safe-surf.ru/glossary/ru/835/ (дата обращения: 09.04.2023)].
Downsampling – overloaded term that can mean either of the following: Reducing the amount of information in a feature in order to train a model more efficiently. For example, before training an image recognition model, downsampling high-resolution images to a lower-resolution format. Training on a disproportionately low percentage of over-represented class examples in order to improve model training on under-represented classes. For example, in a class-imbalanced dataset, models tend to learn a lot about the majority class and not enough about the minority class. Downsampling helps balance the amount of training on the majority and minority classes[426 - Downsampling [Электронный ресурс] https://developers.google.com
https://developers.google.com/machine-learning/glossary#downsampling (дата обращения: 09.04.2023)].
Driver is computer software that allows other software (the operating system) to access the hardware of a device[427 - Драйвер [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Драйвер (дата обращения: 09.04.2023)].
Drone – unmanned aerial vehicle (unmanned aerial system)[428 - Дрон [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Беспилотный_летательный_аппарат (дата обращения: 09.04.2023)].
Dropout regularization is a form of regularization useful in training neural networks. Dropout regularization works by removing a random selection of a fixed number of the units in a network layer for a single gradient step. The more units dropped out, the stronger the regularization[429 - Dropout regularization [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#dropout-regularization (дата обращения: 30.06.2023)].
Dynamic epistemic logic (DEL) is a logical framework dealing with knowledge and information change. Typically, DEL focuses on situations involving multiple agents and studies how their knowledge changes when events occur[430 - Dynamic epistemic logic [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Dynamic_epistemic_logic (дата обращения: 09.04.2023)].
Dynamic model is a model that is trained online in a continuously updating fashion. That is, data is continuously entering the model[431 - Dynamic model [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/dynamic-model (дата обращения: 09.04.2023)],[432 - Динамическая модель [Электронный ресурс] https://kartaslov.ru URL: https://kartaslov.ru/карта-знаний/Динамическая+модель (дата обращения: 09.04.2023)].
«E»
Eager execution is a TensorFlow programming environment in which operations run immediately. By contrast, operations called in graph execution don’t run until they are explicitly evaluated. Eager execution is an imperative interface, much like the code in most programming languages. Eager execution programs are generally far easier to debug than graph execution programs[433 - Eager execution [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/eager-execution (дата обращения 06.07.2023)].
Eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system[434 - Eager learning [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Eager_learning (дата обращения 06.07.2023)].
Early stopping is a method for regularization that involves ending model training before training loss finishes decreasing. In early stopping, you end model training when the loss on a validation dataset starts to increase, that is, when generalization performance worsens[435 - Early stopping [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#early-stopping (дата обращения: 29.06.2023)].
Earth mover’s distance (EMD) is a measure of the relative similarity between two documents. The lower the value, the more similar the documents[436 - Earth mover’s distance (EMD) [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#earth-movers-distance-emd (дата обращения: 29.06.2023)].
Ebert test is a test which gauges whether a computer-based synthesized voice can tell a joke with sufficient skill to cause people to laugh. It was proposed by film critic Roger Ebert at the 2011 TED conference as a challenge to software developers to have a computerized voice master the inflections, delivery, timing, and intonations of a speaking human. The test is similar to the Turing test proposed by Alan Turing in 1950 as a way to gauge a computer’s ability to exhibit intelligent behavior by generating performance indistinguishable from a human being[437 - Ebert test [Электронный ресурс] https://detailedpedia.com URL: https://detailedpedia.com/wiki-Ebert_test (дата обращения 08.08.2021)].
Echo state network (ESN) is 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 (re) produce specific temporal patterns. The main interest of this network is that although its behaviour is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system[438 - Echo state network (ESN) [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Echo_state_network (дата обращения: 30.06.2023)].
Ecosystem of the digital economy is a partnership of organizations that ensures the constant interaction of their technological platforms, applied Internet services, analytical systems, information systems of state authorities of the Russian Federation, organizations and citizens[439 - Экосистема цифровой экономики [Электронный ресурс] https://cdto.wiki URL: https://cdto.wiki/Экосистема_цифровой_экономики (дата обращения: 10.07.2023)].
Edge computing is a subspecies of distributed computing in which information processing takes place in close proximity to the place where the data was received and will be consumed (for example, using phones and other consumer devices)[440 - Пограничные вычисления [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Граничные_вычисления (дата обращения: 26.06.2023)].
Electronic circuit is a product, a combination of individual electronic components, such as resistors, capacitors, diodes, transistors and integrated circuits, interconnected to perform any task or a circuit with conventional signs[441 - Электронная схема [Электронный ресурс] https://ppt-online.org URL: https://ppt-online.org/1164451, слайд 2 (дата обращения: 11.07.2023)],[442 - Электронная схема [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Электронная_схема (дата обращения: 11.07.2023)].
Electronic Data Interchange (EDI) is a series of standards and conventions for the transfer of structured digital information between organizations, based on certain regulations and formats of transmitted messages[443 - EDI Electronic Data Interchange [Электронный ресурс] www.igi-global.com URL: https://www.igi-global.com/dictionary/edi-electronic-data-interchange/9084 (дата обращения: 07.07.2022)].
Electronic government (e-Government) is a package of technologies and a set of related organizational measures, regulatory and legal support for organizing digital interaction between public authorities of various branches of government, citizens, organizations and other economic entities[444 - Электронное правительство (e-Government) [Электронный ресурс] https://it.kurganobl.ru URL: https://it.kurganobl.ru/activity/informatsionnoe-obshchestvo/ (дата обращения: 11.07.2023)].
Electronic industry is a set of organizations that perform scientific, technological and other work in the field of development, production, maintenance of operation, as well as providing services related to electronic and microelectronic products, respectively[445 - Электронная промышленность [Электронный ресурс] https://dzen.ru URL: https://dzen.ru/a/XjsrTkU_ezBirypQ (дата обращения: 11.07.2023)].
Electronic Medical Record (EMR) is electronic health record, is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different healthcare settings[446 - Electronic Medical Record (EMR) [Электронный ресурс] www.iomcworld.org URL: https://www.iomcworld.org/scholarly/electronic-health-record-journals-articles-ppts-list-4360.html (дата обращения 28.02.2022)].
Electronic state is a way of implementing the information aspects of state activity based on the use of IT systems, as well as a new type of state based on the use of this technology. In the Russian Federation, activities to create an «electronic state» are carried out within the framework of the federal target program «Electronic Russia»[447 - Электронное государство [Электронный ресурс] http://www2.rsuh.ru URL: http://www2.rsuh.ru/binary/2632865_61.1412699886.23632.doc (дата обращения: 11.07.2023)],[448 - Электронное государство [Электронный ресурс] https://tutor-web.susu.ru URL: https://tutor-web.susu.ru/wp-content/uploads/2017/06/1-E%60lGos.pdf (дата обращения: 11.07.2023)].
Eli5 environment is a Python environment that is used to debug and visualize machine learning models. By default, it supports several machine learning frameworks – Scikit-learn, XGBoost, LightGBM, CatBoost, lightning, Keras and so on. Eli5 also provides LIME and Permutation Importance models to test machine learning pipelines as black boxes[449 - Eli5 environment [Электронный ресурс] https://github.com URL: https://github.com/TeamHG-Memex/eli5 (дата обращения: 02.07.2023)].
ELIZA effect is a term used to discuss progressive artificial intelligence. It is the idea that people may falsely attach meanings of symbols or words that they ascribe to artificial intelligence in technologies[450 - ELIZA effect [Электронный ресурс] https://www.techopedia.com URL: https://www.techopedia.com/definition/19121/eliza-effect (дата обращения: 11.07.2023)].
Embedding (Word Embedding) is one instance of some mathematical structure contained within another instance, such as a group that is a subgroup[451 - Embedding [Электронный ресурс] https://appen.com URL: https://appen.com/ai-glossary/ (дата обращения 28.02.2022)].
Embedding space – the d-dimensional vector space that features from a higher-dimensional vector space are mapped to. Ideally, the embedding space contains a structure that yields meaningful mathematical results; for example, in an ideal embedding space, addition and subtraction of embeddings can solve word analogy tasks. The dot product of two embeddings is a measure of their similarity[452 - Embedding space [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#embedding-space (дата обращения: 28.03.2023)].
Embeddings is a categorical feature represented as a continuous-valued feature. Typically, an embedding is a translation of a high-dimensional vector into a low-dimensional space[453 - Embeddings [Электронный ресурс] https://nkj.ru URL: https://www.nkj.ru/open/36052/ (дата обращения: 09.02.2022)].
Embodied agent (also interface agent) is an intelligent agent that interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are also called embodied agents, although they have only virtual, not physical, embodiment[454 - Embodied agent [Электронный ресурс] https://scholar.uwindsor.ca URL: https://scholar.uwindsor.ca/cgi/viewcontent.cgi?article=8732&context=etd (дата обращения 28.02.2022)].
Embodied cognitive science is an interdisciplinary field of research, the aim of which is to explain the mechanisms underlying intelligent behavior. It comprises three main methodologies: 1) the modeling of psychological and biological systems in a holistic manner that considers the mind and body as a single entity, 2) the formation of a common set of general principles of intelligent behavior, and 3) the experimental use of robotic agents in controlled environments[455 - Embodied cognitive science [Электронный ресурс] https://psychology.fandom.com URL: https://psychology.fandom.com/wiki/Embodied_cognitive_science (дата обращения 14.03.2022)].
Empirical risk minimization (ERM) – choosing the function that minimizes loss on the training set. Contrast with structural risk minimization[456 - Empirical risk minimization (ERM) [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#empirical-risk-minimization-erm (дата обращения: 10.05.2023)],[457 - Минимизация эмпирического риска (МЭР) [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Минимизация_эмпирического_риска (дата обращения: 10.05.2023)].
Encoder in general, is any system that converts from a raw, sparse, or external representation into a more processed, denser, or more internal representation. Encoders are often a component of a larger model, where they are frequently paired with a decoder. Some Transformers pair encoders with decoders, though other Transformers use only the encoder or only the decoder. Some systems use the encoder’s output as the input to a classification or regression network. In sequence-to-sequence tasks, an encoder takes an input sequence and returns an internal state (a vector). Then, the decoder uses that internal state to predict the next sequence. Refer to Transformer for the definition of an encoder in the Transformer architecture[458 - Encoder [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#encoder (дата обращения: 03.05.2023)].
Encryption is the reversible transformation of information in order to hide from unauthorized persons, while providing, at the same time, authorized users access to it[459 - Encryption [Электронный ресурс] https://context.reverso.net URL: https://context.reverso.net/translation/english-russian/order+to+hide+from (дата обращения: 10.07.2023)],[460 - Шифрование [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Шифрование (дата обращения: 10.07.2023)].
End-to-end digital technologies is a set of technologies that are part of the digital economy: big data, neurotechnologies and artificial intelligence, distributed registry systems, quantum technologies, new production technologies, industrial Internet, robotics and sensor components, wireless communication technologies, virtual and augmented reality technologies[461 - Сквозные цифровые технологии [Электронный ресурс] http://sdo.krsk.irgups.ru URL: http://sdo.krsk.irgups.ru/pluginfile.php/20770/mod_resource/content/0/Сквозные технологии цифровой экономики. pdf (дата обращения: 02.07.2023)].
