objects, as well as telecommunication networks used to organize the interaction of critical information infrastructure objects with each other315.
Cross-entropy is a generalization of Log Loss to multi-class classification problems. Cross-entropy quantifies the difference between two probability distributions. See also perplexity316.
Crossover (also recombination) in genetic algorithms and evolutionary computation, a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and analogous to the crossover that happens during sexual reproduction in biological organisms. Solutions can also be generated by cloning an existing solution, which is analogous to asexual reproduction. Newly generated solutions are typically mutated before being added to the population317.
Cross-Validation (k-fold Cross-Validation, Leave-p-out Cross-Validation) is a collection of processes designed to evaluate how the results of a predictive model will generalize to new data sets. k-fold Cross-Validation; Leave-p-out Cross-Validation318.
Cryogenic freezing (cryonics, human cryopreservation) is a technology of preserving in a state of deep cooling (using liquid nitrogen) the head or body of a person after his death with the intention to revive them in the future319.
Cyber-physical systems are intelligent networked systems with built-in sensors, processors and drives that are designed to interact with the physical environment and support the operation of computer information systems in real time320.
«D»
Darkforest is a computer go program, based on deep learning techniques using a convolutional neural network. Its updated version Darkforest2 combines the techniques of its predecessor with Monte Carlo tree search. The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them. With the update, the system is known as Darkforest3321.
Dartmouth workshop – the Dartmouth Summer Research Project on Artificial Intelligence was the name of a 1956 summer workshop now considered by many (though not all) to be the seminal event for artificial intelligence as a field322.
Data analysis is obtaining an understanding of data by considering samples, measurement, and visualization. Data analysis can be particularly useful when a dataset is first received, before one builds the first model. It is also crucial in understanding experiments and debugging problems with the system323.
Data analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption324.
Data augmentation in data analysis are techniques used to increase the amount of data. It helps reduce overfitting when training a machine learning325.
Data Cleaning is the process of identifying, correcting, or removing inaccurate or corrupt data records326.
Data Curation – includes the processes related to the organization and management of data which is collected from various sources327.
Data entry – the process of converting verbal or written responses to electronic form328.
Data fusion — the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source329.
Data Integration involves the combination of data residing in different resources and then the supply in a unified view to the users. Data integration is in high demand for both commercial and scientific domains in which they need to merge the data and research results from different repositories330.
Data is a collection of qualitative and quantitative variables. It contains the information that is represented numerically and needs to be analyzed.
Data Lake is a type of data repository that stores data in its natural format and relies on various schemata and structure to index the data331.
Data markup is the stage of processing structured and unstructured data, during which data (including text documents, photo and video images) are assigned identifiers that reflect the type of data (data classification), and (or) data is interpreted to solve a specific problem, in including using machine learning methods (National Strategy for the Development of Artificial Intelligence for the period up to 2030)332.
Data Mining is the process of data analysis and information extraction from large amounts of datasets with machine learning, statistical approaches. and many others333.
Data parallelism is a way of scaling training or inference that replicates an entire model onto multiple devices and then passes a subset of the input data to each device. Data parallelism can enable training and inference on very large batch sizes; however, data parallelism requires that the model be small enough to fit on all devices. See also model parallelism334.
Data Processing Unit (DPU) is a programmable specialized electronic circuit with hardware accelerated data processing for data-oriented computing335.
Data protection is the process of protecting data and involves the relationship between the collection and dissemination of data and technology, the public perception and expectation of privacy and the political and legal underpinnings surrounding that data. It aims to strike a balance between individual privacy rights while still allowing data to be used for business purposes336.
Data Refinement is used to convert an abstract data model in terms of sets for example into implementable data structures such as arrays337.
Data Science is a broad grouping of mathematics, statistics, probability, computing, data visualization to extract knowledge from
Критическая информационная инфраструктура РФ [Электронный ресурс] http://government.ru URL: http://government.ru/docs/all/112572/ ФЗ №187 от 26.07.2017 «О безопасности критической информационной инфраструктуры РФ» (дата обращения: 04.05.2023)
Cross-entropy [Электронный ресурс] https://helenkapatsa.ru URL: https://www.helenkapatsa.ru/kross-entropiia/ (дата обращения: 16.02.2022)
Crossover [Электронный ресурс] https://brainly.in URL: https://brainly.in/question/5802477 (дата обращения 28.02.2022)
Перекрёстная проверка [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Перекрёстная_проверка (дата обращения: 20.06.2023)
Криогенная заморозка [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Крионика (дата обращения: 04.05.2023)
Киберфизические системы [Электронный ресурс] https://ulgov.ru URL: https://ulgov.ru/page/index/permlink/id/14949/ (дата обращения: 02.05.2023)
Darkforest [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Darkforest (дата обращения: 28.06.2023)
Dartmouth workshop [Электронный ресурс] https://static.hlt.bme.hu URL: https://static.hlt.bme.hu/semantics/external/pages/John_McCarthy/en.wikipedia.org/wiki/Dartmouth_workshop.html (дата обращения: 16.04.2023)
Data analysis [Электронный ресурс] https://dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1727524 (дата обращения: 16.02.2022)
Data analytics [Электронный ресурс] www.investopedia.com (дата обращения: 07.07.2022) URL: https://www.investopedia.com/terms/d/data-analytics.asp
Data augmentation [Электронный ресурс] https://ibm.com URL: https://www.ibm.com/docs/ru/oala/1.3.5?topic=SSPFMY_1.3.5/com.ibm.scala.doc/config/iwa_cnf_scldc_scl_dc_ovw.html (дата обращения: 18.02.2022)
Очистка данных [Электронный ресурс] https://ru.wikipedia.org URL: https://ru.wikipedia.org/wiki/Очистка_данных (дата обращения: 20.06.2023)
Data Curation [Электронный ресурс] www.geeksforgeeks.org URL: https://www.geeksforgeeks.org/data-curation-lifecycle/ (дата обращения 22.02.2022)
Data entry [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (дата обращения: 07.07.2022)
Data fusion [Электронный ресурс] www.researchgate.net URL: https://www.researchgate.net/post/what_is_the_difference_between_Data_integration_and_data_fusion (дата обращения 14.03.2022)
Data Integration [Электронный ресурс] https://ibm.com URL: https://www.ibm.com/ru-ru/analytics/data-integration (дата обращения: 18.02.2022)
Data Lake [Электронный ресурс] https://bigdataschool.ru URL: https://www.bigdataschool.ru/wiki/data-lake (дата обращения: 17.02.2022)
Разметка данных [Электронный ресурс] https://cdto.wiki URL: https://cdto.wiki/Разметка_данных Указ Президента РФ от 10.10.2019 №490 «О развитии искусственного интеллекта в РФ» (дата обращения: 29.06.2023)
Data Mining [Электронный ресурс] https://bigdataschool.ru URL: https://www.teradata.ru/Glossary/What-is-Data-Mining (дата обращения: 17.02.2022)
Data parallelism [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#data-parallelism (дата обращения: 20.06.2023)
Data Processing Unit (DPU) [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/Data_processing_unit (дата обращения: 11.07.2023)
Data protection [Электронный ресурс] www.techopedia.com URL: https://www.techopedia.com/definition/29406/data-protection (дата обращения: 07.07.2022)
Data Refinement [Электронный ресурс] www.atscale.com URL: https://www.atscale.com/blog/what-is-data-extraction/ (дата обращения 12.01.2022)
