• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта
Найдено 36 597 публикаций
Сортировка:
по названию
по году
Глава
Ryazanskaya G., Khudyakova M. In bk.: Proceedings of the LREC 2020 Workshop on: Resources and Processing of Linguistic, Para-linguistic and Extra-linguistic Data from People with Various Forms of Cognitive/Psychiatric/Developmental Impairments (RaPID-3). European Language Resources Association (ELRA), 2020. P. 98-107.
Добавлено: 2 февраля 2021
Глава
Solodova R., Staroverov V., Galatenko Vladimir et al. In bk.: Studies in Health Technology and Informatics. Vol. 220: Medicine Meets Virtual Reality 22. Netherlands: IOS Press, 2016. P. 383-389.
Добавлено: 22 мая 2016
Глава
Boris Ulitin, Eduard Babkin, Tatiana Babkina et al. In bk.: Lecture Notes in Business Information Processing. Iss. 365: Perspectives in Business Informatics Research. Switzerland: Springer, 2019. P. 59-73.
Добавлено: 30 сентября 2019
Глава
Konstantin Lomotin, Makarov I. In bk.: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers. Vol. 12602. Springer, 2021. P. 243-256.

We study non-reference image and video quality assessment methods, which are of great importance for computational video editing. The object of our work is image quality assessment (IQA) applicable for fast and robust frame-by-frame multipurpose video quality assessment (VQA) for short videos.

We present a complex framework for assessing the quality of images and videos. The scoring process consists of several parallel steps of metric collection with final score aggregation step. Most of the individual scoring models are based on deep convolutional neural networks (CNN). The framework can be flexibly extended or reduced by adding or removing these steps. Using Deep CNN-Based Blind Image Quality Predictor (DIQA) as a baseline for IQA, we proposed improvements based on two patching strategies, such as uniform patching and object-based patching, and add intelligent pre-training step with distortion classification.

We evaluated our model on three IQA benchmark image datasets (LIVE, TID2008, and TID2013) and manually collected short YouTube videos. We also consider interesting for automated video editing metrics used for video scoring based on the scale of a scene, face presence in frame and compliance of the shot transitions with the shooting rules. The results of this work are applicable to the development of intelligent video and image processing systems.

Добавлено: 9 апреля 2021
Глава
Badryzlova Y., Lyashevskaya O., Nikiforova A. In bk.: Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions 18th International Conference (Lecture Notes in Networks and Systems 332). Vol. 2. Springer, 2021. Ch. 8. P. 86-96.
Добавлено: 17 сентября 2021
Глава
Tutubalina E., Nikolenko S. I. In bk.: Mining Intelligence and Knowledge Exploration. 4th International Conference, MIKE 2016, Mexico City, Mexico, November 13 - 19, 2016, Revised Selected Papers. Iss. 10089. Cham: Springer, 2017. P. 174-184.

The advent of personalized medicine and wide-scale drug tests has led to the development of methods intended to automatically mine and extract information regarding drug reactions from user reviews. For medical purposes, it is often important to know demographic information on the authors of these reviews; however, existing studies usually either presuppose that this information is available or disregard the issue. We study automatic mining of demographic information from user-generated texts, comparing modern natural language processing techniques, including extensions of topic models and deep neural networks, for this problem on datasets mined from health-related web sites.

Добавлено: 8 июня 2017
Глава
Korukhova Y., Fastovets N. In bk.: Proceedings of the 15th UK CBR Workshop. L.: CMS Press, 2010. P. 35-44.
Добавлено: 15 ноября 2014
Глава
Skovoroda A., Gamaunov D. In bk.: 2017 15th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2017. P. 243-252.
Добавлено: 12 октября 2018
Глава
Scedrov A., Wang A., Moarref S. et al. In bk.: Network Protocols - Proceedings of the 21st IEEE International Conference ICNP 2013. IEEE, 2013.
Добавлено: 29 мая 2015
Глава
Zhigalova Maria, Sukhov Alexander. In bk.: Proceedings of the 10th Anniversary Spring/Summer Young Researchers’ Colloquium on Software Engineering (SYRCoSE 2016). M.: 2016. P. 135-140.
Добавлено: 25 июня 2016
Глава
Lopukhina A., Лопухин К. А., Носырев Г. В. In bk.: Quantitative approaches to the Russian language. Abingdon: Routledge, 2018. P. 79-94.
Добавлено: 11 октября 2016
Глава
Пономарева М. А., Milintsevich K., Artemova E. et al. In bk.: Proceedings of the First Workshop on Subword and Character Level Models in NLP. Stroudsburg, PA: Association for Computational Linguistics, 2017. P. 31-35.
Добавлено: 10 октября 2017
Глава
Sazontyev V. V. In bk.: Fundamental science and technology - promising developments III. Proceedings of the Conference. North Charleston, USA, 24-25.04.2014 (Материалы III международной научно-практической конференции "Фундаментальная наука и технологии - перспективные разработки"). Iss. 2. North Charleston: CreateSpace, 2014. P. 147-156.

Traditional object oriented way of programming includes objects and classes, where as result we have library of different modules, and scripts that use that library. Where group of the scripts developed for separate task. And even if that scripts are rather similar by their functionality they are different in part of values, functions that used from library and other features. That involves programming each script separately. Also you cant develop highly complicated script that will have all functionality, because in real life you will have problem with it support and further development, also it usually might be that each script is separate process on server side and it will take too much resource; or some functions can’t be in script when other do, that where programmer must do his architectural choice each time for each task. In this paper I reveal a good approach to solve this task. I split this task to architectural part and programming. Where architectural part solved as straightforward graph, and produces information for second, programming part. Programming part solved by using information from previous part and passes it to neural network, that as output produces functions that must be in scripts, and based on that information script will produce working group of script for particular task. Moreover, I experimentally compare traditional approach with this purposed one and reveal some essential shortcomings of traditional approach.

Добавлено: 15 июня 2016
Глава
Ingacheva A., Чукалина М. В., Николаев Д. П. et al. In bk.: Proceedings - 31th European Conference on Modelling and Simulation, ECMS 2017. 2017.
Добавлено: 27 октября 2017
Глава
Paperno D., Ryzhova D. In bk.: Language Documentation & Conservation Special Publication. Iss. 16: Methodological Tools for Linguistic Description and Typology. University of Hawaii Press, 2019. Ch. 5. P. 45-61.
Добавлено: 30 августа 2019
Глава
Demidovskij A. In bk.: Proceedings of 2020 International Conference on Electrical, Communication, and Computer Engineering. United States of America: IEEE, 2020.
Добавлено: 31 августа 2020
Глава
Ryzhova D., Melnik A. A., Ершов И. А. et al. In bk.: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2018”. 2018. P. 619-636.
Добавлено: 17 октября 2018
Глава
Kopotev M., Pivovarova L., Kochetkova N. A. et al. In bk.: NAACL HLT 2013 9th Workshop on Multiword Expressions MWE 2013 Proceedings of the Workhop. Atlanta: The Association for Computational Linguistic, 2013. Ch. 12. P. 73-81.

.

Добавлено: 28 июня 2013
Глава
Kuzmenko E., Mustakimova E. In bk.: Компьютерная лингвистика и интеллектуальные технологии. По материалам ежегодной Международной конференции "Диалог" (2015). M.: 2015. P. 388-398.
Добавлено: 30 июля 2015
Глава
Petrosyants K. O., Kozynko P. In bk.: Collection of Papers Presented at the 14th International Workshop on THERMal INvestigation of ICs and Systems (THERMINIC 2008). Rome: EDA Publishing Association, 2008. P. 76-79.

Автоматический электро-тепловой анализ включён в маршрут проектирования ПП Mentor Graphics. Метод совмещения сред моделирования использован для электро-теплового моделирования на уровне печатных плат. Разработана новая программа TransPower для совмещения систем моделирования электрической (Analog Designer) и тепловой (BETAsoft). Процедура электро-теплового моделирования таким образом полностью автоматизирована, что снижает вероятность возникновения ошибок, вносимых разработчиком, значительно уменьшает время, необходимое для моделирования. В то же время выросли точность и надёжность результатов.

Добавлено: 16 марта 2013
Глава
Petrosyants K. O., Kozynko P. In bk.: Proceedings of IEEE East-West Design & Test Symposium (EWDTS'07). Kharkov: Kharkov national university of radioelectronics, 2007. P. 599-602.

Автоматизированная система электро-теплового анализа включена в САПР ПП Mentor Graphics. Представлена новая программа, названная TransPower, для объединения электрического моделирования (Analog Designer) и теплового (BETAsoft). Моделирования полностью автоматизированно, что исключает возможные ошибки, существенно сокращает время проектирования, повышает точность и надёжность.

Добавлено: 16 марта 2013