Exploiting TTP Co-Occurrence via GloVe-Based Embedding With MITRE ATT&CK Framework

Autor: Chanho Shin, Insup Lee, Changhee Choi

Publikováno v: IEEE Access, Vol 11, Pp 100823-100831 (2023)

The digital transformation of various systems has brought great convenience to our daily lives, but it has also increased the level of cyberattacks. As the number of cyberattacks has increased, so has the number of reports analyzing them, MITRE publi

Externí odkaz: https://doaj.org/article/1cb830128adb427faf01899f34424cd9

Sampling and ranking spatial transcriptomics data embeddings to identify tissue architecture

Autor: Yu Lin, Yan Wang, Yanchun Liang, Yang Yu, Jingyi Li, Qin Ma, Fei He, Dong Xu

Publikováno v: Frontiers in Genetics, Vol 13 (2022)

Spatial transcriptomics is an emerging technology widely applied to the analyses of tissue architecture and corresponding biological functions. Substantial computational methods have been developed for analyzing spatial transcriptomics data. These me

Externí odkaz: https://doaj.org/article/4afdeeb8e1b340b5aedc7ad9ae8d238a

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Embeddings Evaluation Using a Novel Measure of Semantic Similarity

Autor: Fabio Mercorio, MARIO MEZZANZANICA, Lorenzo Malandri, Anna Giabelli, Navid Nobani

Lexical taxonomies and distributional representations are largely used to support a wide range of NLP applications, including semantic similarity measurements. Recently, several scholars have proposed new approaches to combine those resources into un

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7b7c6c492aac86d42232bf8cc509b46
http://hdl.handle.net/10281/344508

A survey on training and evaluation of word embeddings

Autor: Guillaume Gravier, Nihel Kooli, Robin Allesiardo, François Torregrossa, Vincent Claveau

Publikováno v: International Journal of Data Science and Analytics
International Journal of Data Science and Analytics, Springer Verlag, 2021, 11 (2), pp.85-103. ⟨10.1007/s41060-021-00242-8⟩
International Journal of Data Science and Analytics, 2021, 11 (2), pp.85-103. ⟨10.1007/s41060-021-00242-8⟩

International audience; Word Embeddings have proven to be effective for many Natural Language Processing tasks by providing word representations integrating prior knowledge. In this article, we focus on the algorithms and models used to compute those

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7301a886ce62d3f070714a5e1991cf1b
https://hal.archives-ouvertes.fr/hal-03148517/document