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pro vyhledávání: '"Abdullah A. M."'
While existing literature relies on performance differences to uncover gender biases in ASR models, a deeper analysis is essential to understand how gender is encoded and utilized during transcript generation. This work investigates the encoding and
Externí odkaz:
http://arxiv.org/abs/2406.09855
Autor:
Rashed Jalal, Zaheer Iqbal, Matieu Henry, Gianluca Franceschini, Mohammad S. Islam, Mariam Akhter, Zarin T. Khan, Mohammad A. Hadi, Mohammed A. Hossain, M. Golam Mahboob, Tasnuva S. Udita, Tariq Aziz, Syed M. Masum, Liam Costello, Champa R. Saha, Abdullah A. M. Chowdhury, Abdus Salam, Farzana Shahrin, Fazle R. Sumon, Mahbubur Rahman, Mohammad A. Siddique, Mohammad M. Rahman, Md N. Jahan, Mir F. Shaunak, Mohammad S. Rahman, Mohammad R. Islam, Nicola Mosca, Remi D'Annunzio, Shrabanti Hira, Antonio Di Gregorio
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 12, Iss 10, Pp 3852-3861 (2019)
In response to prevailing classification inconsistency between land cover maps, developed by different organizations in different times at different scales, an object-based National Land Representation System (NLRS) for Bangladesh has been developed.
Externí odkaz:
https://doaj.org/article/7d293d2bd18f4ef497cf22835b33979f
Pre-trained Transformer-based speech models have shown striking performance when fine-tuned on various downstream tasks such as automatic speech recognition and spoken language identification (SLID). However, the problem of domain mismatch remains a
Externí odkaz:
http://arxiv.org/abs/2312.07338
We use a large set of halo mass function (HMF) models in order to investigate their ability to represent the observational Cluster Mass Function (CMF), derived from the $\mathtt{GalWCat19}$ cluster catalogue, within the $\Lambda$CDM cosmology. We app
Externí odkaz:
http://arxiv.org/abs/2311.09826
Publikováno v:
Chemical Engineering Transactions, Vol 72 (2019)
Supersonic Gas Separation technology for CO2-NG separation is based on the phase change of the gas mixture component. Simulation technique is typically used to design the SGS separator geometry to optimize the separation efficiency. This approach is
Externí odkaz:
https://doaj.org/article/2597fc74763c4f0194aaf524d5c82eb9
Self-supervised representation learning for speech often involves a quantization step that transforms the acoustic input into discrete units. However, it remains unclear how to characterize the relationship between these discrete units and abstract p
Externí odkaz:
http://arxiv.org/abs/2306.02405
Autor:
Abdullah N. M. Alqahtani, Sandrine Jayne, Matthew J. Ahearne, Christopher S. Trethewey, Sai S. Duraisingham, Susann Lehmann, Caroline M. Cowley, Martin J. S. Dyer, Harriet S. Walter
Publikováno v:
eJHaem, Vol 5, Iss 4, Pp 896-899 (2024)
Externí odkaz:
https://doaj.org/article/48aa8c5bfef1448fae100ab2af0b3346
Autor:
Rajan, Adithya, Saunderson, Tom G., Lux, Fabian R., Díaz, Rocío Yanes, Abdullah, Hasan M., Bose, Arnab, Bednarz, Beatrice, Kim, Jun-Young, Go, Dongwook, Hajiri, Tetsuya, Shukla, Gokaran, Gomonay, Olena, Yao, Yugui, Feng, Wanxiang, Asano, Hidefumi, Schwingenschlögl, Udo, López-Díaz, Luis, Sinova, Jairo, Mokrousov, Yuriy, Manchon, Aurélien, Kläui, Mathias
Ferromagnets generate an anomalous Hall effect even without the presence of a magnetic field, something that conventional antiferromagnets cannot replicate but noncollinear antiferromagnets can. The anomalous Hall effect governed by the resistivity t
Externí odkaz:
http://arxiv.org/abs/2304.10747
Buffaloes are farm animals that contribute to food security by providing high quality meat and milk. They can better tolerate the adverse effects of global climate change on their meat and milk production. Despite their advantages, buffaloes are heav
Externí odkaz:
http://arxiv.org/abs/2304.04977
Publikováno v:
2024, Aug Vol 1
The artificial intelligence (AI) system designer for thermal comfort faces insufficient data recorded from the current user or overfitting due to unreliable training data. This work introduces the reliable data set for training the AI subsystem for t
Externí odkaz:
http://arxiv.org/abs/2303.03873