Zobrazeno 1 - 10
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pro vyhledávání: '"Han,Jing"'
With the increasing implementation of machine learning models on edge or Internet-of-Things (IoT) devices, deploying advanced models on resource-constrained IoT devices remains challenging. Transformer models, a currently dominant neural architecture
Externí odkaz:
http://arxiv.org/abs/2411.09339
Autor:
Zhou, Quan, Pei, Changhua, Sun, Fei, Han, Jing, Gao, Zhengwei, Pei, Dan, Zhang, Haiming, Xie, Gaogang, Li, Jianhui
Time series anomaly detection (TSAD) has become an essential component of large-scale cloud services and web systems because it can promptly identify anomalies, providing early warnings to prevent greater losses. Deep learning-based forecasting metho
Externí odkaz:
http://arxiv.org/abs/2411.00278
The lack of labeled data is a common challenge in speech classification tasks, particularly those requiring extensive subjective assessment, such as cognitive state classification. In this work, we propose a Semi-Supervised Learning (SSL) framework,
Externí odkaz:
http://arxiv.org/abs/2409.16937
Autor:
Zhang, Yuwei, Xia, Tong, Han, Jing, Wu, Yu, Rizos, Georgios, Liu, Yang, Mosuily, Mohammed, Chauhan, Jagmohan, Mascolo, Cecilia
Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for those applications arises from the difficulty in collecting large labele
Externí odkaz:
http://arxiv.org/abs/2406.16148
Publikováno v:
published at ICASSP 2024
Heart murmurs are a common manifestation of cardiovascular diseases and can provide crucial clues to early cardiac abnormalities. While most current research methods primarily focus on the accuracy of models, they often overlook other important aspec
Externí odkaz:
http://arxiv.org/abs/2405.03953
Publikováno v:
publised at ICASSP 2024
Automatically detecting Alzheimer's Disease (AD) from spontaneous speech plays an important role in its early diagnosis. Recent approaches highly rely on the Transformer architectures due to its efficiency in modelling long-range context dependencies
Externí odkaz:
http://arxiv.org/abs/2405.03952
Autor:
Si, Haotian, Li, Jianhui, Pei, Changhua, Cui, Hang, Yang, Jingwen, Sun, Yongqian, Zhang, Shenglin, Li, Jingjing, Zhang, Haiming, Han, Jing, Pei, Dan, Xie, Gaogang
Time series anomaly detection (TSAD) has gained significant attention due to its real-world applications to improve the stability of modern software systems. However, there is no effective way to verify whether they can meet the requirements for real
Externí odkaz:
http://arxiv.org/abs/2402.10802
Autor:
Peng, Liyizhe, Zhang, Zixing, Pang, Tao, Han, Jing, Zhao, Huan, Chen, Hao, Schuller, Björn W.
The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tasks but also in emotion recognition in terms of accuracy, universality,
Externí odkaz:
http://arxiv.org/abs/2310.14225
Publikováno v:
International Journal of Ophthalmology, Vol 17, Iss 12, Pp 2214-2220 (2024)
AIM: To quantify the severity and frequency of ocular pain in Tibetan plateau patients with dry eye, and to evaluate the related factors affecting ocular pain. METHODS: A retrospective study included 160 cases of dry eye disease (DED) patients who we
Externí odkaz:
https://doaj.org/article/bb55771688a84202a6d28a309a77de55
Publikováno v:
Shanghai yufang yixue, Vol 36, Iss 10, Pp 997-1002 (2024)
ObjectiveTo establish an analytical method for the determination of resorcinol, ferulic acid, phenethylresorcinol and benzoyl peroxide in freckle whitening cosmetics by high performance liquid chromatography (HPLC), to provide data support for the es
Externí odkaz:
https://doaj.org/article/e68125d717c7404d85117e3153214693