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Hyeon-Kyu Noh and Hong-June Park
A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy. The CNN of 177 k parameters and a kernel size of 6 generates the proba...
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Gulshan Saleem, Usama Ijaz Bajwa, Rana Hammad Raza and Fan Zhang
Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNe...
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Hui Sheng, Min Liu, Jiyong Hu, Ping Li, Yali Peng and Yugen Yi
Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods ha...
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Soumyashree Kar, Jason R. McKenna, Glenn Anglada, Vishwamithra Sunkara, Robert Coniglione, Steve Stanic and Landry Bernard
While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noi...
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Yuanyuan Li, Yuan Huang, Weijian Huang, Junhao Yu and Zheng Huang
An abstractive summarization model based on the joint-attention mechanism and a priori knowledge is proposed to address the problems of the inadequate semantic understanding of text and summaries that do not conform to human language habits in abstractiv...
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Songnan Chen, Mengxia Tang, Ruifang Dong and Jiangming Kan
The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB?D images can provide additional depth information for improving the performance of semanti...
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Niousha Ghannad and Kalpdrum Passi
Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone signifi...
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Mohammad Masum Billah, Jing Zhang and Tianchi Zhang
Data-driven technologies and automated identification systems (AISs) provide unprecedented opportunities for maritime surveillance. As part of enhancing maritime situational awareness and safety, in this paper, we address the issue of predicting a ship?s...
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Xin-Yi Yuan, Yue Hua, Nadine Aubry, Mansur Zhussupbekov, James F. Antaki, Zhi-Fu Zhou and Jiang-Zhou Peng
This study develops a data-driven reduced-order model based on a deep convolutional neural network (CNN) for real-time and accurate prediction of the drug trajectory and concentration field in transarterial chemoembolization therapy to assist in directin...
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Jiawei Zhang, Xin Zhao, Tao Jiang, Md Mamunur Rahaman, Yudong Yao, Yu-Hao Lin, Jinghua Zhang, Ao Pan, Marcin Grzegorzek and Chen Li
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder?decoder architecture...
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