Vietnamese, a low-resource language, is typically categorized into three primary dialect groups that belong to Northern, Central, and Southern Vietnam. However, each province within these regions exhibits its own distinct pronunciation variations. Despite the existence of various speech recognition datasets, none of them has provided a fine-grained classification of the 63 dialects specific to individual provinces of Vietnam. To address this gap, we introduce Vietnamese Multi-Dialect (ViMD) dataset, a novel comprehensive dataset capturing the rich diversity of 63 provincial dialects spoken across Vietnam. Our dataset comprises 102.56 hours of audio, consisting of approximately 19,000 utterances, and the associated transcripts contain over 1.2 million words. To provide benchmarks and simultaneously demonstrate the challenges of our dataset, we fine-tune state-of-the-art pre-trained models for two downstream tasks: (1) Dialect identification and (2) Speech recognition. The empirical results suggest two implications including the influence of geographical factors on dialects, and the constraints of current approaches in speech recognition tasks involving multi-dialect speech data. Our dataset is available for research purposes.

Concentration of drivers on traffic is a vital safety issue; thus, monitoring a driver being on road becomes an essential requirement. The key purpose of supervision is to detect abnormal behaviours of the driver and promptly send warnings to him her for avoiding incidents related to traffic accidents. In this paper, to meet the requirement, based on radar sensors applications, the authors first use a small sized millimetre wave radar installed at the steering wheel of the vehicle to collect signals from different head movements of the driver. The received signals consist of the reflection patterns that change in response to the head movements of the driver. Then, in order to distinguish these different movements, a classifier based on the measured signal of the radar sensor is designed. However, since the collected data set is not large, in this paper, the authors propose One shot learning to classify four cases of driver's head movements. The experimental results indicate that the proposed method can classify the four types of cases according to the various head movements of the driver with a high accuracy reaching up to 100. In addition, the classification performance of the proposed method is significantly better than that of the convolutional neural network model.

Vietnam is in the nascent stages of its use of Artificial Intelligence (AI) and Big Data in healthcare: only a few hospitals out of nearly 1,400 hospitals currently have any form of AI.ome notable hospitals pioneering AI applications in diagnosis and treatment, are the People's 115 Hospital and Gia An 115 Hospital. Both of which have adopted the Stanford University "RAPID" system to diagnose and treat strokes. In cancer diagnosis and treatment, three Vietnamese hospitals are leading the way in terms of digitisation, namely the National Cancer Hospital, Phu Tho General Hospital, and HCMC Oncology Hospital. These three hospitals were selected to participate in the "IBM Watson for Oncology" AI application pilot. AI can be used in various ways to improve healthcare services including, day-to-day tracking, early detection of diseases, diagnoses, and treatment planning.

Vietnam's response to COVID-19 is a perfect example of how a country with modest financial means can successfully address a grave health challenge. At the end of Vietnam's first pandemic wave, Politico, the US-based political news organization ranked Vietnam the world's best performer. Many in Vietnam have now begun to focus on the technology that will create better healthcare. Some of this is no doubt driven by the focus that COVID-19 has put on Vietnam's healthcare deficiencies, and on worldwide innovation in the delivery of solid healthcare. The case for augmented healthcare solutions has been growing, leveraging the use of artificial intelligence ("AI"), blockchain technology, virtual reality/augmented reality ("VR/AR"), 3D printing, and robotic applications.