Since deep learning was introduced, a series of achievements has been published in the field of automatic machine translation (MT). However, Korean-Vietnamese MT systems face many challenges because of a lack of data, multiple meanings of individual words, and grammatical diversity that depends on context. Therefore, the quality of Korean-Vietnamese MT systems is still sub-optimal. This paper discusses a method for applying Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to Vietnamese sentences to improve the performance of Korean-Vietnamese MT systems. In terms of implementation, we used a tool to tag NER and POS in Vietnamese sentences. In addition, we had access to a Korean-Vietnamese parallel corpus with more than 450K paired sentences from our previous research paper. The experimental results indicate that tagging NER and POS in Vietnamese sentences can improve the quality of Korean-Vietnamese Neural MT (NMT) in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score. On average, our MT system improved by 1.21 BLEU points or 2.33 TER scores after applying both NER and POS tagging to the Vietnamese corpus. Due to the structural features of language, the MT systems in the Korean to Vietnamese direction always give better BLEU and TER results than translation machines in the reverse direction.

Interpretations by physicians of capsule endoscopy image sequences captured over periods of 7-8 hours usually require 45 to 120 minutes of extreme concentration. This paper describes a novel method to reduce diagnostic time by automatically controlling the display frame rate. Unlike existing techniques, this method displays original images with no skipping of frames. The sequence can be played at a high frame rate in stable regions to save time. Then, in regions with rough changes, the speed is decreased to more conveniently ascertain suspicious findings. To realize such a system, cue information about the disparity of consecutive frames, including color similarity and motion displacements is extracted. A decision tree utilizes these features to classify the states of the image acquisitions. For each classified state, the delay time between frames is calculated by parametric functions. A scheme selecting the optimal parameters set determined from assessments by physicians is deployed. Experiments involved clinical evaluations to investigate the effectiveness of this method compared to a standard-view using an existing system. Results from logged action based analysis show that compared with an existing system the proposed method reduced diagnostic time to around 32.5 7 minutes per full sequence while the number of abnormalities found was similar. As well, physicians needed less effort because of the systems efficient operability. The results of the evaluations should convince physicians that they can safely use this method and obtain reduced diagnostic times.