Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.

Object detection, one of the core research topics in computer vision, is extensively used in various industrial activities. Although there have been many studies of daytime images where objects can be easily detected, there is relatively little research on nighttime images. In the case of nighttime, various types of noises, such as darkness, haze, and light blur, deteriorate image quality. Thus, an appropriate process for removing noise must precede to improve object detection performance. Although there are many studies on removing individual noise, only a few studies handle multiple noises simultaneously. In this paper, we propose a convolutional denoising autoencoder (CDAE)-based architecture trained on various types of noises. We also present various composing modules for each noise to improve object detection performance for night images. Using the exclusively dark (ExDark) Image dataset, experimental results show that the Sequential filtering architecture showed superior mean average precision(mAP) compared to other architectures.

Tremendous amounts of data are generated daily. Accordingly, unstructured text data that is distributed through news, blogs, and social media has gained much attention from many researchers as this data contains abundant information about various consumers' opinions. However, as the usefulness of text data is increasing, attempts to gain profits by distorting text data maliciously or non-maliciously are also increasing. In this sense, various types of spam detection techniques have been studied to prevent the side effects of spamming. The most representative studies include e-mail spam detection, web spam detection, and opinion spam detection. \"Spam\" is recognized on the basis of three characteristics and actions: (1) if a certain user is recognized as a spammer, then all content created by that user should be recognized as spam; (2) if certain content is exposed to other users (regardless of the users' intention), then content is recognized as spam; and (3) any content that contains malicious or non-malicious false information is recognized as spam. Many studies have been performed to solve type (1) and type (2) spamming by analyzing various metadata, such as user networks and spam words. In the case of type (3), however, relatively few studies have been conducted because it is difficult to determine the veracity of a certain word or information. In this study, we regard a hashtag that is irrelevant to the content of a blog post as spam and devise a methodology to detect such spam hashtags.

Yoon H, et al. Nutr Res Pract. 2021 Dec;15(Suppl 1):S110-0. https://doi.org/10.4162/nrp.2021.15.S1.S110

Recent advances in deep learning have led to the development of large, high-performing models that have been pretrained on massive datasets. However, employing these models in real-world services requires fast inference speed and low computational complexity. This has driven an interest in model compression techniques, such as knowledge distillation, which transfers the knowledge learned by a teacher model to a smaller student model. However, traditional knowledge distillation models are limited in that the student learns from the teacher model only the knowledge needed to solve the given problem. Therefore, giving an appropriate answer to a case that has not been encountered yet is difficult. In this study, we propose a heterogeneous knowledge distillation method that distills knowledge through a teacher model that has obtained knowledge about higher concepts, not the knowledge that needs to be obtained. The proposed methodology is based on the pedagogical discovery that problems can be solved better by learning not only specific knowledge about the problem but also general knowledge of higher concepts. In particular, beyond the limitations where traditional knowledge distillation was only capable of transferring knowledge for the same task, one can anticipate performance enhancement in lightweight models and extended applicability of pre-trained teacher models through the transfer of heterogeneous knowledge using the proposed methodology. In addition, through classification experiments on 70,000 images from the machine learning benchmark dataset Fashion-MNIST, we confirmed that the proposed heterogeneous knowledge distillation methodology achieves superior performance in terms of classification accuracy than does traditional knowledge distillation.