Comparative Studies of Detecting Abusive Language on Twitter

The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.Comment: ALW2: 2nd Workshop on Abusive Language Online to be held at EMNLP 2018 (Brussels, Belgium), October 31st, 201

Investigation of the SH3BP2 Gene Mutation in Cherubism

Cherubism is a rare developmental lesion of the jaw that is generally inherited as an autosomal dominant trait. Recent studies have revealed point mutations in the SH3BP2 gene in cherubism patients. In this study, we examined a 6-year-old Korean boy and his family. We found a Pro418Arg mutation in the SH3BP2 gene of the patient and his mother. A father and his 30-month-old younger brother had no mutations. Immunohistochemically, the multinucleated giant cells proved positive for CD68 and tartrate-resistant acid phosphatase (TRAP). Numerous spindle-shaped stromal cells expressed a ligand for receptor activator of nuclear factor kB (RANKL), but not in multinucleated giant cells. These results provide evidence that RANKL plays a critical role in the differentiation of osteoclast precursor cells to multinucleated giant cells in cherubism. Additionally, genetic analysis may be a useful method for differentiation of cherubism.</p

Intercultural Adaptation in the Context of Short Term Mission Trips

Intercultural adaptation is a critical issue in international tourism. Good quality interactions can provide satisfaction for both tourists and local communities. This paper presents a study of intercultural adaptation in the context of international short-term mission trips using qualitative research methods. The objective of this study was to understand the process of intercultural adaptation so that existing theory on intercultural adaptation can be informed and, at the same time, insights can be gained with respect to short-term mission travel as a new form of tourism for which such intercultural adaptation seems to be an important condition

A Meta-Analysis of Ethical Fashion Consumption Research in South Korea

In this study, a meta-analysis of studies on ethical fashion consumption in South Korea was conducted with the purpose of better understanding the influences of different factors on ethical fashion consumption

Sample-efficient Adversarial Imitation Learning

Imitation learning, in which learning is performed by demonstration, has been studied and advanced for sequential decision-making tasks in which a reward function is not predefined. However, imitation learning methods still require numerous expert demonstration samples to successfully imitate an expert's behavior. To improve sample efficiency, we utilize self-supervised representation learning, which can generate vast training signals from the given data. In this study, we propose a self-supervised representation-based adversarial imitation learning method to learn state and action representations that are robust to diverse distortions and temporally predictive, on non-image control tasks. In particular, in comparison with existing self-supervised learning methods for tabular data, we propose a different corruption method for state and action representations that is robust to diverse distortions. We theoretically and empirically observe that making an informative feature manifold with less sample complexity significantly improves the performance of imitation learning. The proposed method shows a 39% relative improvement over existing adversarial imitation learning methods on MuJoCo in a setting limited to 100 expert state-action pairs. Moreover, we conduct comprehensive ablations and additional experiments using demonstrations with varying optimality to provide insights into a range of factors.Comment: A preliminary version of this manuscript was presented at Deep RL Workshop, NeurIPS 202