YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018

A hybrid EAV-relational model for consistent and scalable capture of clinical research data

Many clinical research databases are built for specific purposes and their design is often guided by the requirements of their particular setting. Not only does this lead to issues of interoperability and reusability between research groups in the wider community but, within the project itself, changes and additions to the system could be implemented using an ad hoc approach, which may make the system difficult to maintain and even more difficult to share. In this paper, we outline a hybrid Entity-Attribute-Value and relational model approach for modelling data, in light of frequently changing requirements, which enables the back-end database schema to remain static, improving the extensibility and scalability of an application. The model also facilitates data reuse. The methods used build on the modular architecture previously introduced in the CURe project

Music Sequence Prediction with Mixture Hidden Markov Models

Recommendation systems that automatically generate personalized music playlists for users have attracted tremendous attention in recent years. Nowadays, most music recommendation systems rely on item-based or user-based collaborative filtering or content-based approaches. In this paper, we propose a novel mixture hidden Markov model (HMM) for music play sequence prediction. We compare the mixture model with state-of-the-art methods and evaluate the predictions quantitatively and qualitatively on a large-scale real-world dataset in a Kaggle competition. Results show that our model significantly outperforms traditional methods as well as other competitors. We conclude by envisioning a next-generation music recommendation system that integrates our model with recent advances in deep learning, computer vision, and speech techniques, and has promising potential in both academia and industry.Comment: Accepted to the 4th International Conference on Artificial Intelligence and Applications (AI 2018

Multiband effects on beta-FeSe single crystals

We present the upper critical fields Hc2(T) and Hall effect in beta-FeSe single crystals. The Hc2(T) increases as the temperature is lowered for field applied parallel and perpendicular to (101), the natural growth facet of the crystal. The Hc2(T) for both field directions and the anisotropy at low temperature increase under pressure. Hole carriers are dominant at high magnetic fields. However, the contribution of electron-type carriers is significant at low fields and low temperature. Our results show that multiband effects dominate Hc2(T) and electronic transport in the normal state