Recently, Bayesian optimization has become commonly used in material development. However, in the development of polymer materials, a problem occurs wherein polymers do not always get formed. To address this, we incorporated an auxiliary classifier into the flow of Bayesian optimization. The results of a preliminary experiment show the potential of this approach.

We apply an attention-based sequence-to-sequence model for the Japanese HIRAGANA-KANJI conversion task in spontaneous speech transcripts. Experimental results indicate that short HIRAGANA sequences containing speech-specific errors can be converted into error-free HIRAGANA-KANJI mixed Japanese text.

Graph construction is an important process in graph-based semi-supervised learning. Presently, the mutual kNN graph is the most preferred as it reduces hub nodes which can be a cause of failure during the process of label propagation. However, the mutual kNN graph, which is usually very sparse, suffers from over sparsification problem. That is, although the number of edges connecting nodes that have different labels decreases in the mutual kNN graph, the number of edges connecting nodes that have the same labels also reduces. In addition, over sparsification can produce a disconnected graph, which is not desirable for label propagation. So we present a new graph construction method, the centered kNN graph, which not only reduces hub nodes but also avoids the over sparsification problem.

It is known that memory-based collaborative filtering systems are vulnerable to shilling attacks. In this paper, we demonstrate that hubness, which occurs in high dimensional data, is exploited by the attacks. Hence we explore methods for reducing hubness in user-response data to make these systems robust against attacks. Using the MovieLens dataset, we empirically show that the two methods for reducing hubness by transforming a similarity matrix(i) centering and (ii) conversion to a commute time kernel-can thwart attacks without degrading the recommendation performance.