京都大学 / 新制・課程博士 / 博士(理学) / 甲第22984号 / 理博第4661号 / 新制||理||1669(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 藤 定義, 教授 佐々 真一, 教授 早川 尚男 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Recent issues such as global economic crises, terrorism, and conservation efforts are making international collaboration a critical topic. While cultural diversity often brings with it new perspectives and innovative solutions, diversity in collaborative settings can also lead to misunderstandings and interaction problems. Therefore, there is a pressing need to understand the processes and influences of intercultural collaboration and how to manage the collaborative process to result in the most effective outcomes possible. In order to address this need, the current study examines the effect of ethnic diversity, perceived deep-level similarity, trust, and distrust on collaborative behavior and performance in decision-making dyads. Participants were assigned to either same-ethnicity or different-ethnicity dyads and worked together on a political simulation game in which they had to make complex decisions to solve societal problems and increase their popularity. The results of this study indicate that ethnically similar dyads reported higher levels of perceived deep-level similarity than ethnically dissimilar dyads, and that this perceived deep-level similarity served as the mediating mechanism between objective differences in ethnic diversity and trust and distrust, respectively. The findings also suggest that trust and distrust attitudes, when considered together as a multiple mediation model, mediate the positive relationship between perceived deep-level similarity and collaborative behavior. Finally, results show that collaborative behavior significantly predicts objective performance on the political decision-making simulation. The implications of this study for theory and practice are discussed along with the study limitations and several suggestions for future research.

Deep learning is increasingly becoming a viable way of classifying all types of data. Modern deep learning algorithms, such as one dimensional convolutional neural networks, have demonstrated excellent performance in classifying time series data because of the ability to identify time invariant features. A primary challenge of deep learning for time series classification is the large amount of data required for training and many application domains, such as in medicine, have challenges obtaining sufficient data. Transfer learning is a deep learning method used to apply feature knowledge from one deep learning model to another; this is a powerful tool when both training datasets are similar and offers smaller datasets the power of more robust larger datasets. This makes it vital that the best source dataset is selected when performing transfer learning and presently there is no metric for this purpose. In this thesis a metric of predicting the performance of transfer learning is proposed. To develop this metric this research will focus on classification and transfer learning for human-activity-recognition time series data. For general time series data, finding temporal relations between signals is computationally intensive using non-deep learning techniques. Rather than time-series signal processing, a neural network autoencoder was used to first transform the source and target datasets into a time independent feature space. To compare and quantify the suitability of transfer learning datasets, two metrics were examined: i) average embedded signal from each dataset was used to calculate the distance between each datasets centroid, and ii) a Generative Adversarial Network (GAN) model was trained and the discriminator portion of the GAN is then used to assess the dissimilarity between source and target. This thesis measures a correlation between the distance between two dataset and their similarity, as well as the ability for a GAN to discriminate between two datasets and their similarity. The discriminator metric, however, does suffer from an upper limit of dissimilarity. These metrics were then used to predict the success of transfer learning from one dataset to another for the purpose of general time series classification. / Thesis / Master of Applied Science (MASc) / Over the past decade, advances in computational power and increases in data quantity have made deep learning a useful method of complex pattern recognition and classification in data. There is a growing desire to be able to use these complex algorithms on smaller quantities of data. To achieve this, a deep learning model is first trained on a larger dataset and then retrained on the smaller dataset; this is called transfer learning. For transfer learning to be effective, there needs to be a level of similarity between the two datasets so that properties from larger dataset can be learned and then refined using the smaller dataset. Therefore, it is of great interest to understand what level of similarity exists between the two datasets. The goal of this research is to provide a similarity metric between two time series classification datasets so that potential performance gains from transfer learning can be better understood. The measure of similarity between two time series datasets presents a unique challenge due to the nature of this data. To address this challenge an encoder approach was implemented to transform the time series data into a form where each signal example can be compared against one another. In this thesis, different similarity metrics were evaluated and correlated to the performance of a deep learning model allowing the prediction of how effective transfer learning may be when applied.

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