Visualizing behavioral states of teams is useful when providing effective reflection on teamwork for team members, monitoring and intervening teams, and analyzing team processes for teamwork researchers. This paper aims to develop a method to visualize behavioral states in meeting settings. We assumed that team processes follow several periods where behavioral states are stable and tried to detect these periods by focusing on change of communication patterns and facial expression. Detailed methods of detection and subsequent visualization are provided. We then test our method by comparing visualized team states in an idea generation workshop with qualitative observation of its team process. The result shows our method can effectively express some team states and provides viewpoints for more rigid quantitative verification.
There is a growing need for algorithms to automatically detect objects in satellite images. Object detection algorithms using deep learning have demonstrated a significant improvement in object detection performance. However, deep-learning models have difficulty in interpreting the features for inference. This difficulty is practically problematic when analyzing earth-observation images, which are often used as evidence for public decision-making. In addition, for the same reason, it is difficult to set an explicit policy or criteria to improve the models. To deal with these challenges, we introduce a feature attribution method that defines an approximate model and calculates the attribution of input features to the output of a deep-learning model. For the object detection models of satellite images with complex textures, we propose a method to visualize the basis of inference using pixel-wise feature attribution. Furthermore, we propose new methods for model evaluation, regularization, and data selection, based on feature attribution. Experimental results demonstrate the feasibility of the proposed methods for basis visualization and model evaluation. Moreover, the results illustrate that the model using the proposed regularization method can avoid over-fitting and achieve higher performance, and the proposed data selection method allows for the efficient selection of new training data.
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