Recently, a worker’s subjective satisfaction, in other words Quality-of-Working Life (QWL), has attracted more attention than productivity or efficiency. To provide QWL-oriented working support in a factory manufacturing environment, this study proposes a framework for recognizing manual assembly behaviors that may reflect a worker’s inner state or physical condition. First, a new set of interactions is defined to describe the behavioral fluctuations and diversity that appear even in the same assembly task. We expand the conventional interaction definitions for manufacturing analysis in three ways: 1) we add primitive interactions that qualify the fundamental interactions, 2) we install a spatial attribute into the interaction definition, and 3) we allow the simultaneous occurrence of multiple interactions. Additionally, an image-based automatic recognition technique is designed to detect the newly defined interactions. Through experimental evaluations for a compressor attachment task, we found various differences in manual assembly behaviors and confirmed that they can be distinguished using the recognized interactions.

In the fashion industry where social media has a growing presence, it is increasingly important to find the emergence of people's new tastes in the early stage based on the photos posted there. However, the amount of photos posted on fashion social media is so large that it is almost impossible for people to examine them manually. Also, previous studies on image analysis in social media focus only on individual items for trend detection. Therefore, in this research, we propose a novel framework for capturing changes in people's tastes in terms of coordination rather than individual items. In the framework, we apply Emerging Topic Detection (ETD) to multiple meta-data of images automatically extracted by deep learning. In ETD, new topics which did not exist previously are detected by comparing multiple time windows. To better capture the nature of fashion topics, we employ a clustering method MULIC as a topic detection method, which is density-based, centroid-based, and designed for categorical data. Our experiments with real-world data, in terms of method stability, qualitative evaluation of the output, and experts review, confirmed that the Emerging Topics were properly captured.

This study examines the relationships between Twitter users’ motives for using the service and their egocentric network sizes on Twitter in terms of online social capital. Based on the literature, we focus on quantiles of egocentric network sizes rather than on means. The respondents were 1,559 Japanese Twitter users; they participated in an online survey and allowed us to collect their log data on Twitter. A socializing motive was associated with the number of mutual follows only in the lower tails of the size distribution and was negatively linked to the number of one-sided follows. In contrast, an information-seeking motive was positively related to the number of one-sided follows. These findings suggest that cognitive constraints exert an effect on socializing through an online service.

In order to group web pages, vector space models for analyzing documents similarities have been often used variously. However, they are not used for analyzing link structures, partly because they are complex and links do not necessarily satisfy the similarity relation. If we can devise vector space models for link structures, we can combine them with those models for document similarity in order to develop the unified basis for grouping web pages. In this paper, we present a vector space model for link structures, based on the notion of link vectors, the specifically designed characteristic vectors for link structures. We also discuss the extension of this model to the model called content-link vector space model, which can treat document information and link information of web pages in a unified way. The preliminary experiments show that the models show good performance even when document information is ignored.