石橋 直樹

In this paper, a dynamic curation method that uses pretrained models of art interpretation is proposed. The proposed method leverages exhibition catalogs (primary classifications and images) as a source of curatorial knowledge to construct a machine learning model, thereby using this model to evaluate, position, and visualize other artwork. This method enables the dynamic curation of artwork in a vast integrated art collection archive. A prototype system for the proposed method was implemented and applied to two exhibition catalogs at the Artizon Museum. In the experiment, artworks matching the purpose and art style of the modeled exhibition from the archives of the Metropolitan Museum of Art in New York City and Paris Musées were evaluated using the constructed model. The experiment demonstrated that the proposed method successfully classifies and enables the visualization of artwork and images available in the open data from the MET and Paris Musées in alignment with the intended theme of the exhibition for which the model was constructed. Feedback from curators and art professionals indicates that the proposed method can be used to compare museums with art collections of the same genre and to organize new exhibitions based on the curatorial model.

Achieving the 2030 and 2050 Paris Agreement targets to improve the global environment and address climate change is critical despite the costs and time requirements to implement possible measures. However, current measures are primarily undertaken from a global macro perspective, and lack established means to examine them from micro, realistic, and practical perspectives. CO2 direct air capture (DAC) is an innovative negative CO2 emission technology in its early commercial stages that can help control and mitigate climate change in the long term. Despite technological advances in the past decade, there are still misconceptions about the current and long-term costs of DAC, and energy, water, and area demands. This paper presents a knowledge-based indication method with a prototype system for a DAC location and cost simulator to support early-stage decisions from a micro-local perspective and promote the use of the applicable and scalable DAC technology. Additionally, this study presents a method for determining optimal locations, estimating project costs, and prioritizing projects for early-stage feasibility assessments, policy, and business decisions on DAC investments to accelerate its deployment. The main feature of this approach is to provide a data model and unified cost index ($/CO2 Ton) to calculate the optimal location and cost projection of DAC implementation based on location characteristics and a quantified industry knowledge base considering various cost-carbon intensity constraints—such as reservoirs, infrastructure, low-carbon electricity, heat, transportation, and atmospheric conditions—that affect the suitability of specific locations.

In this paper, we proposed an implementation method of a system according to ‘tri-knowledge base with personal context vectors model’ that proposed in previous study. The system aims not only a single set of contexts and knowledge base, but also create snapshot and stored it as the memories of the changes in timeseries. Organisational contexts vectors were added to the system. As well as the personal context vectors to absorb personal preferences, the organisational context vectors can absorb organisational preference without changing the knowledgebase. Experiments were conducted to verify the functionality of memorising the changes in timeseries and the effects of the organisational context vector.

This study investigates the complex dynamics of team sports, with a focus on the role of team members’ impressions of each other. We propose a novel team building method that incorporates dynamic elements to promote team formation and development, based on these impressions. A web-based system was developed to manage these impressions and collect data. This data was then analyzed to evaluate the impact of these impressions on team performance. Two team sports with distinct characteristics, American football and basketball, were examined. The results revealed that mutual impressions among team members significantly influenced team performance. This study underscores the importance of the interplay of team members’ impressions in team performance.

We propose a multidimensional corporate analysis system architecture, Dynamic Corporate Inspector (DCI), for stakeholders. This system architecture provides multifaceted corporate analysis of companies by changing text datasets and keyword sets. In this paper, We implemented the system using 4225 Japanese annual securities reports as text data and 173 keywords of SDGs as keyword sets. The analysis visualizes each company’s interest and activities for the SDGs.