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.

This paper introduces Art Sensorium Project that is founded in Asia AI Institute of Musashino University. A main target of the project is to design and implement a system architecture of unified art collections for virtual art experiences. To provide art experiences, a projection-based VR system, called Data Sensorium, is used to stage art materials in a form of real-sized virtual reality. Furthermore, a system architecture of a multidatabase system for heterogeneous art collection archives is presented, so a set of integrated art data is applied to Data Sensorium for newly generated art experiences.

In this paper, an implementation method of GACA, Global Art Collection Archive, is proposed. Each museum maintains their own archives of art collections. GACA dynamically integrate those collection data of artworks in each museum archive and provide them with REST API. GACA works as a integrated data platform for various kinds of viewing environment of artworks such as virtual reality, physical exhibitions, smartphone applications and so on. It allows users not only to view artworks, but also to experience the creativity of artworks through seeing, feeling, and knowing them, inspiring a new era of creation.

In recent years, there are great attentions to realworld data services where the Internet services, ubiquitous computing and robot services are integrated. In order to spread such services widely, standardized platforms and wide variety of applications are required. In this paper, we propose a Remote Open Campus System (ROCS) which provides a campus tour service by controlling a robot at a campus through the Internet. ROCS maps a real world campus environment to a virtual world, that is, reflecting real views captured by a robot to a virtual campus created by itself. For that, ROCS functions are created by using RSNP (Robot Service Network Protocol), which is a protocol specification for robot services.

Recently, many ICT companies as well as researchers are taking an increasing interest in robot services using cloud computing, and various service platforms for them have been proposed. From these backgrounds, we have proposed a distributed service framework using Robot Service Network Protocol (RSNP) to integrate various devices including robots with internet services. The key mechanism of the proposed framework is a robot assignment function, which discovers distributed robot resources and assign the requested tasks by end users to suitable robots. In this paper, we implement a prototype system on the proposed framework, and conduct implementation evaluation using the system.