We propose a novel system, called CosmicAI, for providing an intuitive and efficient way to synthesize 2D sky images. The system utilizes a collection of high-quality proprietary sky part images for backgrounds, clouds, and the moon, and allows the users to generate their own sky images effectively through attribute-based retrieval of these sky parts images and their flexible layout and natural composition based on a new cloud extraction algorithm and an original filter for partly-overlapping pixels. Its one click search and active composition play an important role for realizing rapid image synthesis. Moreover, CosmicAI is capable of collecting via the Internet and searching for composite works. This function promotes group works by giving incentives to users with unclear design motives for generating a wide variety of sky images.

In this paper, we present a novel particlebased method for efficiently simulating viscous fluids using position-based constraints. Our method utilizes the geometric configuration of particles for the positional constraints in the position-based dynamics, and thus can generate visually-plausible behavior of viscous fluids, while allowing for the use of much larger time steps than the ones previously adopted in the viscous fluid simulations. An associated boundary handling scheme for the position-based fluids is also proposed to properly address constraints for density and viscosity distributions on boundaries. In addition, by adjusting parameters of particles, our method can produce complicated dynamics of threads, sheets, and volumes of different viscosity values in a unified framework. Several examples demonstrate the efficiency as well as robustness and versatility of our approach.

Digital signage has been getting more popularity due to the recent development of underlying hardware technology and improvement in installing environments. Any signage is required to make its content more attractive to viewers by evaluating the current attractiveness on the fly, in order to deliver the message from the sender more effectively. However, most previous methods for signage require time to reflect the viewers’ evaluations. In this paper, we present a novel, viewer-adaptive digital signage displaying academic conference posters, which automatically learns what structure and content are being attractive through the viewers’ involuntary behaviors, and makes them more adapted to the viewers. This system takes away the current gap between viewers’ evaluations and the content actually displayed on digital signage, and makes efficient mutual transmission of information between the cyberworld and the reality.

Computer visualization has become a crucial component of data analysis, and so-called Modular Visualization Environments (MVEs) have been used in a variety of analytical disciplines. Even so, scientists and engineers who are not visualization experts have some difficulty in selecting and applying visualization techniques that best suit their purposes. To address this problem, we have extended our visualization ontology to develop a visualization system that helps the users select and execute suitable visualization techniques. In this paper, we attempt to further extend our ontology towards a particularly important domain of visualization: structural analysis. The resulting system uses a refined Wehrend Matrix to represent users’ goals and requirements, and to search the knowledge-base for appropriate visualization workflows. The system also allows the users to browse successful examples maintained in the case repository, thereby supporting “design by example”. Three practical structural analysis examples are presented to illustrate the usefulness of our prototype system.

Digital signage has been getting more popular due to the recent development of underlying hardware technology and improvement in installing environments. In order to deliver a message from a sender effectively, it is necessary to check up viewers’ evaluations towards the content actually displayed by the digital signage and utilize the evaluations to make it more adapted to the local viewers’ tastes. In our study, we have attempted to take advantage of viewers’ involuntary behaviors.