With the development of ICT (information and communications technology) and service economy, service ecosystem is emerging in a lot of fields, including E-commerce, O2O(Online To Offline) life service, healthcare service, cloud manufacturing, and so on. As a complex socio-technical system, the evolution of service ecosystem is the joint result of the interaction of the three heterogeneous networks, including social network, service network and value network. Under such circumstances, the traditional SOA (Service Oriented Architecture)-based analysis model is powerless. As a result, how to analyze the laws behind the evolution of service ecosystem is still a serious challenge in the field. This paper proposes a value oriented analysis framework (VOA) of service ecosystem, which can use value as a clue to describe the interaction of the three heterogeneous networks. In addition, a computational experiment system is established to verify the effectiveness of the VOA framework, which stimulates the effect of different intervention strategies on service ecosystem. The result shows that our analysis framework can provide new means and ideas for the analysis of service ecosystem.

Abstract Line drawing with colorization is a popular art format and tool for architectural illustration. The goal of this research is toward generating a high-quality and natural-looking colorization based on an architectural line drawing. This paper presents a new Generative Adversarial Network (GAN)-based method, named ArchGANs, including ArchColGAN and ArchShdGAN. ArchColGAN is a GAN-based line-feature-aware network for stylized colorization generation. ArchShdGAN is a lighting effects generation network, from which the building depiction in 3D can benefit. In particular, ArchColGAN is able to maintain the important line features and the correlation property of building parts as well as reduce the uneven colorization caused by sparse lines. Moreover, we proposed a color enhancement method to further improve ArchColGAN. Besides the single line drawing images, we also extend our method to handle line drawing image sequences and achieve rotation animation. Experiments and studies demonstrate the effectiveness and usefulness of our proposed method for colorization prototyping.

Architectural illustration using line drawing with colorization is an important tool and art format. In this paper, in order to generate a natural-looking and high quality watercolorlike colorization for architectural line drawing, we propose a novel Generative Adversarial Network (GAN) approach, namely ArchGANs. The proposed ArchGANs unifies a line-feature-aware stylized colorization network (ArchColGAN), which can learn, predict and generate the coloring based on a dataset, as well as a shading generation network (ArchShdGAN), which augments the illustration with controllable lighting effects for better depicting building in 3D. Specifically, ArchColGAN can preserve the essential line features and building part correlation property, it also tackles the uneven colorization problem caused by the sparse lines. Experimental results demonstrate our proposed method is effective and suitable for colorization prototyping.