With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering, and, latterly, neural style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalized methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation, and development of AI artwork methods face many challenges. This article is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line, and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.@en

Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes.

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