Cellular Trending is an artwork that reveals information fragmentation on the social media through generative lens. It visualizes the fragmented information dissemination in social media with affective attributes integrated cellular automata to create artistic experience. A multi-level interactive system consists of CELL, FACT and VIEW is proposed based on information dissemination theory mapping to fragmented communication, thinking and reading. From information acquisition to opinion expression, the artwork resonates and arouses people's reflection on modern information dissemination and information acquisition behavior in the digital age.

Everyone begins to interact with the world from the moment they are born, and individual life will always have more or less influence on other lives in the group. Being's Spread" is an interactive art, which aims to reveal the relationship between the uniqueness of individual life and the life group from the perspective of light and shadow art, and trigger the experiencer's reflection on the real world. The interactive system uses the combination of symbolism and reality. It creates a dynamic light and shadow image and artistic atmosphere by using the elements of water and light, constructing an immersive experience space.

Fragrance in sight" is an interactive art installation that correlates visual and olfactory sensations. Besides, the art installation presents human's synesthesia capabilities by artificial intelligence. Fragrance in sight" recognizes the clothing style of user by artificial intelligence, then matches the results to the composition and proportion of the perfume to generate a customized perfume. Ultimately, build an individual cognition at the olfactory level.

Color is a main channel to express emotions of an image. Selecting a good color scheme with color harmony and proper emotional expression is very important in many fields, such as design. There are many color palette tools to help users search, create, edit, save, and share color themes, but few tools are from the perspective of emotional expression and explicitly address the emotional effects of colors. In this paper, a color hunt system with affective words called ColorEmo is developed. Multiple types of input, including affective words, affective categories in image‐scale space, and main colors, are allowed for users with accurate emotional description. Based on the dataset with 428,924 color themes, the system provides rich candidates. The system is designed for user research experiments. The user‐friendly interactions are offered for easy color modifications. The affective matching and color harmony are evaluated in real time while changing colors. This system can be used in many scenarios of designs and applications.

Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.

"AI mirror", an interactive art, tends to visualize the self-knowledge mechanism from the AI's perspective, and arouses people's reflection on artificial intelligence. In the first stage of the unconscious imitation, the visual neurons perceive environmental information and mirror neurons imitate human behavior. Then, the language and consciousness are generated from the long term of imitation, denoted as poet and coordinates in an affective space. In the final stage of conscious behavior, an affinity analysis is generated, and the mirror neurons will behave more harmoniously with the user or have the autonomous movements on its own, which evokes the user's reflection on its undiscovered traits.

Every child is the leading actor in her/his unique world. To help them achieve performance, an interactive art called 'little world' is proposed to let virtual humans accompany children on drama performance. Theatrical adaptation rewrites the novel to an interactive drama suitable for children. Little world builds drama scenes and virtual humans for characters and lets children interact with them by speech and actions.

When you first encounter a person, a mental image of that person is formed. First impression, an interactive art, is proposed to let AI understand human personality at first glance. The mental image is demonstrated by Beijing opera facial makeups, which shows the character personality with a combination of realism and symbolism. We build Beijing opera facial makeup dataset and semantic dataset of facial features to establish relationships among real faces, personalities and facial makeups. First impression detects faces, recognizes personality from facial appearance and finds the matching Beijing opera facial makeup. Finally, the morphing process from real face to facial makeup is shown to let users enjoy the process of AI understanding personality.

Color transfer is to alter an image’s color composition by reference to the color characteristics of another image. In this paper, we build a system called artistic coloring that realizes automatic color transfer from famous paintings. It properly extracts the wonderful color characteristics of famous paintings and applies them to color transfer. Specially, we investigate the traditional color theme extraction methods and find their deficiencies. Based on this, we quantify the processing of human painting to find the main colors in the color palette and propose an artistic balanced color theme extraction algorithm aimed specially at paintings. In the experiments, a user study is carried out to evaluate the artistic balanced extraction method. Our proposed method achieves the highest score 3.98 in a 5-point evaluation, which is 41% higher than traditional methods at most. We have successfully tested the artistic coloring system on lots of images with different painting styles. The results are natural and have the similar color characteristics with their corresponding reference paintings.

We study emotion influence in large image social networks. We focus on users' emotions reflected by images that they have uploaded and social influence that plays a role in changing users' emotions. We first verify the existence of emotion influence in the image networks, and then propose a probabilistic factor graph based emotion influence model to answer the questions of “who influences whom”. Employing a real network from Flickr as the basis in our empirical study, we evaluate the effectiveness of different factors in the proposed model with in-depth data analysis. The learned influence is fundamental for social network analysis and can be applied to many applications. We consider using the influence to help predict users' emotions and our experiments can significantly improve the prediction accuracy (3.0-26.2 percent) over several alternative methods such as Naive Bayesian, SVM (Support Vector Machine) or traditional Graph Model. We further examine the behavior of the emotion influence model, and find that more social interactions correlate with higher emotion influence between two users, and the influence of negative emotions is stronger than positive ones.

Images can not only display contents themselves, but also convey emotions, e.g., excitement, sadness. Affective image classification is useful and hot in many fields such as computer vision and multimedia. Current researches usually consider the relationship model between images and emotions as a black box. They extract the traditional discursive visual features such as SIFT and wavelet textures, and use them directly upon various classification algorithms. However, these visual features are not interpretable, and people cannot know why such a set of features induce a particular emotion. And due to the highly subjective nature of images, the classification accuracies on these visual features are not satisfactory for a long time. We propose the interpretable aesthetic features to describe images inspired by art theories, which are intuitive, discriminative and easily understandable. Affective image classification based on these features can achieve higher accuracy, compared with the state-of-the-art. Specifically, the features can also intuitively explain why an image tends to convey a certain emotion. We also develop an emotion guided image gallery to demonstrate the proposed feature collection.

Can we understand van Gogh's mood from his artworks? For many years, people have tried to capture van Gogh's affects from his artworks so as to understand the essential meaning behind the images and catch on why van Gogh created these works. In this paper, we study the problem of inferring affects from images in social networks. In particular, we aim to answer: What are the fundamental features that reflect the affects of the authors in images? How the social network information can be leveraged to help detect these affects? We propose a semi-supervised framework to formulate the problem into a factor graph model. Experiments on 20,000 random-download Flickr images show that our method can achieve a precision of 49% with a recall of 24% on inferring authors'affects into 16 categories. Finally, we demonstrate the effectiveness of the proposed method on automatically understanding van Gogh's Mood from his artworks, and inferring the trend of public affects around special event.

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