With the emergence of large digitized fine art collections and the successful performance of deep learning techniques, new research prospects unfold in the intersection of artificial intelligence and art. In order to explore the applicability of deep learning techniques in understanding art images beyond object recognition and classification, we employ convolutional neural networks (CNN) to predict scores related to three subjective aspects of human perception: aesthetic evaluation of the image, sentiment evoked by the image and memorability of the image. For each concept, we evaluate several different CNN models trained on various natural image datasets and select the best performing model based on the qualitative results and the comparison with existing subjective ratings of artworks. Furthermore, we employ different decision tree based machine learning models to analyze the relative importance of various image features related to content, composition and color in determining image aesthetics, visual sentiment and memorability scores. Our findings suggest that content and image lighting have significant influence on aesthetics, that color vividness and harmony strongly influence sentiment prediction, while object emphasis has a high impact on memorability. Additionally, we explore the predicted aesthetic, sentiment and memorability scores in the context of art history by analyzing their distribution in regard to different artistic styles, genres, artists and centuries. The presented approach enables new ways of exploring fine art collections based on highly subjective aspects of art, as well as represents one step forward towards bridging the gap between traditional formal analysis and computational analysis of fine art.

Real-world attacks can be interpreted as the result of competitive interactions between networks, ranging from predator–prey networks to networks of countries under economic sanctions. Although the purpose of an attack is to damage a target network, it also curtails the ability of the attacker, which must choose the duration and magnitude of an attack to avoid negative impacts on its own functioning. Nevertheless, despite the large number of studies on interconnected networks, the consequences of initiating an attack have never been studied. Here, we address this issue by introducing a model of network competition where a resilient network is willing to partially weaken its own resilience in order to more severely damage a less resilient competitor. The attacking network can take over the competitor's nodes after their long inactivity. However, owing to a feedback mechanism the takeovers weaken the resilience of the attacking network. We define a conservation law that relates the feedback mechanism to the resilience dynamics for two competing networks. Within this formalism, we determine the cost and optimal duration of an attack, allowing a network to evaluate the risk of initiating hostilities.

Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society