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A capacitive humidity sensor is presented for moisture detection at room temperature. The sensor is fabricated by depositing multi-wall carbon nanotubes (MWCNTs) on one of the stainless-steel substrates. When compared to a sensor without CNTs, a CNT-enhanced sensor has an increase of 60–200% in capacitance response when the humidity is under 70% relative humidity (RH), and 300–3000% if the RH level goes over 70%. The performance is comparable to a commercial sensor from Honeywell, which is used as a benchmark throughout the experiments. Our results demonstrate that nano-materials like MWCNTs can naturally form networks of porous nano-structures, which can potentially realize a miniature capacitive humidity sensor with a higher sensitivity. The gain in performance is attributed to the capillary condensation effect.

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Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art motivates us to examine and discuss the creative and explorative potentials of AI technologies in the context of art. This article provides an integrated review of two facets of AI and art: (1) AI is used for art analysis and employed on digitized artwork collections, or (2) AI is used for creative purposes and generating novel artworks. In the context of AI-related research for art understanding, we present a comprehensive overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, and computational aesthetics, among others. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail. Finally, we provide a concise outlook on the future progression and potential impact of AI technologies on our understanding and creation of art.

Sina Weibo (Weibo) is a fast growing microblogging social network with total user size closer to Twitter. Weibo adopts a mechanism to verify users, so that the public can identify true accounts of celebrities and official channels of certain organizations. The verification mechanism builds trust and authenticity to the source, and hence, stimulates people to actively participate on the platform. However, how the verifications affect the user behaviors in microblogging social networks have never been fully investigated. This paper analyzes the Weibo social network with verifications, by comparing the user microblogging behaviors between verified users and unverified users and studying the social network evovlements of these two group of users. In addition, a method is proposed to approximately reconstruct the network evolvement, and lower bound quasi-densification exponents for the social networks are found. Empirical evidence demonstrates that verifications play a significant role in motivating users to have more interactions in a social network.

Billions of user-shared images are generated by individuals in many social networks today, and this particular form of user data is widely accessible to others due to the nature of online social sharing. When user social graphs are only accessible to exclusive parties, these user-shared images are proved to be an easier and effective alternative to discover user connections. This work investigated over 360 000 user shared images from two social networks, Skyrock and 163 Weibo, in which 3 million follower/followee relationships are involved. It is observed that the shared images from users with a follower/followee relationship show relatively higher similarities. A multimedia big data system that utilizes this observed phenomenon is proposed as an alternative to user-generated tags and social graphs for follower/followee recommendation and gender identification. To the best of our knowledge, this is the first attempt in this field to prove and formulate such a phenomenon for mass user-shared images along with more practical prediction methods. These findings are useful for information or services recommendations in any social network with intensive image sharing, as well as for other interesting personalization applications, particularly when there is no access to those exclusive user social graphs.