EXPRESS: Machine Learning for Creativity: Using Similarity Networks to Design Better Crowdfunding Projects

A fundamental tension exists in creativity between novelty and similarity. This paper exploits this tension to help creators craft successful projects in crowdfunding. To do so, we apply the concept of combinatorial creativity, analyzing each new project in connection to prior similar projects. By using machine learning techniques (Word2vec and Word Mover’s Distance), we measure the degrees of similarity between crowdfunding projects on Kickstarter. We analyze how this similarity pattern relates to a project’s funding performance. We find: (i) the prior level of success of similar projects strongly predicts a new project’s funding performance, (ii) the funding performance increases with a balance between being novel and imitative, (iii) the optimal level for funding goal is close to the funds raised by prior similar projects, and (iv) the funding performance increases with a balance between atypical and conventional imitation. We use these findings to generate actionable recommendations for project creators and crowdfunding platforms.

Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition

A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data.

Pattern similarity and connectivity of hippocampal-neocortical regions support empathy for pain

Empathy is thought to engage mental simulation, which in turn is known to rely on hippocampal-neocortical processing. Here, we tested how hippocampal-neocortical pattern similarity and connectivity contributed to pain empathy. Using this approach, we analyzed a data set of 102 human participants who underwent functional MRI while painful and non-painful electrical stimulation was delivered to themselves or to a confederate. As hypothesized, results revealed increased pattern similarity between first-hand pain and pain empathy (compared to non-painful control conditions) within the hippocampus, retrosplenial cortex, the temporo-parietal junction and anterior insula. While representations in these regions were unaffected by confederate similarity, pattern similarity in the dorsal medial prefrontal cortex was increased the more dissimilar the other individual was perceived. Hippocampal-neocortical connectivity during first-hand pain and pain empathy engaged largely distinct but neighboring primary motor regions, and empathy-related hippocampal coupling with the fusiform gyrus positively scaled with trait measures of perspective taking. These findings suggest that shared representations and mental simulation might contribute to pain empathy via hippocampal-neocortical pattern similarity and connectivity, partially affected by personality traits and the similarity of the observed individual.

Fostering Net Promoter Score: Crafting User Experience in Difference Cultural Ecosystem

The customer research in terms of usability, usefulness and branding on User Experience (UX) design is a critical part of the success in application implementation on Information communication technology (ICT) industry. The study on impact of cultural system with similar target market into designing UX in correlation with net promoter score (NPS) has not been revealed as part of important factor, for designing and evaluation of application design. Hence, this paper has objective to assess the effect of user experience (UX) in difference cultural ecosystem in relation with NPS for Indonesia diaspora. The case of prepaid product Kartu As 2in1 was investigated. The survey done through discussion with 20 respondents of Indonesia diaspora, with 10 respondents in Indonesia and Malaysia respectively. The result found that there is some similarity pattern of customer characteristics. However, some variations due to cultural ecosystem difference is found, that the Indonesia diaspora living in Malaysia mostly focused on the easiness of the use, but in Indonesia, they focused on the functionality