TET-GAN: Text Effects Transfer via Stylization and Destylization

Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via one-shot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.Comment: Accepted by AAAI 2019. Code and dataset will be available at http://www.icst.pku.edu.cn/struct/Projects/TETGAN.htm

Profiling Hate Speech Spreaders on Twitter

Hate speech is defined as any public communication that depreciates a person or a group by expressing hate or encouraging violence. From the identification of the profiles of hate propagators, it is possible to avoid the spread of hate speech and keep social networks healthier. In this study, I focused on Twitter. Simply analyzing words in tweets is a good starting point to identify hate speech and people who spread hate speech. However, we believe there is value in considering other expressions that are commonly seen in tweets. The purpose of this study was to explore a variety of expressions and unveil a set of common patterns that could lead to identifying user profiles that promote hate speech on social media (Twitter)

Power Line Communication Input Impedance Adjustment via Network Load Measurements

Power Line Communication (PLC) allows data to be multiplexed with the power line sinusoidal waveform. We consider a lumped circuit model network representing a distribution line and typical PLC hardware on either side of the network. The distribution line is assumed to have an auxiliary load which varies but can be measured. For the modulated and multiplexed data signal to be received requires sufficient power transfer through the network. We propose a methodology to adjust the receiving end input impedance to optimize the data transfer using a power transfer metric