Creating layouts is a fundamental step in graphic design. In this work, we propose to use text as the guidance to create graphic layouts, i.e., Text-to-Layout, aiming to lower the design barriers. Text-to-Layout is a challenging task, because it needs to consider the implicit, combined, and incomplete layout constraints from text, each of which has not been studied in previous work. To address this, we present a two-stage approach, named parse-then-place. The approach introduces an intermediate representation (IR) between text and layout to represent diverse layout constraints. With IR, Text-to-Layout is decomposed into a parse stage and a place stage. The parse stage takes a textual description as input and generates an IR, in which the implicit constraints from the text are transformed into explicit ones. The place stage generates layouts based on the IR. To model combined and incomplete constraints, we use a Transformer-based layout generation model and carefully design a way to represent constraints and layouts as sequences. Besides, we adopt the pretrain-then-finetune strategy to boost the performance of the layout generation model with large-scale unlabeled layouts. To evaluate our approach, we construct two Text-to-Layout datasets and conduct experiments on them. Quantitative results, qualitative analysis, and user studies demonstrate our approach’s effectiveness.

When data mining occurs on distributed data, privacy of parties becomes great concerns. This paper considers the problem of mining quantitative association rules without revealing the private information of parties who compute jointly and share distributed data. The issue is an area of privacy preserving data mining (PPDM) research. Some researchers have considered the case of mining Boolean association rules; however, this method cannot be easily applied to quantitative rules mining. A new secure set union algorithm is proposed in this paper, which unifies the input sets of parties without revealing any element's owner and has lower time cost than existing algorithms. The new algorithm takes the advantages of both in privacy-preserving Boolean association rules mining and in privacy-preserving quantitative association mining. This paper also presents an algorithm for privacy-preserving quantitative association rules mining over horizontally portioned data, based on CF tree and secure sum algorithm. Besides, the analysis of the correctness, the security and the complexity of our algorithms are provided