Ideology detection (ID) is important for gaining insights about peoples' opinions and stances on our world and society, which can find many applications in politics, economics and social sciences.It is not uncommon that a piece of text can contain descriptions of various issues.It is also widely accepted that a person can take different ideological stances in different facets.However, existing datasets for the ID task only label a text as ideologically left-or right-leaning as a whole, regardless whether the text containing one or more different issues.Moreover, most prior work annotates texts from data resources with known ideological bias through distant supervision approaches, which may result in many false labels.With some theoretical help from social sciences, this work first designs an ideological schema containing five domains and twelve facets for a new multifaceted ideology detection (MID) task to provide a more complete and delicate description of ideology.We construct a MITweet dataset for the MID task, which contains 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets.We also design and test a few of strong baselines for the MID task under in-topic and cross-topic settings, which can serve as benchmarks for further research.

It is important to obtain accurate indoor location information in the Wi-Fi fingerprinting localization system, which suffers from a lot of labour-power to construct the fingerprint database. Improving positioning accuracy with less manual labour is still an issue. In this paper, we propose a signal-physical Siamese neural network SPSNN, consisting of a signal distance matrix encoder and a physical distance matrix encoder to learn features. The main idea of the SPSNN is to use the physical feature to constrain the signal feature. Grid fingerprints are constructed using crowdsourcing samples. We compute the distance difference between the sample and different grid fingerprints in signal space to build the pictures as the input of the Siamese network for further feature extraction. In addition, we exploit the distance distribution information in physical space to modify and constrain the signal feature vector during the network training phase. The position prediction is given by the regression method. Experiments on-field measurements validate the effectiveness and robustness of the proposed model, and the optimal localization accuracy is less than 1 m.