In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: We propose a novel fully-automated approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach first leverages the interlanguage links of Wikipedia to automatically construct training datasets for the is-a relation in the target language. Character-level classifiers are trained on the constructed datasets, and used in an optimal path discovery framework to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages.
[A]: 280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia Using Character-level Classification


[Q]: The Internet of Things is arriving to our homes or cities through fields already known like Smart Homes, Smart Cities, or Smart Towns. The monitoring of environmental conditions of cities can help to adapt the indoor locations of the cities in order to be more comfortable for people who stay there. A way to improve the indoor conditions is an efficient temperature control, however, it depends on many factors like the different combinations of outdoor temperature and humidity. Therefore, adjusting the indoor temperature is not setting a value according to other value. There are many more factors to take into consideration, hence the traditional logic based in binary states cannot be used. Many problems cannot be solved with a set of binary solutions and we need a new way of development. Fuzzy logic is able to interpret many states, more than two states, giving to computers the capacity to react in a similar way to people. In this paper we will propose a new approach to control the temperature using the Internet of Things together its platforms and fuzzy logic regarding not only the indoor temperature but also the outdoor temperature and humidity in order to save energy and to set a more comfortable environment for their users. Finally, ∗Corresponding author Email addresses: danielmeanallorian@gmail.com (Daniel Meana-Llorián), gonzalezgarciacristian@hotmail.com (Cristian González Garćıa), crispelayo@uniovi.es (B. Cristina Pelayo G-Bustelo), cueva@uniovi.es (Juan Manuel Cueva Lovelle), nestor@uniovi.es (Nestor Garcia-Fernandez)
[A]: IoFClime: The fuzzy logic and the Internet of Things to control indoor temperature regarding the outdoor ambient conditions


[Q]: Nonlinear independent component analysis (ICA) provides an appealing framework<lb>for unsupervised feature learning, but the models proposed so far are not identifiable.<lb>Here, we first propose a new intuitive principle of unsupervised deep learning<lb>from time series which uses the nonstationary structure of the data. Our learning<lb>principle, time-contrastive learning (TCL), finds a representation which allows<lb>optimal discrimination of time segments (windows). Surprisingly, we show how<lb>TCL can be related to a nonlinear ICA model, when ICA is redefined to include<lb>temporal nonstationarities. In particular, we show that TCL combined with linear<lb>ICA estimates the nonlinear ICA model up to point-wise transformations of the<lb>sources, and this solution is unique — thus providing the first identifiability result<lb>for nonlinear ICA which is rigorous, constructive, as well as very general.
[A]:
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA