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.

The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities may manifest in the form of offensive speech, consequently making political discussions more aggressive and polarizing than they already are. Such environments may result in harassment and self-censorship from its targets. In this paper, we present preliminary results from a large-scale temporal measurement aimed at quantifying offensiveness in online political discussions. To enable our measurements, we develop and evaluate an offensive speech classifier. We then use this classifier to quantify and compare offensiveness in the political and general contexts. We perform our study using a database of over 168M Reddit comments made by over 7M pseudonyms between January 2015 and January 2017 – a period covering several divisive political events including the 2016 US presidential elections.
Measuring Offensive Speech in Online Political Discourse

Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.
Explanation Trees for Causal Bayesian Networks

Wide-angle sonar mapping of the environ­ ment by mobile robot is nontrivial due to sev­ eral sources of uncertainty: dropouts due to "specular" reflections, obstacle location un­ certainty due to the wide beam, and distance measurement error. Earlier papers address the latter problems, but dropouts remain a problem in many environments. We present an approach that lifts the overoptimistic in­ dependence assumption used in earlier work, and use Bayes nets to represent the depen­ dencies between objects of the model. Ob­ jects of the model consist of readings, and of regions in which "quasi location invari­ ance" of the (possible) obstacles exists, with respect to the readings. Simulation supports the method's feasibility. The model is readily extensible to allow for prior distributions, as well as other types of sensing operations.

Bayes Networks for Sonar Sensor Fusion