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.

[EX Q]: Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.
[EX A]: Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages

[EX Q]: Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.
[EX A]: Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

[EX Q]: Submodular functions describe a variety of discrete problems in machine learn-<lb>ing, signal processing, and computer vision. However, minimizing submodular<lb>functions poses a number of algorithmic challenges. Recent work introduced an<lb>easy-to-use, parallelizable algorithm for minimizing submodular functions that<lb>decompose as the sum of “simple” submodular functions. Empirically, this al-<lb>gorithm performs extremely well, but no theoretical analysis was given. In this<lb>paper, we show that the algorithm converges linearly, and we provide upper and<lb>lower bounds on the rate of convergence. Our proof relies on the geometry of<lb>submodular polyhedra and draws on results from spectral graph theory.
[EX A]:
On the Convergence Rate of Decomposable Submodular Function Minimization