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 Input:
This paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system. For creating the links, we build on the Polylingual Labeled Topic Model (PLL-TM) [6]. PLL-TM identifies probable thesaurus descriptors for each class in the classification system by using information from the natural language text of documents, their assigned thesaurus descriptors and their designated classes. The links are then presented to users of the system in an interactive visualization, providing them with an automatically generated overview of the relations between the thesaurus and the classification system. Lisa Posch GESIS Leibniz Institute for the Social Sciences Cologne, Germany Institute for Web Science and Technologies University of Koblenz-Landau, Germany E-mail: lisa.posch@gesis.org Philipp Schaer GESIS Leibniz Institute for the Social Sciences Cologne, Germany E-mail: philipp.schaer@gesis.org Arnim Bleier GESIS Leibniz Institute for the Social Sciences Cologne, Germany E-mail: arnim.bleier@gesis.org Markus Strohmaier GESIS Leibniz Institute for the Social Sciences Cologne, Germany Institute for Web Science and Technologies University of Koblenz-Landau, Germany E-mail: markus.strohmaier@gesis.org

Ex Output:
A System for Probabilistic Linking of Thesauri and Classification Systems

Ex Input:
In this research we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the re-use of previously learned classifiers without the need for re-learning while providing for better accuracy during the concept recurrence interval. We capture concepts by applying the Discrete Fourier Transform (DFT) to Decision Tree classifiers to obtain highly compressed versions of the trees at concept drift points in the stream and store such trees in a repository for future use. Our empirical results on real world and synthetic data exhibiting varying degrees of recurrence show that the Fourier compressed trees are more robust to noise and are able to capture recurring concepts with higher precision than a meta learning approach that chooses to re-use classifiers in their originally occurring form.

Ex Output:
Mining Recurrent Concepts in Data Streams using the Discrete Fourier Transform

Ex Input:
Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and runtime performance when compared to standard reinforcement learning algorithms such as Q-learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms. J. Xu and L. Chen are with the Department of Electrical and Computer Engineering, University of Miami. Email: jiexu@miami.edu, lx.chen@miami.edu. S. Ren is with the Department of Electrical and Computer Engineering, University of California, Riverside. Email: sren@ece.ucr.edu

Ex Output:
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing