TASK DEFINITION: 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.
PROBLEM: 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

SOLUTION: A System for Probabilistic Linking of Thesauri and Classification Systems

PROBLEM: The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment. The underlying idea is that such systems must have self-x properties in order to adapt their behavior to external disturbances with a high-degree of autonomy.

SOLUTION: TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENT

PROBLEM: Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating infrequently occurring words or multi-language topic modeling. Experimental results demonstrate the effectiveness of our model compared with standard LDA.

SOLUTION:
Modeling Word Relatedness in Latent Dirichlet Allocation