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.

Example Input: The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We introduce three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of inputoutput traces into an incomplete Moore machine and then completes that machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI always learns the right machine when the training set is a characteristic sample, which is generally not true for the other two algorithms. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample.
Example Output: Learning Moore Machines from Input-Output Traces

Example Input: In this paper we present architecture of a fuzzy expert system used for therapy of dyslalic children. With fuzzy approach we can create a better model for speech therapist decisions. A software interface was developed for validation of the system. The main objectives of this task are: personalized therapy (the therapy must be in according with child’s problems level, context and possibilities), speech therapist assistant (the expert system offer some suggestion regarding what exercises are better for a specific moment and from a specific child), (self) teaching (when system’s conclusion is different that speech therapist’s conclusion the last one must have the knowledge base change possibility).
Example Output: ARCHITECTURE OF A FUZZY EXPERT SYSTEM USED FOR DYSLALIC CHILDREN THERAPY

Example Input: While the general analysis of named entities has received substantial research attention, the analysis of relations over named entities has not. In fact, a review of the literature on unstructured as well as structured data revealed a deficiency in research on the abstract conceptualization required to organize relations. We believe that such an abstract conceptualization can benefit various communities and applications such as natural language processing, information extraction, machine learning and ontology engineering. In this paper, we present CEVO (i.e., a Comprehensive EVent Ontology) built on Levin’s conceptual hierarchy of English verbs that categorizes verbs with the shared meaning and syntactic behavior. We present the fundamental concepts and requirements for this ontology. Furthermore, we present three use cases for demonstrating the benefits of this ontology on annotation tasks: 1) annotating relations in plain text, 2) annotating ontological properties and 3) linking textual relations to ontological properties.
Example Output:
CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation