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]: In this information technology age, a convenient and user friendly interface is required to operate the computer system on very fast rate. In human being, speech being a natural mode of communication has potential to being a fast and convenient mode of interaction with computer. Speech recognition will play an important role in taking technology to them. It is the need of this era to access the information with in seconds. This paper describes the design and development of speaker independent and English command interpreted system for computer. HMM model is used to represent the phoneme like speech commands. Experiments have been done on real world data and system has been trained in normal condition for real world subject.
[EX A]: CONATION: English Command Input/Output System for Computers

[EX Q]: Online learning aims to perform nearly as well as the best hypothesis in hindsight. For some hypothesis classes, though, even finding the best hypothesis offline is challenging. In such offline cases, local search techniques are often employed and only local optimality guaranteed. For online decision-making with such hypothesis classes, we introduce local regret, a generalization of regret that aims to perform nearly as well as only nearby hypotheses. We then present a general algorithm to minimize local regret with arbitrary locality graphs. We also show how the graph structure can be exploited to drastically speed learning. These algorithms are then demonstrated on a diverse set of online problems: online disjunct learning, online Max-SAT, and online decision tree learning.
[EX A]: On Local Regret

[EX Q]: How to build a machine learning method that can continuously gain structured visual knowledge by learning structured facts? Our goal in this paper is to address this question by proposing a problem setting, where training data comes as structured facts in images with different types including (1) objects(e.g., <boy>), (2) attributes (e.g., <boy,tall>), (3) actions (e.g., <boy, playing>), (4) interactions (e.g., <boy, riding, a horse >). Each structured fact has a semantic language view (e.g., < boy, playing>) and a visual view (an image with this fact). A human is able to efficiently gain visual knowledge by learning facts in a never ending process, and as we believe in a structured way (e.g., understanding “playing” is the action part of < boy, playing>, and hence can generalize to recognize <girl, playing > if just learn <girl> additionally). Inspired by human visual perception, we propose a model that is (1) able to learn a representation, we name as wild-card, which covers different types of structured facts, (2) could flexibly get fed with structured fact language-visual view pairs in a never ending way to gain more structured knowledge, (3) could generalize to unseen facts, and (4) allows retrieval of both the fact language view given the visual view (i.e., image) and vice versa. We also propose a novel method to generate hundreds of thousands of structured fact pairs from image caption data, which are necessary to train our model and can be useful for other applications.
[EX A]:
SHERLOCK: MODELING STRUCTURED KNOWLEDGE IN IMAGES