instruction:
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.
question:
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.
answer:
CONATION: English Command Input/Output System for Computers

question:
An emerging way to deal with highdimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as Time-Vertex Signal Processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: (a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple Partial Differential Equations on graphs. (b) We improve the accuracy of joint filtering operators by up-to two orders of magnitude. (c) Using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning.
answer:
A Time-Vertex Signal Processing Framework Scalable processing and meaningful representations for time-series on graphs

question:
We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We intro duce a property of state space partitions which we call f-homogeneity. Intuitively, an f-homogeneous partition groups together states that behave approximately the same under all or some subset of policies. Borrow ing from recent work on model minimization in computer-aided software verification, we present an algorithm that takes a factored representation of an MDP and an 0 f I and computes a factored f-homogeneous par tition of the state space. This partition defines a family of related MOPs-those MOP's with state space equal to the blocks of the partition, and transition probabilities "appro:X:imately" like those of any (original MDP) state in the source block. To formally study such families of MDPs, we introduce the new notion of a "bounded parameter MDP" (BMDP), which is a fam ily of (traditional) MOPs defined by speci fying upper and lower bounds on the transi tion probabilities and rewards. We describe algorithms that operate on BMDPs to find policies that are approximately optimal with respect to the original MDP. In combination, our method for reducing a large implicit MDP to a possibly much smaller BMDP using an f-homogeneous par tition, and our methods for selecting actions in BMDP's constitute a new approach for an alyzing large implicit MOP's. Among its ad vantages, this new approach provides insight into existing algorithms to solving implicit MDPs, provides useful connections to work in automata theory and model minimization, and suggests methods, which involve vary ing f, to trade time and space (specifically in terms of the size of the corresponding state space) for solution quality.
answer:
Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes