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.
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Question: Speech analysis had been taken to a new level with the discovery of Reverse Speech (RS). RS is the discovery of hidden messages, referred as reversals, in normal speech. Works are in progress for exploiting the relevance of RS in different real world applications such as investigation, medical field etc. In this paper we represent an innovative method for preparing a reliable Software Requirement Specification (SRS) document with the help of reverse speech. As SRS act as the backbone for the successful completion of any project, a reliable method is needed to overcome the inconsistencies. Using RS such a reliable method for SRS documentation was developed. Keywords Reverse Speech, Software Requirement Specification (SRS), Speech Enhancement, Speech Recognition.

Answer: Software Requirement Specification Using Reverse Speech Technology

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

Question: The undirected technique for evaluating be lief networks [Jensen et al., 1990a, Lauritzen and Spiegelhalter, 1988] requires clustering the nodes in the network into a junction tree. In the traditional view, the junction tree is constructed from the cliques of the moral ized and triangulated belief network: trian gulation is taken to be the primitive concept, the goal towards which any clustering algo rithm (e.g. node elimination) is directed. In this paper, we present an alternative concep tion of clustering, in which clusters and the junction tree property play the role of prim itives: given a graph (not a tree) of clusters which obey (a modified version of) the junc tion tree property, we transform this graph until we have obtained a tree. There are sev eral advantages to this approach: it is much clearer and easier to understand, which is important for humans who are constructing belief networks; it admits a wider range of heuristics which may enable more efficient or superior clustering algorithms; and it serves as the natural basis for an incremental clus tering scheme, which we describe.

Answer:
Clustering Without (Thinking About) Triangulation