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An electronic payment system ideally should provide security, anonymity, fairness, transferability and scal-ability. Existing payment schemes often lack either anonymity or scalability. In this paper we propose Who-Pay, a peer-to-peer payment system that provides all the above properties. For anonymity, we represent coins with public keys; for scalability,(More)
We address the problem of subselecting a large set of acoustic data to train automatic speech recognition (ASR) systems. To this end, we apply a novel data selection technique based on constrained submodular function maximization. Though NP-hard, the combinatorial optimization problem can be approximately solved by a simple and scalable greedy algorithm(More)
We conduct a comparative study on selecting subsets of acoustic data for training phone recognizers. The data selection problem is approached as a constrained submodular optimization problem. Previous applications of this approach required transcriptions or acoustic models trained in a supervised way. In this paper we develop and evaluate a novel and(More)
We apply methods for selecting subsets of dimensions from high-dimensional score spaces, and subsets of data for training, using submodular function optimization. Sub-modular functions provide theoretical performance guarantees while simultaneously retaining extremely fast and scal-able optimization via an accelerated greedy algorithm. We evaluate this(More)
We study the problem of selecting a subset of big data to train a classifier while incurring minimal performance loss. We show the connection of submodularity to the data likelihood functions for Na¨ıve Bayes (NB) and Nearest Neighbor (NN) classifiers, and formulate the data subset selection problems for these classifiers as constrained submodular(More)
1. Background • Generative acoustic score spaces, such as the Fisher score space, are widely used in speech processing, including acoustic event classification, acoustic-phonetic classification, segmental minimum Bayes risk decoding, and speaker verification. The drawback of these score space is their high dimensionality. • This work presents a(More)
We study two mixed robust/average-case submodular partitioning problems that we collectively call Submodular Partitioning. These problems generalize both purely robust instances of the problem (namely max-min submodular fair allocation (SFA) Golovin (2005) and min-max submodular load balancing (SLB) Svitkina and Fleischer (2008)) and also generalize(More)