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Application of a Relative Development Time Productivity Metric to Parallel Software Development

Published in:
SE-HPCS '05: Proceedings of the second international workshop on Software engineering for high performance computing system applications

Summary

Evaluation of High Performance Computing (HPC) systems should take into account software development time productivity in addition to hardware performance, cost, and other factors. We propose a new metric for HPC software development time productivity, defined as the ratio of relative runtime performance to relative programmer effort. This formula has been used to analyze several HPC benchmark codes and classroom programming assignments. The results of this analysis show consistent trends for various programming models. This method enables a high-level evaluation of development time productivity for a given code implementation, which is essential to the task of estimating cost associated with HPC software development.
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Summary

Evaluation of High Performance Computing (HPC) systems should take into account software development time productivity in addition to hardware performance, cost, and other factors. We propose a new metric for HPC software development time productivity, defined as the ratio of relative runtime performance to relative programmer effort. This formula has...

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The MIT Lincoln Laboratory RT-04F diarization systems: applications to broadcast audio and telephone conversations

Published in:
NIST Rich Transcription Workshop, 8-11 November 2004.

Summary

Audio diarization is the process of annotating an input audio channel with information that attributes (possibly overlapping) temporal regions of signal energy to their specific sources. These sources can include particular speakers, music, background noise sources, and other signal source/channel characteristics. Diarization has utility in making automatic transcripts more readable and in searching and indexing audio archives. In this paper we describe the systems developed by MITLL and used in DARPA EARS Rich Transcription Fall 2004 (RT-04F) speaker diarization evaluation. The primary system is based on a new proxy speaker model approach and the secondary system follows a more standard BIC based clustering approach. We present experiments analyzing performance of the systems and present a cross-cluster recombination approach that significantly improves performance. In addition, we also present results applying our system to a telephone speech, summed channel speaker detection task.
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Summary

Audio diarization is the process of annotating an input audio channel with information that attributes (possibly overlapping) temporal regions of signal energy to their specific sources. These sources can include particular speakers, music, background noise sources, and other signal source/channel characteristics. Diarization has utility in making automatic transcripts more readable...

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Two new experimental protocols for measuring speech transcript readability for timed question-answering tasks

Published in:
Proc. DARPA EARS Rich Translation Workshop, 8-11 November 2004.

Summary

This paper reports results from two recent psycholinguistic experiments that measure the readability of four types of speech transcripts for the DARPA EARS Program. The two key questions in these experiments are (1) how much speech transcript cleanup aids readability and (2) how much the type of cleanup matters. We employ two variants of the four-part figure of merit to measure readability defined at the RT02 workshop and described in our Eurospeech 2003 paper [4] namely: accuracy of answers to comprehension questions, reaction-time for passage reading, reaction-time for question answering and a subjective rating of passage difficulty. The first protocol employs a question-answering task under time pressure. The second employs a self-paced line-by-line paradigm. Both protocols yield similar results: all three types of clean-up in the experiment improve readability 5-10%, but the self-paced reading protocol needs far fewer subjects for statistical significance.
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Summary

This paper reports results from two recent psycholinguistic experiments that measure the readability of four types of speech transcripts for the DARPA EARS Program. The two key questions in these experiments are (1) how much speech transcript cleanup aids readability and (2) how much the type of cleanup matters. We...

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Robust collaborative multicast service for airborne command and control environment

Summary

RCM (Robust Collaborative Multicast) is a communication service designed to support collaborative applications operating in dynamic, mission-critical environments. RCM implements a set of well-specified message ordering and reliability properties that balance two conflicting goals: a)providing low-latency, highly-available, reliable communication service, and b) guaranteeing global consistency in how different participants perceive their communication. Both of these goals are important for collaborative applications. In this paper, we describe RCM, its modular and flexible design, and a collection of simple, light-weight protocols that implement it. We also report on several experiments with an RCM prototype in a test-bed environment.
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Summary

RCM (Robust Collaborative Multicast) is a communication service designed to support collaborative applications operating in dynamic, mission-critical environments. RCM implements a set of well-specified message ordering and reliability properties that balance two conflicting goals: a)providing low-latency, highly-available, reliable communication service, and b) guaranteeing global consistency in how different participants perceive...

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Testing static analysis tools using exploitable buffer overflows from open source code

Published in:
Proc. 12th Int. Symp. on Foundations of Software Engineering, ACM SIGSOFT, 31 October - 6 November 2004, pp. 97-106.

Summary

Five modern static analysis tools (ARCHER, BOON, PolySpace C Verifier, Splint, and UNO) were evaluated using source code examples containing 14 exploitable buffer overflow vulnerabilities found in various versions of Sendmail, BIND, and WU-FTPD. Each code example included a "BAD" case with and a "OK" case without buffer overflows. Buffer overflows varied and included stack, heap, bss and data buffers; access above and below buffer bounds; access using pointers, indices, and functions; and scope differences between buffer creation and use. Detection rates for the "BAD" examples were low except for PolySpace and Splint which had average detection rates of 87% and 57%, respectively. However, average false alarm rates were high and roughly 50% for these two tools. On patched programs these two tools produce one warning for every 12 to 46 lines of source code and neither tool accurately distinguished between vulnerable and patched code.
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Summary

Five modern static analysis tools (ARCHER, BOON, PolySpace C Verifier, Splint, and UNO) were evaluated using source code examples containing 14 exploitable buffer overflow vulnerabilities found in various versions of Sendmail, BIND, and WU-FTPD. Each code example included a "BAD" case with and a "OK" case without buffer overflows. Buffer...

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A comparison of soft and hard spectral subtraction for speaker verification

Published in:
8th Int. Conf. on Spoken Language Processing, ICSLP 2004, 4-8 October 2004.

Summary

An important concern in speaker recognition is the performance degradation that occurs when speaker models trained with speech from one type of channel are subsequently used to score speech from another type of channel, known as channel mismatch. This paper investigates the relative performance of two different spectral subtraction methods for additive noise compensation in the context of speaker verification. The first method, termed "soft" spectral subtraction, is performed in the spectral domain on the |DFT|^2 values of the speech frames while the second method, termed "hard" spectral subtraction, is performed on the Mel-filter energy features. It is shown through both an analytical argument as well as a simulation that soft spectral subtraction results in a higher signal-to-noise ratio in the resulting Mel-filter energy features. In the context of Gaussian mixture model-based speaker verification with additive noise in testing utterances, this is shown to result in an equal error rate improvement over a system without spectral subtraction of approximately 7% in absolute terms, 21% in relative terms, over an additive white Gaussian noise range of 5-25 dB.
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Summary

An important concern in speaker recognition is the performance degradation that occurs when speaker models trained with speech from one type of channel are subsequently used to score speech from another type of channel, known as channel mismatch. This paper investigates the relative performance of two different spectral subtraction methods...

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Group membership: a novel approach and the first single-round algorithm

Author:
Published in:
23rd ACM SIGACT-SIGOPS Symp. on Principles of Distributed Computing, PODC, 25-28 July 2004, pp. 347–356.

Summary

We establish a new worst-case upper bound on the Membership problem: We present a simple algorithm that is able to always achieve Agreement on Views within a single message latency after the final network events leading to stability of the group become known to the membership servers. In contrast, all of the existing membership algorithms may require two or more rounds of message exchanges. Our algorithm demonstrates that the Membership problem can be solved simpler and more efficiently than previously believed. By itself, the algorithm may produce disagreement (that is, inconsistent, transient views) prior to the "final" view. Even though this is allowed by the problem specification, such views may create overhead at the application level, and are therefore undesirable. We propose a new approach for designing group membership services in which our algorithm for reaching Agreement on Views is combined with a filter-like mechanism for reducing disagreements. This approach can use the mechanisms of existing algorithms, yielding the same multi-round performance as theirs. However, the power of this approach is in being able to use other mechanisms. These can be tailored to the specifics of the deployment environments and to the desired combinations of the speed of agreement vs. the amount of preceding disagreement. We describe one mechanism that keeps the combined performance to within a single-round, and sketch another two.
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Summary

We establish a new worst-case upper bound on the Membership problem: We present a simple algorithm that is able to always achieve Agreement on Views within a single message latency after the final network events leading to stability of the group become known to the membership servers. In contrast, all...

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Next-generation technologies to enable sensor networks

Published in:
Handbook of Sensor Networks, Chapter 2

Summary

Examples are advances in ground moving target indicator (GMTI) processing, space-time adaptive processing (STAP), target discrimination, and electronic counter-countermeasures (ECCM). All these advances have improved the capabilities of radar sensors. Major improvements expected in the next several years will come from exploiting collaborative network-centric architectures to leverage synergies among individual sensors. Such an approach has become feasible as a result of major advances in network computing, as well as communication technologies in both wireless and fiber networks. The exponential growth of digital technology, together with highly capable networks, enable in-depth exploitation of sensor synergy, including multi-aspect sensing. New signal processing algorithms exploiting multi-sensor data have been demonstrated in non-real-time, achieving improved performance against surface mobile targets by leveraging high-speed sensor networks. The paper demonstrates a significant advancement in exploiting complex ground moving target indicator (GMTI) and synthetic aperture radar (SAR) data to accurately geo-locate and identify mobile targets.
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Summary

Examples are advances in ground moving target indicator (GMTI) processing, space-time adaptive processing (STAP), target discrimination, and electronic counter-countermeasures (ECCM). All these advances have improved the capabilities of radar sensors. Major improvements expected in the next several years will come from exploiting collaborative network-centric architectures to leverage synergies among individual...

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Channel compensation for SVM speaker recognition

Published in:
Odyssey, The Speaker and Language Recognition Workshop, 31 May - 3 June 2004.

Summary

One of the major remaining challenges to improving accuracy in state-of-the-art speaker recognition algorithms is reducing the impact of channel and handset variations on system performance. For Gaussian Mixture Model based speaker recognition systems, a variety of channel-adaptation techniques are known and available for adapting models between different channel conditions, but for the much more recent Support Vector Machine (SVM) based approaches to this problem, much less is known about the best way to handle this issue. In this paper we explore techniques that are specific to the SVM framework in order to derive fully non-linear channel compensations. The result is a system that is less sensitive to specific kinds of labeled channel variations observed in training.
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Summary

One of the major remaining challenges to improving accuracy in state-of-the-art speaker recognition algorithms is reducing the impact of channel and handset variations on system performance. For Gaussian Mixture Model based speaker recognition systems, a variety of channel-adaptation techniques are known and available for adapting models between different channel conditions...

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Fusing discriminative and generative methods for speaker recognition: experiments on switchboard and NFI/TNO field data

Published in:
ODYSSEY 2004, Speaker and Language Recognition Workshop, 31 May - 3 June 2004.

Summary

Discriminatively trained support vector machines have recently been introduced as a novel approach to speaker recognition. Support vector machines (SVMs) have a distinctly different modeling strategy in the speaker recognition problem. The standard Gaussian mixture model (GMM) approach focuses on modeling the probability density of the speaker and the background (a generative approach). In contrast, the SVM models the boundary between the classes. Another interesting aspect of the SVM is that it does not directly produce probabilistic scores. This poses a challenge for combining results with a GMM. We therefore propose strategies for fusing the two approaches. We show that the SVM and GMM are complementary technologies. Recent evaluations by NIST (telephone data) and NFI/TNO (forensic data) give a unique opportunity to test the robustness and viability of fusing GMM and SVM methods. We show that fusion produces a system which can have relative error rates 23% lower than individual systems.
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Summary

Discriminatively trained support vector machines have recently been introduced as a novel approach to speaker recognition. Support vector machines (SVMs) have a distinctly different modeling strategy in the speaker recognition problem. The standard Gaussian mixture model (GMM) approach focuses on modeling the probability density of the speaker and the background...

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