The goal of the speech segments extraction process is to separate acoustic events of interest (the speech segment to be recognised) in a continuously recorded signal from other parts of the signal (background). The recognition rate of many voice command systems is very much dependent on speech segment extraction accuracy. This paper discusses two novel HMM based techniques that segregate a speech segment from its concurrent background. The first method can be reliably used in clean environments while the second method, which makes use of the wavelets denoising technique, is effective in noisy environments. These methods have been implemented and shown superiority over other popular techniques, thus, indicating that they have the potential to achieve greater levels of accuracy in speech recognition rates.

Waleed H. Abdulla

HMM-Based Techniques for Speech Segments Extraction

Hidden Markov Models(HMM) have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

Cen Li

Gautam Biswas

A Bayesian Approach for Structural Learning with Hidden Markov Models

Performance Engineering is concerned with the reliable prediction and estimation of the performance of scientific and engineering applications on a variety of parallel and distributed hardware. This paper reviews the present state of the art in 'Performance Engineering' for both parallel computing and meta-computing environments and attempts to look forward to the application of these techniques in the wider context of Problem Solving Environments and the Grid. The paper compares various techniques such as benchmarking, performance measurements, analytical modelling and simulation, and highlights the lessons learned in the related projects. The paper concludes with a discussion of the challenges of extending such methodologies to computational Grid environments.

Tony Hey

Juri Papay

Performance Engineering, PSEs and the GRID

Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

Ingmar Visser

Maartje E.J. Raijmakers

Peter C.M. Molenaar

Fitting Hidden Markov Models to Psychological Data

Chris Bystroff

Introduction

The Grid-based Virtual Laboratory AMsterdam (VLAM-G), provides a science portal for distributed analysis in applied scientific research. It offers scientists remote experiment control, data management facilities and access to distributed resources by providing cross-institutional integration of information and resources in a familiar environment. The main goal is to provide a unique integration of existing standards and software packages. This paper describes the design and prototype implementation of the VLAM-G platform. In this testbed we applied several recent technologies such as the Globus toolkit, enhanced federated database systems, and visualization and simulation techniques. Several domain specific case studies are described in some detail. Information management will be discussed separately in a forthcoming paper.

H. Afsarmanesh

R.G. Belleman

A.S.Z. Belloum

A. Benabdelkader

J.F.J. van den Brand

G.B. Eijkel

A. Frenkel

C. Garita

D.L. Groep

R.M.A. Heeren

Z.W. Hendrikse

L.O. Hertzberger

J.A. Kaandorp

E.C. Kaletas

V. Korkhov

C.T.A.M. de Laat

P.M.A. Sloot

D. Vasunin

A. Visser

H.H. Yakali

VLAM-G: A Grid-Based Virtual Laboratory

CX, a network-based computational exchange, is presented. The system's design integrates variations of ideas from other researchers, such as work stealing, non-blocking tasks, eager scheduling, and space-based coordination. The object-oriented API is simple, compact, and cleanly separates application logic from the logic that supports interprocess communication and fault tolerance. Computations, of course, run to completion in the presence of computational hosts that join and leave the ongoing computation. Such hosts, or producers, use task caching and prefetching to overlap computation with interprocessor communication. To break a potential task server bottleneck, a network of task servers is presented. Even though task servers are envisioned as reliable, the self-organizing, scalable network of n- servers, described as a sibling-connected height-balanced fat tree, tolerates a sequence of n-1 server failures. Tasks are distributed throughout the server network via a simple &#x22;diffusion&#x22; process. CX is intended as a test bed for research on automated silent auctions, reputation services, authentication services, and bonding services. CX also provides a test bed for algorithm research into network-based parallel computation.

Peter Cappello

Dimitrios Mourloukos

CX: A Scalable, Robust Network for Parallel Computing

UNICORE (Uniform Interface to Computer Resources) is a software infrastructure supporting seamless and secure access to distributed resources. UNICORE allows uniform access to different hardware and software platforms as well as different organizational environments. Based on the abstract job model it offers services for security, translation of abstract jobs into real batch jobs for different target systems, and a public key infrastructure. This paper describes the UNICORE architecture and the services provided.

Mathilde Romberg

The UNICORE Grid Infrastructure

Resource management is an important component of a grid computing infrastructure. The scalability and adaptability of such systems are two key challenges that must be addressed. In this work an agent-based resource management system, ARMS, is implemented for grid computing. ARMS utilises the performance prediction techniques of the PACE toolkit to provide quantitative data regarding the performance of complex applications running on a local grid resource. At the meta-level, a hierarchy of homogeneous agents are used to provide a scalable and adaptable abstraction of the system architecture. Each agent is able to cooperate with other agents and thereby provide service advertisement and discovery for the scheduling of applications that need to utilise grid resources. A case study with corresponding experimental results is included to demonstrate the efficiency of the resource management and scheduling system.

Junwei Cao

Stephen A. Jarvis

Subhash Saini

Darren J. Kerbyson

Graham R. Nudd

ARMS: An Agent-Based Resource Management System for Grid Computing

The GDMP client-server software system is a generic file replication tool that replicates files securely and efficiently from one site to another in a Data Grid environment using Globus Grid tools. In addition, it manages replica catalogue entries for file replicas and thus maintains a consistent view on names and locations of replicated files. Files to be transferred can be of any particular file format and GDMP treats them all in the same way. However, for Objectivity database files a particular plug-in exists. All files are assumed to be read-only.

Heinz Stockinger

Asad Samar

Shahzad Muzaffar

Flavia Donno

Grid Data Mirroring Package (GDMP)

Portals to computational/data grids provide the scientific community with a friendly environment in order to solve large-scale computational problems. The Grid Resource Broker (GRB) is a grid portal that allows trusted users to create and handle computational/data grids on the fly exploiting a simple and friendly web-based GUI. GRB provides location-transparent secure access to Globus services, automatic discovery of resources matching the user's criteria, selection and scheduling on behalf of the user. Moreover, users are not required to learn Globus and they do not need to write specialized code or to rewrite their existing legacy codes. We describe GRB architecture, its components and current GRB features addressing the main differences between our approach and related work in the area.

Giovanni Aloisio

Massimo Cafaro

Euro Blasi

Italo Epicoco

The Grid Resource Broker, A Ubiquitous Grid Computing Framework

The design and implementation of a national computing system and data grid has become a reachable goal from both the computer science and computational science point of view. A distributed infrastructure capable of sophisticated computational functions can bring many benefits to scientific work, but poses many challenges, both technical and socio-political. Technical challenges include having basic software tools, higher-level services, functioning and pervasive security, and standards, while socio-political issues include building a user community, adding incentives for sites to be part of a user-centric environment, and educating funding sources about the needs of this community. This paper details the areas relating to Grid research that we feel still need to be addressed to fully leverage the advantages of the Grid.

Jennifer M. Schopf

Bill Nitzberg

Grids: The Top Ten Questions

Ewa Deelman

Carl Kesselman

Grid Computing

Michael Gerndt

PADDA 2001

About this journal

Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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Chief Editor&nbsp;Professor Tramontana&nbsp;is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.

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Scientific Programming - Table of contents 2002

title

Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

Scientific Programming