We describe a procedure for quantitative evaluation of interactive question-answering systems and illustrate it with application to the High-Quality Interactive Question-Answering (HITIQA) system. Our objectives were (a) to design a method to realistically and reliably assess interactive question-answering systems by comparing the quality of reports produced using different systems, (b) to conduct a pilot test of this method, and (c) to perform a formative evaluation of the HITIQA system. Far more important than the specific information gathered from this pilot evaluation is the development of (a) a protocol for evaluating an emerging technology, (b) reusable assessment instruments, and (c) the knowledge gained in conducting the evaluation. We conclude that this method, which uses a surprisingly small number of subjects and does not rely on predetermined relevance judgments, measures the impact of system change on work produced by users. Therefore this method can be used to compare the product of interactive systems that use different underlying technologies. © 2007 Wiley Periodicals, Inc.

A model for quantitative evaluation of an end-to-end question-answering system

Increasing interest is being shown in the use of Java for large scale or Grande applications. This new use of Java places specific demands on the Java execution environments that can be tested using the Java Grande benchmark suite [5], [6], [7]. The large processing requirements of Grande applications makes parallelisation of interest. A suite of parallel benchmarks has been developed from the serial Java Grande benchmark suite, using three parallel programming models: Java native threads, MPJ (a message passing interface) and JOMP (a set of OpenMP-like directives). The contents of the suite are described, and results presented for a number of platforms.

A Parallel Java Grande Benchmark Suite

This paper describes the functionality and software archit e ture of a generic problem-solving environment (PSE) for collaborative computational science and enginee ri g. The PSE is designed to provide transparent access to heterogeneous distributed computing resources, and is intended to enhance research productivity by making it easier to construct, run, and analyze the result s of computer simulations. Although implementation details are not discussed in depth, the role of sof tware technologies such as CORBA, Java, and XML is outlined. An XML-based component model is presented. The main features of a Visual Component Composition Environment for software development andIntelligent Resource Management System for scheduling components are described. Some prototype im lementations of PSE sub-systems are also presented.

Concurrency: Practice and Experience

Designing, tuning, and analyzing the performance of distributed algorithms and protocols are complex tasks. A major factor that contributes to this complexity is the fact that there is no single environment to support all phases of the development of a distributed algorithm. This paper presents Neko, an easy to use Java platform that provides a uniform and extensible environment for the various phases of algorithm design and performance evaluation: prototyping, tuning, simulation, deployment, etc.

/pdf/neko-a-single-environment-to-simulate-and-prototype-1xny7mpfp4.pdf

Neko: a single environment to simulate and prototype distributed algorithms

The SURA Coastal Ocean Observing and Prediction (SCOOP) program is using geographical information system (GIS) technologies to visualize and integrate distributed data sources from across the United States and Canada. Hydrodynamic models are run at different sites on a developing multi-institutional computational Grid. Some of these predictive simulations of storm surge and wind waves are triggered by tropical and subtropical cyclones in the Atlantic and the Gulf of Mexico. Model predictions and observational data need to be merged and visualized in a geospatial context for a variety of analyses and applications. A data archive at LSU aggregates the model outputs from multiple sources, and a data-driven workflow triggers remotely performed conversion of a subset of model predictions to georeferenced data sets, which are then delivered to a Web Map Service located at Texas A&M University. Other nodes in the distributed system aggregate the observational data. This paper describes the use of GIS within the SCOOP program for the 2005 hurricane season, along with details of the data-driven distributed dataflow and workflow, which results in geospatial products. We also focus on future plans related to the complimentary use of GIS and Grid technologies in the SCOOP program, through which we hope to provide a wider range of tools that can enhance the tools and capabilities of earth science research and hazard planning. Copyright © 2008 John Wiley & Sons, Ltd.

Towards an integrated GIS-based coastal forecast workflow

A basic prerequisite for parallel programming is a good communication API The recent interest in using Java for scientific and engineering application has led to several international efforts to produce a message passing interface to support parallel computation In this paper we describe and then discuss the syntax, functionality and performance of one such interface, mpiJava, an object-oriented Java interface to MPI We first discuss the design of the mpiJava API and the issues associated with its development We then more on to briefly outline the steps necessary to ‘port’ mpiJava onto a range of operating systems, including Windows NT, Linux and Solaris In the second part of the paper we present and then discuss some performance measurements made of communications bandwidth and latency to compare mpiJava on these systems Finally, we summarise our experiences and then briefly mention work that we plan to undertake

/pdf/mpijava-an-object-oriented-java-interface-to-mpi-4yq5f1dz6w.pdf

MPIJAVA: An Object-Oriented JAVA Interface to MPI

Several Java bindings to Message Passing Interface (MPI) software have been developed recently. Message bu ers have usually been restricted to arrays with elements of primitive type. We discuss adoption of the Java object serialization model for marshalling general communication data in MPI-like APIs. This approach is compared with a Java transcription of the standard MPI derived datatype mechanism. We describe an implementation of the mpiJava interface to MPI that incorporates automatic object serialization. Benchmark results con rm that current JDK implementations of serialization are not fast enough for high performance messaging applications. Means of solving this problem are discussed, and benchmarks for greatly improved schemes are presented.

Object serialization for marshalling data in a Java interface to MPI

/pdf/mpijava-1-2-api-specification-3sus2modhf.pdf

mpiJava 1.2: API Specification

http://www.naradabrokering.org/papers/cellularGrid_final.pdf

Message-based cellular peer-to-peer grids: foundations for secure federation and autonomic services

Object serialization for marshaling data in a Java interface to MPI

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sang Lim

Author Tools

Chat about Author