5453537
Testability of Software in Service-Oriented Architecture
Service-oriented architecture (SOA) is a system architecture in which a collection of loosely coupled services communicate with each other using standard interfaces and message-exchanging protocols. As an emerging technology in software development, the SOA presents a new paradigm, and it affects the entire software development cycle including analysis, specification, design, implementation, verification, validation, maintenance and evaluation. This paper proposes several testability evaluation criteria for SOA software, which serves as a reference for both service providers and application builders to evaluate the test support to SOA software. The proposed evaluation criteria are illustrated in a stock-trading case study
365943
Modeling and Storing Scientific Protocols
We propose an abstract model for scientific protocols, where several atomic operators are proposed for protocol composition We distinguish two different layers associated with scientific protocols: design and implementation, and discuss the mapping between them We illustrate our approach with a representative example and describe ProtocolDB, a scientific protocol repository currently in development Our approach benefits scientists by allowing the archiving of scientific protocols with the collected data sets to constitute a scientific portfolio for the laboratory to query, compare and revise protocols.
129842
BLAS: an efficient XPath processing system
We present BLAS, a Bi-LAbeling based System, for efficiently processing complex XPath queries over XML data. BLAS uses P-labeling to process queries involving consecutive child axes, and D-labeling to process queries involving descendant axes traversal. The XML data is stored in labeled form, and indexed to optimize descendent axis traversals. Three algorithms are presented for translating complex XPath queries to SQL expressions, and two alternate query engines are provided. Experimental results demonstrate that the BLAS system has a substantial performance improvement compared to traditional XPath processing using D-labeling.
369741
EXPedite: a system for encoded XML processing
As XML becomes an increasingly popular format for information exchange, the efficient processing of broadcast XML data on a constrained device (for example, a cell phone or a PDA) becomes a critical task. In this paper we present the EXPedite system: a new model of data processing in an information exchange environment, which "migrates" the power of the data-sending server to receivers for efficient processing. It consists of a simple and general encoding scheme for servers, and streaming query processing algorithms on encoded XML stream for data receivers with constrained computing abilities. Experiments show the impressive performance of EXPedite.
712
Rational Radial Distortion Models with Analytical Undistortion Formulae
The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as well as satisfactory accuracy. This paper presents a new class of rational radial distortion models with easy analytical undistortion formulae. Experimental results are presented to show that with this class of rational radial distortion models, satisfactory and comparable accuracy is achieved.
13487570
Texture classification using statistical geometrical features
This paper presents a new texture feature set based on the statistics of geometrical attributes of connected regions in a stack of binary images obtained from a texture image. Systematic evaluation using all the Brodatz textures shows that the new set outperforms the well-known statistical gray level dependence matrix, the recently proposed statistical feature matrix, and Liu's features.<<ETX>>
7510335
Intelligent Sensor Fusion And Learning For Autonomous Robot Navigation
ABSTRACT This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustnes...
17642246
Evolving an Experience Base for Software Process Research
Since 1996 the USC Center for Software Engineering has been accumulating a large amount of software process experience through many real-client project software engineering practices. Through the application of the Experience Factory approach, we have collected and evolved this experience into an experience base (eBASE) which has been leveraged successfully for empirically based software process research. Through eBASE we have realized tangible benefits in automating, organizational learning, and strategic advantages for software engineering research. We share our rationale for creating and evolving eBASE, give examples of how the eBASE has been used in recent process research, discuss current limitations and challenges with eBASE, and what we hope to do achieve in the future with it.
10897641
Reversible sketches for efficient and accurate change detection over network data streams
Traffic anomalies such as failures and attacks are increasing in frequency and severity, and thus identifying them rapidly and accurately is critical for large network operators. The detection typically treats the traffic as a collection of flows and looks for heavy changes in traffic patterns (<i>e.g.</i>, volume, number of connections). However, as link speeds and the number of flows increase, keeping per-flow state is not scalable. The recently proposed sketch-based schemes [14] are among the very few that can detect heavy changes and anomalies over massive data streams at network traffic speeds. However, sketches do not preserve the key (<i>e.g.</i>, source IP address) of the flows. Hence, even if anomalies are detected, it is difficult to infer the culprit flows, making it a big practical hurdle for online deployment. Meanwhile, the number of keys is too large to record.
 To address this challenge, we propose efficient <i>reversible hashing</i> algorithms to infer the keys of culprit flows from sketches without storing any explicit key information. No extra memory or memory accesses are needed for recording the streaming data. Meanwhile, the heavy change detection daemon runs in the background with space complexity and computational time sublinear to the key space size. This short paper describes the conceptual framework of the reversible sketches, as well as some initial approaches for implementation. See [23] for the optimized algorithms in details. comment We further apply various emph IP-mangling algorithms and emph bucket classification methods to reduce the false positives and false negatives. Evaluated with netflow traffic traces of a large edge router, we demonstrate that the reverse hashing can quickly infer the keys of culprit flows even for many changes with high accuracy.