We have developed a new, robust clustering algorithm, Self-Splitting Competitive Learning (SSCL). It has shown great abilities in detecting not only isolated clusters, but overlapped clusters, curves and spherical she...
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We have developed a new, robust clustering algorithm, Self-Splitting Competitive Learning (SSCL). It has shown great abilities in detecting not only isolated clusters, but overlapped clusters, curves and spherical shells. We apply SSCL to quantization of color images. The clustering algorithm iteratively partitions the color space into natural clusters without a prior information on the number of clusters. The algorithm starts with only a single color prototype and adaptively splits it into multiple prototypes during the learning process based on a split validity measure. It is able to discover all natural groups; each is associated with a color prototype. The experimental results show remarkably better performance as compared to several other existing clustering algorithms.
software architectural styles that represent structural characteristics of software programs range from specific ones that can be applied to a particular domain to generic ones that can be applied to any domain. If a ...
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software architectural styles that represent structural characteristics of software programs range from specific ones that can be applied to a particular domain to generic ones that can be applied to any domain. If a specific architectural style is available for the target system to be developed, it is appropriate to apply it together with its associated modeling method. However, no quantitative evaluation on the efficiency of specific architectural styles has as yet been reported. This paper presents a quantitative comparison of two architectural styles: specific and generic software architectural styles. The comparison shows that a specific architectural style combined with its associated modeling method allows us to reduce modeling cost as much as a few scores of percent compared with the generic one combined with its modeling method. The improvement results from the characteristics that (1) a specific software architectural style requires less rewriting of modeling diagrams due to its inherent basic structure and (2) there is less redundant information among modeling diagrams.
Transform coefficients carry important data characteristics but can also be compressed significantly in many remote sensing applications. Failures in the several computing facilities that execute lossy compression alg...
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Transform coefficients carry important data characteristics but can also be compressed significantly in many remote sensing applications. Failures in the several computing facilities that execute lossy compression algorithms and support the transmission of Fourier transform data can corrupt the values beyond recovery at the final destination. Various methods for including fault tolerance at the data processing level are exemplified by describing a protected system that computes the FFT, truncates small coefficients and compresses the remaining nonzero coefficients using lossless arithmetic coding. Algorithmic checks within the FFT and arithmetic encoding and decoding operations are augmented with additional features between and across several subsystems involved in compressing and transmitting the FFT data. End-to-end error detection is achieved in this manner.
Presents the results of the third international graphics recognition contest. We evaluated the performance of four different vectorization systems on scanned images of CAD drawings. The EditCost Index was used for com...
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ISBN:
(纸本)0769507506
Presents the results of the third international graphics recognition contest. We evaluated the performance of four different vectorization systems on scanned images of CAD drawings. The EditCost Index was used for comparative evaluation of the systems. The performance curves of the systems on the test images are presented in this paper.
Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality...
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Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality of the delivered software and thus on a company's business reputation and competitiveness. On the other hand, overestimation of software cost can result in missed opportunities to funds in other projects. In response to industry demand, a myriad of estimation techniques has been proposed during the last three decades. In order to assess the suitability of a technique from a diverse selection, its performance and relative merits must be compared. The current study replicates a comprehensive comparison of common estimation techniques within different organizational contexts, using data from the European Space Agency. Our study is motivated by the challenge to assess the feasibility of using multi-organization data to build cost models and the benefits gained from company-specific data collection. Using the European Space Agency data set, we investigated a yet unexplored application domain, including military and space projects. The results showed that traditional techniques, namely, ordinary least-squares regression and analysis of variance outperformed analogy-based estimation and regression trees. Consistent with the results of the replicated study no significant difference was found in accuracy between estimates derived from company-specific data and estimates derived from multi-organizational data.
We present an approach to social reasoning that integrates prior work on norms and obligations with the BDI approach to agent architectures. Norms and obligations can be used to increase the efficiency of agent reason...
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We present an approach to social reasoning that integrates prior work on norms and obligations with the BDI approach to agent architectures. Norms and obligations can be used to increase the efficiency of agent reasoning, and their explicit representation supports reasoning about a wide range of behaviour types in a single framework. We propose a modified BDI interpreter loop that takes norms and obligations into account in an agent's deliberation.
Linear repetitive processes are a distinct class of 2D linear systems of both theoretical and practical interest. The stability theory for these processes currently consists of two distinct concepts termed asymptotic ...
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Linear repetitive processes are a distinct class of 2D linear systems of both theoretical and practical interest. The stability theory for these processes currently consists of two distinct concepts termed asymptotic stability and stability along the pass respectively where the former is a necessary condition for the latter. Recently applications have arisen where asymptotic stability is too weak and stability along the pass is too strong for meaningful progress to be made. This paper develops the concept of strong practical stability for such cases.
Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality...
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ISBN:
(纸本)9781581132069
Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality of the delivered software and thus on a company's business reputation and competitiveness. On the other hand, overestimation of software cost can result in missed opportunities to funds in other projects. In response to industry demand, a myriad of estimation techniques has been proposed during the last three decades. In order to assess the suitability of a technique from a diverse selection, its performance and relative merits must be *** current study replicates a comprehensive comparison of common estimation techniques within different organizational contexts, using data from the European Space Agency. Our study is motivated by the challenge to assess the feasibility of using multi-organization data to build cost models and the benefits gained from company-specific data collection. Using the European Space Agency data set, we investigated a yet unexplored application domain, including military and space projects. The results showed that traditional techniques, namely, ordinary least-squares regression and analysis of variance outperformed Analogy-based estimation and regression trees. Consistent with the results of the replicated study no significant difference was found in accuracy between estimates derived from company-specific data and estimates derived from multi-organizational data.
Opus is a new programming language designed to assist in coordinating the execution of multiple, independent program modules. With the help of Opus, coarse grained task parallelism between data parallel modules can be...
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