11798209
A Hardware-Assisted Tool for Fast, Full Code Coverage Analysis
Software reliability can be improved by using code coverage analysis to ensure that all statements are executed at least once during the testing process. When full code coverage information is obtained through software code instrumentation, high runtime performance overheads are incurred. Techniques that perform deferred or selective code instrumentation have shown success in reducing run-time overheads; however, the execution profile remains distorted. Techniques have been proposed that use internal processor hardware during the data gathering process, e.g. program counter logging. These approaches have been shown to reduce overheads; but currently trade swift execution for sparse code coverage. By combining the branch-vector hardware designed for debugging modern embedded processors with on-demand code coverage analysis, we have developed a new tool which provides full code coverage, while minimizing performance distortions. Experimental results show a performance impact of only 8 - 12%, while still providing 100% code coverage information.
8493649
Empirical analysis of the correlation between amount-of-reuse metrics in the C programming language
Disclosed are the magnesium salts of N-carboxyamino acids, a process for their preparation, and their use of lubricating oil additives.
18665149
Owlgres: A Scalable OWL Reasoner
We present Owlgres, a DL-Lite reasoner implementation written for PostgreSQL, a mature open source database. Owlgres is an OWL reasoner that provides consistency checking and conjunctive query services, supports DL-LiteR as well as the OWL sameAs construct, and is not limited to PostgreSQL. We discuss the implementation with special focus on sameAs and the supported subset of the SPARQL language. Emphasis is given to the implemented optimization techniques which resulted in significant performance improvement. Based on a confidential NASA dataset and part of the DBpedia dataset, we show a typical use case for Owlgres, i.e. given a terminology and a dataset, Owlgres provides querying on a persistent knowledge base with reasoning at query time in the expressivity of DL-LiteR.
20760479
The CORC experience: survey of founding libraries. Part II
30141926
Nonparametric regression using linear combinations of basis functions
This paper discusses a Bayesian approach to nonparametric regression initially proposed by Smith and Kohn (1996. Journal of Econometrics 75: 317–344). In this approach the regression function is represented as a linear combination of basis terms. The basis terms can be univariate or multivariate functions and can include polynomials, natural splines and radial basis functions. A Bayesian hierarchical model is used such that the coefficient of each basis term can be zero with positive prior probability. The presence of basis terms in the model is determined by latent indicator variables. The posterior mean is estimated by Markov chain Monte Carlo simulation because it is computationally intractable to compute the posterior mean analytically unless a small number of basis terms is used. The present article updates the work of Smith and Kohn (1996. Journal of Econometrics 75: 317–344) to take account of work by us and others over the last three years. A careful discussion is given to all aspects of the model specification, function estimation and the use of sampling schemes. In particular, new sampling schemes are introduced to carry out the variable selection methodology.
166928377
Promoting health and productivity in the computerized office
29590207
Combining spatial and scale-space techniques for edge detection to provide a spatially adaptive wavelet-based noise filtering algorithm
New methods for detecting edges in an image using spatial and scale-space domains are proposed. A priori knowledge about geometrical characteristics of edges is used to assign a probability factor to the chance of any pixel being on an edge. An improved double thresholding technique is introduced for spatial domain filtering. Probabilities that pixels belong to a given edge are assigned based on pixel similarity across gradient amplitudes, gradient phases and edge connectivity. The scale-space approach uses dynamic range compression to allow wavelet correlation over a wider range of scales. A probabilistic formulation is used to combine the results obtained from filtering in each domain to provide a final edge probability image which has the advantages of both spatial and scale-space domain methods. Decomposing this edge probability image with the same wavelet as the original image permits the generation of adaptive filters that can recognize the characteristics of the edges in all wavelet detail and approximation images regardless of scale. These matched filters permit significant reduction in image noise without contributing to edge distortion. The spatially adaptive wavelet noise-filtering algorithm is qualitatively and quantitatively compared to a frequency domain and two wavelet based noise suppression algorithms using both natural and computer generated noisy images.