An Ada programming support environment

The toolset of an Ada Programming Support Environment (APSE) being developed at North American Aircraft Operations (NAAO) of Rockwell International, is described. The APSE is resident on three different hosts and must support developments for the hosts and for embedded targets. Tools and developed software must be freely portable between the hosts. The toolset includes the usual editors, compilers, linkers, debuggers, configuration magnagers, and documentation tools. Generally, these are being supplied by the host computer vendors. Other tools, for example, pretty printer, cross referencer, compilation order tool, and management tools were obtained from public-domain sources, are implemented in Ada and are being ported to the hosts. Several tools being implemented in-house are of interest, these include an Ada Design Language processor based on compilable Ada. A Standalone Test Environment Generator facilitates test tool construction and partially automates unit level testing. A Code Auditor/Static Analyzer permits the Ada programs to be evaluated against measures of quality. An Ada Comment Box Generator partially automates generation of header comment boxes

Duality between Feature Selection and Data Clustering

The feature-selection problem is formulated from an information-theoretic perspective. We show that the problem can be efficiently solved by an extension of the recently proposed info-clustering paradigm. This reveals the fundamental duality between feature selection and data clustering,which is a consequence of the more general duality between the principal partition and the principal lattice of partitions in combinatorial optimization

Fast computation of the deviance information criterion for latent variable models

© 2014 Elsevier B.V. The deviance information criterion (DIC) has been widely used for Bayesian model comparison. However, recent studies have cautioned against the use of certain variants of the DIC for comparing latent variable models. For example, it has been argued that the conditional DIC–based on the conditional likelihood obtained by conditioning on the latent variables–is sensitive to transformations of latent variables and distributions. Further, in a Monte Carlo study that compares various Poisson models, the conditional DIC almost always prefers an incorrect model. In contrast, the observed-data DIC–calculated using the observed-data likelihood obtained by integrating out the latent variables–seems to perform well. It is also the case that the conditional DIC based on the maximum a posteriori (MAP) estimate might not even exist, whereas the observed-data DIC does not suffer from this problem. In view of these considerations, fast algorithms for computing the observed-data DIC for a variety of high-dimensional latent variable models are developed. Through three empirical applications it is demonstrated that the observed-data DICs have much smaller numerical standard errors compared to the conditional DICs. The corresponding MATLAB code is available upon request

A Bayesian Model Comparison for Trend-Cycle Decompositions of Output

© 2017 The Ohio State University We compare a number of widely used trend-cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions—different decompositions have broad implications for the relative importance of real versus nominal shocks in explaining variations in output. Using U.S. quarterly real GDP, we find that the overall best model is an unobserved components model with two features: (i) a nonzero correlation between trend and cycle innovations and (ii) a break in trend output growth in 2007. The annualized trend output growth decreases from about 3.4% to 1.2%–1.5% after the break. The results also indicate that real shocks are more important than nominal shocks. The slowdown in trend output growth is robust when we expand the set of models to include bivariate unobserved components models

Security and Privacy of Intelligent VANETs

Non

The Use of Kahoot Application in Comprehending Figurative Language

Smart phone as a device cannot be separated from our daily lives today. Many applications in smartphone are available to learn English language. One of them is kahoot application. The objective of this study was to compare the effectiveness of using kahoot application and traditional discussion method in learning figurative language. This was an experimental research. Two classes as control class and treatment class were observed by giving pretest and post test. Result indicated that students in the control class performed significantly better on post test than students in the treatment class. Further research needs to be conducted to combine the use of the application and conventional method