21 research outputs found
4. »Be Honest in Just–War–Thinking!« John H. Yoders Rezeption der Lehre vom gerechten Krieg
Software as Learning: Quality Factors and Life-Cycle Revised
. In this paper Software Development (SD) is understood explicitly as a learning process, which relies much more on induction than deduction, with the main goal of being predictive to requirements evolution. Concretely, classical processes from philosophy of science and machine learning such as hypothesis generation, refinement, confirmation and revision have their counterpart in requirement engineering, program construction, validation and modification in SD, respectively. Consequently, we have investigated the appropriateness for software modelling of the most important paradigms of modelling selection in machine learning. Under the notion of incremental learning, we introduce a new factor, predictiveness, as the ability to foresee future changes in the specification, thereby reducing the number of revisions. As a result, other quality factors are revised. Finally, a predictive software life cycle is outlined as an incremental learning session, which may or may not be aut..
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Early validation analyses of atmospheric profiles from EOS MLS on the aura Satellite
We present results of early validation studies using retrieved atmospheric profiles from the Earth Observing System Microwave Limb Sounder (MLS) instrument on the Aura satellite. "Global" results are presented for MLS measurements of atmospheric temperature, ozone, water vapor, hydrogen chloride, nitrous oxide, nitric acid, and carbon monoxide, with a focus on the January-March 2005 time period. These global comparisons are made using long-standing global satellites and meteorological datasets, as well as some measurements from more recently launched satellites. Comparisons of MLS data with measurements from the Ft. Sumner, NM, September 2004 balloon flights are also presented. Overall, good agreement is obtained, often within 5% to 10%, but we point out certain issues to resolve and some larger systematic differences; some artifacts in the first publicly released MLS (version 1.5) dataset are noted. We comment briefly on future plans for validation and software improvements