The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities.Accordingly,an alternative approach is necessary where...The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities.Accordingly,an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods.We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities.This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach,which enables non-computer scientists to code.Therein,the learning content is provided in small tasks and embedded in learning factory scenarios.Therefore,different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated,yet condensed skills catalogue.This enables the development of training and education units that focus on the most important skills demanded on the market.To achieve this objective,individual learning scenarios are developed.Of course,proper basic skills in coding cannot be learned over night but software programming is also no sorcery.展开更多
We present a factorial representation of Gaussian mixture models for observation densities in Hidden Markov Models(HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for...We present a factorial representation of Gaussian mixture models for observation densities in Hidden Markov Models(HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm. We conduct several experiments to compare the performance of this model structure with Factorial Hidden Markov Models(FHMMs) and HMMs, some conclusions and promising empirical results are presented.展开更多
基金supported in part by the Junior Research Group“ProMUT”(01UU1705B)which is funded by the German Federal Ministry of Education and Research as part of its funding initiative“Social-Ecological Research”the German Internet Institute(16DII116).
文摘The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities.Accordingly,an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods.We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities.This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach,which enables non-computer scientists to code.Therein,the learning content is provided in small tasks and embedded in learning factory scenarios.Therefore,different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated,yet condensed skills catalogue.This enables the development of training and education units that focus on the most important skills demanded on the market.To achieve this objective,individual learning scenarios are developed.Of course,proper basic skills in coding cannot be learned over night but software programming is also no sorcery.
文摘We present a factorial representation of Gaussian mixture models for observation densities in Hidden Markov Models(HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm. We conduct several experiments to compare the performance of this model structure with Factorial Hidden Markov Models(FHMMs) and HMMs, some conclusions and promising empirical results are presented.
