Structured Training for Neural Network Transition-Based Parsing

We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide in-depth ablative analysis to determine which aspects of our model provide the largest gains in accuracy

Differential evolution technique on weighted voting stacking ensemble method for credit card fraud detection

Differential Evolution is an optimization technique of stochastic search for a population-based vector, which is powerful and efficient over a continuous space for solving differentiable and non-linear optimization problems. Weighted voting stacking ensemble method is an important technique that combines various classifier models. However, selecting the appropriate weights of classifier models for the correct classification of transactions is a problem. This research study is therefore aimed at exploring whether the Differential Evolution optimization method is a good approach for defining the weighting function. Manual and random selection of weights for voting credit card transactions has previously been carried out. However, a large number of fraudulent transactions were not detected by the classifier models. Which means that a technique to overcome the weaknesses of the classifier models is required. Thus, the problem of selecting the appropriate weights was viewed as the problem of weights optimization in this study. The dataset was downloaded from the Kaggle competition data repository. Various machine learning algorithms were used to weight vote a class of transaction. The differential evolution optimization techniques was used as a weighting function. In addition, the Synthetic Minority Oversampling Technique (SMOTE) and Safe Level Synthetic Minority Oversampling Technique (SL-SMOTE) oversampling algorithms were modified to preserve the definition of SMOTE while improving the performance. Result generated from this research study showed that the Differential Evolution Optimization method is a good weighting function, which can be adopted as a systematic weight function for weight voting stacking ensemble method of various classification methods.School of ComputingM. Sc. (Computing

Gemeten actuele verdamping voor twaalf locaties in Nederland

Stowa heeft Alterra de opdracht gegeven jaarreeksen van actuele dagelijkse verdamping af te leiden voor twaalf meetlocaties in Nederland. Daarbij is gebruik gemaakt van bestaande (micro)meteorologische meetgegevens. De meetgegevens zijn gecontroleerd op kwaliteit en continuïteit en ontbrekende dagtotalen zijn aangevuld met door een Artificieel Neuraal Netwerk gesimuleerde gegevens. De onzekerheid in de jaartotalen van de bepaalde actuele verdamping ligt tussen de 10 en 15%

Neural Networks for cost estimating in project management

This thesis considers the application of neural networks in cost estimating in project management and whether they lead to more accurate estimates. It strikes two areas of research, namely neural networks and project management; an introductory chapter on both subjects is included. The statistical problem of parametric cost estimating is described and an explanation of the general principles is given. The Multi-Layer Perceptron with the Backpropagation learning algorithm is determined to be the most appropriate network and a selection of available software programs is reviewed. A Multi-Layer Perceptron neural model is used to determine one of the most important cost estimating relationships of the PRICE model. A comparison of the outputs of the neural network and the PRICE model shows that the Backpropagation algorithm is able to find the underlying estimating relationships used by PRJCE. To investigate whether other underlying functions can be found with artificial intelligence methods, other input parameters are selected and the costs generated by the PRICE model and by the neural network are compared with each other. Further experiments were undertaken in order to improve the performance of the neural network. The neural networks were applied to real data. and their output compared with the PRICE model. The processes of achieving better results are analogous to those used for the artificial data. A neural network was created which performs better than the PRICE model in terms of the accuracy of the estimates produced. The results are discussed and the collection of significant and accurate information and then deciding on which type of network is the best network to be used are identified as the major problems in the application of artificial intelligence for cost estimation in project management. The limitations and restrictions of the implementation of neural networks are examined and the scope and topics of further research are suggested

Search for the Standard Model Higgs Boson in Associated Production with W Boson at the Tevatron.

A search for the Standard Model Higgs boson in proton-antiproton collisions with center-of-mass energy 1.96 TeV at the Tevatron is presented in this dissertation. The process of interest is the associated production of W boson and Higgs boson, with the W boson decaying leptonically and the Higgs boson decaying into a pair of bottom quarks. The dataset in the analysis is accumulated by the D0 detector from April 2002 to April 2008 and corresponding to an integrated luminosity of 2.7 fb$^{-1}$. The events are reconstructed and selected following the criteria of an isolated lepton, missing transverse energy and two jets. The D0 Neural Network b-jet identification algorithm is further used to discriminate b jets from light jets. A multivariate analysis combining Matrix Element and Neural Network methods is explored to improve the Higgs boson signal significance. No evidence of the Higgs boson is observed in this analysis. In consequence, an observed (expected) limit on the ratio of $sigma$ (ppbar $rightarrow$ WH) $times$ Br (H $rightarrow$ bbbar) to the Standard Model prediction is set to be 6.7 (6.4) at $95$ $C.L.$ for the Higgs boson with a mass of 115 GeV.Ph.D.PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75869/1/xuchun_1.pd

Advanced Digital Auditing

This open access book discusses the most modern approach to auditing complex digital systems and technologies. It combines proven auditing approaches, advanced programming techniques and complex application areas, and covers the latest findings on theory and practice in this rapidly developing field. Especially for those who want to learn more about novel approaches to testing complex information systems and related technologies, such as blockchain and self-learning systems, the book will be a valuable resource. It is aimed at students and practitioners who are interested in contemporary technology and managerial implications