An improved parser for data-oriented lexical-functional analysis

We present an LFG-DOP parser which uses fragments from LFG-annotated sentences to parse new sentences. Experiments with the Verbmobil and Homecentre corpora show that (1) Viterbi n best search performs about 100 times faster than Monte Carlo search while both achieve the same accuracy; (2) the DOP hypothesis which states that parse accuracy increases with increasing fragment size is confirmed for LFG-DOP; (3) LFG-DOP's relative frequency estimator performs worse than a discounted frequency estimator; and (4) LFG-DOP significantly outperforms Tree-DOP is evaluated on tree structures only.Comment: 8 page

Improved Conflict Detection for Graph Transformation with Attributes

In graph transformation, a conflict describes a situation where two alternative transformations cannot be arbitrarily serialized. When enriching graphs with attributes, existing conflict detection techniques typically report a conflict whenever at least one of two transformations manipulates a shared attribute. In this paper, we propose an improved, less conservative condition for static conflict detection of graph transformation with attributes by explicitly taking the semantics of the attribute operations into account. The proposed technique is based on symbolic graphs, which extend the traditional notion of graphs by logic formulas used for attribute handling. The approach is proven complete, i.e., any potential conflict is guaranteed to be detected.Comment: In Proceedings GaM 2015, arXiv:1504.0244

Dropout Inference in Bayesian Neural Networks with Alpha-divergences

To obtain uncertainty estimates with real-world Bayesian deep learning models, practical inference approximations are needed. Dropout variational inference (VI) for example has been used for machine vision and medical applications, but VI can severely underestimates model uncertainty. Alpha-divergences are alternative divergences to VI's KL objective, which are able to avoid VI's uncertainty underestimation. But these are hard to use in practice: existing techniques can only use Gaussian approximating distributions, and require existing models to be changed radically, thus are of limited use for practitioners. We propose a re-parametrisation of the alpha-divergence objectives, deriving a simple inference technique which, together with dropout, can be easily implemented with existing models by simply changing the loss of the model. We demonstrate improved uncertainty estimates and accuracy compared to VI in dropout networks. We study our model's epistemic uncertainty far away from the data using adversarial images, showing that these can be distinguished from non-adversarial images by examining our model's uncertainty

Business process modelling and visualisation to support e-government decision making: Business/IS alignment

© 2017 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-57487-5_4.Alignment between business and information systems plays a vital role in the formation of dependent relationships between different departments in a government organization and the process of alignment can be improved by developing an information system (IS) according to the stakeholders’ expectations. However, establishing strong alignment in the context of the eGovernment environment can be difficult. It is widely accepted that business processes in the government environment plays a pivotal role in capturing the details of IS requirements. This paper presents a method of business process modelling through UML which can help to visualise and capture the IS requirements for the system development. A series of UML models have been developed and discussed. A case study on patient visits to a healthcare clinic in the context of eGovernment has been used to validate the models

Objective speckle displacement: an extended theory for the small deformation of shaped objects

This paper describes an extended and improved theory of the displacement of the objective speckle pattern resulting from displacement and/or deformation of a coherently illuminated diffuse object. Using the theory developed by Yamaguchi [Opt. Acta 28, 1359 (1981)], extended expressions are derived that include the influence of surface shape/gradients via the first order approximation of the shape as linear surface gradients. Both the original Yamaguchi expressions and the extended form derived here are shown experimentally to break down as the detector position moves away from the z-axis. As such, improved forms of the expressions are then presented, which remove some of the approximations used by Yamaguchi and can be used to predict the objective speckle displacement over a wide range of detector positions and surface slopes. Finally, these expressions are then verified experimentally for the speckle shifts resulting from object translations

Sundanese Stemming using Syllable Pattern

Stemming is a technique to return the word derivation to the root or base word. Stemming is widely used for data processing such as searching word indexes, translating, and information retrieval from a document in the database. In general, stemming uses a morphological pattern from a derived word to produce the original word or root word.  In the previous research, this technique faced over-stemming and under-stemming problems. In this study, the stemming process will be improved by the syllable pattern (canonical) based on the phonological rule in Sundanese. The stemming result for syllable patterns gets an accuracy of 89% and the execution of the test data resulted in 95% from all the basic words. This simple algorithm has the advantage of being able to adjust the position of the syllable pattern with the word to be stemmed. Due to some data shortage constraints (typo, loan-word, non-deterministic word with syllable pattern), we can improve to increase the accuracy such as adjusting words and adding reference dictionaries. In addition, this algorithm has a drawback that causes the execution to be over-stemming

On the Exploitation of Admittance Measurements for Wired Network Topology Derivation

The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of Power Line Communications (PLC) networks it is helpful to implement data routing strategies, while in power distribution networks and Smart Micro Grids (SMG) it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm using values that are typical of power line distribution networks.Comment: A version of this manuscript has been submitted to the IEEE Transactions on Instrumentation and Measurement for possible publication. The paper consists of 8 pages, 11 figures, 1 tabl