Semantic Mediation for Standard-Based B2B Interoperability

The authors discuss a semantic-mediation architecture to advance traditional approaches for standards-based business-to-business (B2B) interoperability. The architecture is supported by the Athena Knowledge Representation and Semantics Mediation tool suite. Initial experimentations with the architecture and the toolset offer discussions of key architectural and functional aspects and suggest directions for future tools enhancements.     

Plaster materials from waste calcium sulfate containing chemicals,organic fibers and inorganic additives

Plaster materials made of waste gypsum or flue-gas-desulferized (FGD) gypsum with chemicals;organic and inorganic additives were studied. Glucose;citric acid and sodium bicarbonate were incorporated to retard the hydration of plaster. Saw dust (SD);coconut fibers (CCF) and tobacco waste fiber (TWF) were incorporated to improve the thermal property. Diatomaceous earth (DE);fly ash (FA) and bottom ash (BA) were incorporated to improve the mechanical and thermal properties. Citric acid;TWF;sodium bicarbonate and glucose could be used to retard the setting time of fresh FGD-plaster to approximately 25 min comparable to that of commercial plaster while the other additives did not retard the hydration. In presences of these retarding additives;needle shaped gypsum changed into lower aspect ratio particles. SD;CCF;DE;FA and BA modified gypsum crystal growth and reduced the crystal length. These changes in morphology consequently gave significant alterations of mechanical and thermal properties of the materials. The additions of organic and inorganic additives resulted in a reduction of bulk density and increases in water absorption;and similar strength compared to commercial gypsum. A good thermal insulating property was obtained from the samples with the incorporation of coconut fiber;BA and DE. In addition;these samples had a good performance in fire proof.     

Model-driven integration using existing models

Models can describe business transactions in terms of business entities and help automate some systems integration tasks. The joint action model describes a new business transaction motivating systems integration.     

Improved geopolymerization of bottom ash by incorporating fly ash and using waste gypsum as additive

This research studied the improvement of the geopolymerization of bottom ash (BA) by incorporating fly ash (FA) and using flue gas desulfurization gypsum (FGDG) as additive. The BA:FA ratios of 100:0, 75:25, 50:50, 25:75, and 0:100 were used as the blended source materials. The source materials were then replaced with 0%, 5%, 10%, and 15% of FGDG. NaOH, sodium silicate and temperature curing were used to activate the geopolymer. Test results indicated that the increase in FA content in the BA–FA blends improved the strengths of geopolymer mortars owing to the high glassy phase content and high reactivity of FA compared to those of BA. The use of up to 10% of FGDG as additive also significantly increased the strengths of geopolymer. In this case, the compressive strength enhancement was due to the increase in the Al³⁺ leached from BA in the presence of ${\text{SO}}_4^{2-}$ and the formation of additional calcium silicate hydrate.     

Zone analysis: a visualization framework for classification problems

There have been large attempts to adopt the bias-variance framework from the regression problems to the classification problems. However, recently, it has been shown that only non-straightforward extensions exist for classification problems. In this paper, we present an alternative visualization framework for classification problems called zone analysis. Our zone analysis framework partly extends the bias-variance idea; instead of decomposing an error into two parts, i.e. the biased and unbiased components, our framework decomposes the error into K components. While bias-variance information is still contained in our framework, our framework provides interesting observations which are not obviously seen in the previous bias-variance framework, e.g. a prejudice behavior of the bagging algorithm to various unbiased instances. Our framework is suitable for visualizing an effect of context changes on learning performance. The type of context changes which we primarily investigate in the paper is “a change from a base learner to an ensemble learner such as bagging, adaboost, arc-x4 and multi-boosting”.     

Evolutionary strategies for hyperparameters of support vector machines based on multi-scale radial basis function kernels

Kernel functions are used in support vector machines (SVM) to compute inner product in a higher dimensional feature space. SVM classification performance depends on the chosen kernel. The radial basis function (RBF) kernel is a distance-based kernel that has been successfully applied in many tasks. This paper focuses on improving the accuracy of SVM by proposing a non-linear combination of multiple RBF kernels to obtain more flexible kernel functions. Multi-scale RBF kernels are weighted and combined. The proposed kernel allows better discrimination in the feature space. This new kernel is proved to be a Mercer’s kernel. Furthermore, evolutionary strategies (ESs) are used for adjusting the hyperparameters of SVM. Training accuracy, the bound of generalization error, and subset cross-validation on training accuracy are considered to be objective functions in the evolutionary process. The experimental results show that the accuracy of multi-scale RBF kernels is better than that of a single RBF kernel. Moreover, the subset cross-validation on training accuracy is more suitable and it yields the good results on benchmark datasets.     

A Method for Isolated Thai Tone Recognition Using a Combination of Neural Networks

Tone information is very important to speech recognition in a tonal language such as Thai. In this article, we present a method for isolated Thai tone recognition. First, we define three sets of tone features to capture the characteristics of Thai tones and employ a feedforward neural network to classify tones based on these features. Next, we describe several experiments using the proposed features. The experiments are designed to study the effect of initial consonants, vowels, and final consonants on tone recognition. We find that there are some correlations between tones and other phonemes, and the recognition performances are satisfying. A human perception test is then conducted to judge the recognition rate. The recognition rate of a human is much lower than that of a machine. Finally, we explore various combination schemes to enhance the recognition rate. Further improvements are found in most experiments.     

Approximate Match of Rules Using Backpropagation Neural Networks

This paper presents a method for approximate match of first-order rules with unseen data. The method is useful especially in case of a multi-class problem or a noisy domain where unseen data are often not covered by the rules. Our method employs the Backpropagation Neural Network for the approximation. To build the network, we propose a technique for generating features from the rules to be used as inputs to the network. Our method has been evaluated on four domains of first-order learning problems. The experimental results show improvements of our method over the use of the original rules. We also applied our method to approximate match of propositional rules converted from an unpruned decision tree. In this case, our method can be thought of as soft-pruning of the decision tree. The results on multi-class learning domains in the UCI repository of machine learning databases show that our method performs better than standard C4.5's pruned and unpruned trees.