‘<Emphasis Type="Italic">if p then q</Emphasis>’ . . . and all that: Logical Elements in Reasoning and Discourse

In this paper we explore differences in use of the so-called ‘logical’ elements of language such as quantifiers and conditionals, and use this to explain differences in performance in reasoning tasks across subject groups with different educational backgrounds. It is argued that quantified sentences are difficult natural bases for reasoning, and hence more prone to elicit variation in reasoning behaviour, because they are chiefly used with a pre-determined domain in everyday speech. By contrast, it is argued that conditional sentences form natural premises because of the function they serve in everyday speech. Implications of this for the role of logic in modelling human reasoning behaviour are briefly considered.     

A survey on the history of transaction management: from flat to grid transactions

Transactions have been around since the Seventies to provide reliable information processing in automated information systems. Originally developed for simple ‘debit-credit’ style database operations in centralized systems, they have moved into much more complex application domains including aspects like distribution, process-orientation and loose coupling. The amount of published research work on transactions is huge and a number of overview papers and books already exist. A concise historic analysis providing an overview of the various phases of development of transaction models and mechanisms in the context of growing complexity of application domains is still missing, however. To fill this gap, this paper presents a historic overview of transaction models organized in several ‘transaction management eras’, thereby investigating numerous transaction models ranging from the classical flat transactions, via advanced and workflow transactions to the Web Services and Grid transaction models. The key concepts and techniques with respect to transaction management are investigated. Placing well-known research efforts in historical perspective reveals specific trends and developments in the area of transaction management. As such, this paper provides a comprehensive, structured overview of developments in the area.     

Continuous State Dynamic Programming via Nonexpansive Approximation

This paper studies fitted value iteration for continuous state numerical dynamic programming using nonexpansive function approximators. A number of approximation schemes are discussed. The main contribution is to provide error bounds for approximate optimal policies generated by the value iteration algorithm.      

Multi-core CPUs,Clusters, and Grid Computing: A Tutorial

The nature of computing is changing and it poses both challenges and opportunities for economists. Instead of increasing clock speed, future microprocessors will have “multi-cores” with separate execution units. “Threads” or other multi-processing techniques that are rarely used today are required to take full advantage of them. Beyond one machine, it has become easy to harness multiple computers to work in clusters. Besides dedicated clusters, they can be made up of unused lab computers or even your colleagues’ machines. Finally, grids of computers spanning the Internet are now becoming a reality.     

Differencing and merging of architectural views

Differencing and merging architectural views is an important activity in software engineering. However, existing approaches are still based on restrictive assumptions, such as requiring view elements to have unique identifiers or exactly matching types, which is often not the case in many application domains. We propose an approach based on structural information. We generalize a published polynomial-time tree-to-tree correction algorithm that detects inserts, renames and deletes, into a novel algorithm that additionally detects restricted moves. Our algorithm also supports forcing and preventing matches between view elements. We incorporate the algorithm into tools to compare and merge Component-and-Connector (C&C) architectural views. We provide an empirical evaluation of the algorithm. We illustrate the tools using extended examples, and use them to detect and reconcile interesting differences between real architectural views. This article is an expanded version of the following paper: Abi-Antoun, M., Aldrich, J., Nahas, N., Schmerl, B., and Garlan, D: 2006, ‘Differencing and Merging of Architectural Views’. In: Proceedings of the 21st IEEE International Conference on Automated Software Engineering, pp. 47–58.     

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.      

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.     

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem.