Chandrabose Aravindan

On the correctness of unfold/fold transformation of normal and extended logic programs

We show that the framework for unfold/fold transformation of logic programs, first proposed by Tamaki and Sato and later extended by various researchers, preserves various nonmonotonic semantics of normal logic programs, especially preferred extension, partial stable models, regular model, and stable theory semantics. The primary aim of this research is to adopt a uniform approach for every semantics of normal programs, and that is elegantly achieved through the notion of semantic kernel. Later, we show that this framework can also be applied to extended logic programs, preserving the answer set semantics.

Root Mean Square filter for noisy images based on hyper graph model

In this paper, we propose a noise removal algorithm for digital images. This algorithm is based on hypergraph model of image, which enables us to distinguish noisy pixels in the image from the noise-free ones. Hence, our algorithm obviates the need for denoising all the pixels, thereby preserving as much image details as possible. The identified noisy pixels are denoised through Root Mean Square (RMS) approximation. The performance of our algorithm, based on peak-signal-to-noise-ratio (PSNR) and mean-absolute-error (MAE), was studied on various benchmark images, and found to be superior to that of other traditional filters and other hypergraph based denoising algorithms.

Partial deduction of logic programs wrt well-founded semantics

In this paper, we extend the partial deduction framework of Lloyd and Shepherdson, so that unfolding of non-ground negative literals and loop checks can be carried out during partial deduction. We show that the unified framework is sound and complete wrt well-founded model semantics, when certain conditions are satisfied.

Theorem Proving Techniques for View Deletion in Databases

In this paper, we show how techniques from first-order theorem proving can be used for efficient deductive database updates. The key idea is to transform the given database, together with the update request, into a (disjunctive) logic program and to apply the hyper-tableau calculus (Baumgartner et al. 1996) to solve the original update problem. The resulting algorithm has the following properties: it works goal-directed (i.e. the search is driven by the update request), it is rational in the sense that it satisfies certain rationality postulates stemming from philosophical works on belief dynamics, and, unlike comparable approaches, it is of polynomial space complexity. To obtain soundness and completeness results, the hyper-tableau calculus is slightly modified for minimal model reasoning. Besides a direct proof we give an alternate proof which gives insights into the relation to previous approaches. As a by-product we thereby derive a soundness and completeness result of hyper-tableaux for computing minimal abductive explanations.

Naive Bayes Approach for Website Classification

World Wide Web has become the largest repository of information because of its connectivity and scalability. With the increase in number of web users and the websites, the need for website classification gains attraction. The website classification based on URLs alone plays an important role, since the contents of web pages need not be fetched for classification. In this paper, a soft computing approach is proposed for classification of websites based on features extracted from URLs alone. The Open Directory Project dataset was considered and the proposed system classified the websites into various categories using Naive Bayes approach. The performance of the system was evaluated and Precision, Recall and F-measure values of 0.7, 0.88 and 0.76 were achieved by this approach.

An Abductive Framework for Negation in Disjunctive Logic Programming

In this paper, we study an abductive framework for disjunctive logic programming that provides a new way to understand negation in disjunctive logic programming. We show that the defined framework captures the existing minimal model semantics based on (Extended) Generalised Closed World Assumption ((E)GGWA), This relationship between abduction and minimal model reasoning provides a methodology to develop algorithms for minimal model reasoning. To demonstrate this, we show how a theorem prover, based on restart model elimination calculus, can be modified for abductive reasoning and thus for minimal model reasoning.

Belief Dynamics, Abduction, and Database

In this paper, we introduce a new concept of generalized partial meet contraction for contracting a sentence from a belief base. We show that a special case of belief dynamics, referred to as knowledge base dynamics, where certain part of the belief base is declared to be immutable, has interesting connections with abduction, thus enabling us to use abductive procedures to realize contractions. Finally, an important application of knowledge base dynamics in providing an axiomatic characterization for deleting view atoms from databases is discussed in detail.

Dislop: Towards a Disjunctive Logic Programming System

This paper gives a brief high-level description of the implementation of a disjunctive logic programming system referred to as DisLoP. This system is a result of research activities of the Disjunctive Logic Programming-project (funded by Deutsche Forschungs-Gemeinschaft), undertaken by the University of Koblenz since July 1995.

Improving emotion recognition from speech using sensor fusion techniques

In this paper, we propose a two level hierarchical ensemble of classifiers for improved recognition of emotion from speech. At the first level, Mel Frequency Cepstral Coefficients (MFCC) of input speech are classified independently by suitably trained Support Vector Machine (SVM) and Gaussian Mixer Model (GMM) classifiers. From these first level classifiers, posterior probabilities of GMM and discriminate function values of SVM are extracted and given as input to second level SVM classifier, which classifies emotion based on these values. Extensive experiments were carried out using the Berlin database Emo-DB for seven emotions (anger, fear, bore, happy, neutral, disgust and sad). While the SVM and GMM classifiers produced only 67% and 66% accuracy respectively, 75% accuracy was achieved with our fusion approach.

Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

Evolutionary multi-objective optimization algorithms are generally employed to generate Pareto optimal solutions by exploring the search space. To enhance the performance, exploration by global search can be complemented with exploitation by combining it with local search. In this paper, we address the issues in integrating local search with global search such as: how to select individuals for local search; how deep the local search is performed; how to combine multiple objectives into single objective for local search. We introduce a Preferential Local Search mechanism to fine tune the global optimal solutions further and an adaptive weight mechanism for combining multi-objectives together. These ideas have been integrated into NSGA-II to arrive at a new memetic algorithm for solving multi-objective optimization problems. The proposed algorithm has been applied on a set of constrained and unconstrained multi-objective benchmark test suite. The performance was analyzed by computing different metrics such as Generational distance, Spread, Max spread, and HyperVolume Ratio for the test suite functions. Statistical test applied on the results obtained suggests that the proposed algorithm outperforms the state-of-art multi-objective algorithms like NSGA-II and SPEA2. To study the performance of our algorithm on a real-world application, Economic Emission Load Dispatch was also taken up for validation. The performance was studied with the help of measures such as Hypervolume and Set Coverage Metrics. Experimental results substantiate that our algorithm has the capability to solve real-world problems like Economic Emission Load Dispatch and is able to produce better solutions, when compared with NSGA-II, SPEA2, and traditional memetic algorithms with fixed local search steps.