14245135
Automatic Correlation and Calibration of Noisy Sensor Readings Using Elite Genetic Algorithms
This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray scale images corrupted with noise. Both tabu search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results.
31224210
The strong semantics for logic programs
Recently, the well-founded semantics of a logic programP has been strengthened to the well-founded semantics-by-case (WFC) and this in turn has been strengthened to the extended well-founded semantics (WFE). Both WFC(P) and WFE(P) have thelogical consequence property, namely, if an atomAj is true in the theory Th(P), thenAj is true in the semantics as well. However, neither WFC nor WFE has the GCWA property, i.e., if an atomAj is false in all minimal models ofP,Aj may not be false in WFC(P) (resp. WFE(P)). We extend the ideas in WFC and WFE to define a strong well-founded semantics WFS which has the GCWA property. The strong semantics WFS(P) is defined by combining GCWA with the notion ofderived rules. Here we use a new Type-III derived rules in addition to those used in WFC and WFE. The relationship between WFS and WFC is also clarified.
30548729
A Class of Stratified Programs in Autoepistemic Logic of Knowledge and Belief
Przymusinski proposed [11] AELB, the autoepistemic logic of knowledge and belief. The AELB logic is a very powerful and expressive formal system which isomorphically contains various major non-monotonic knowledge representation formalisms and major logic program semantics. Thus AELB can be used as a unifying framework for non-monotonic knowledge representation formalisms, which allows us to compare and better understand different formalizations of common-sense knowledge.
9144862
Rules and fuzzy rules in text: concept, extraction and usage
Several concepts and techniques have been imported from other disciplines such as Machine Learning and Artificial Intelligence to the field of textual data. In this paper, we focus on the concept of rule and the management of uncertainty in text applications. The different structures considered for the construction of the rules, the extraction of the knowledge base and the applications and usage of these rules are detailed. We include a review of the most relevant works of the different types of rules based on their representation and their application to most of the common tasks of Information Retrieval such as categorization, indexing and classification.
27866706
A New Method of Circumscribing Beliefs: The Propositional Case
We propose here a new notion of a minimal model for forming circumscription in belief-logic by incorporating the notion of minimal models in the prepositional logic. This results in a new circumscription operation for belief-formulas than the one considered in [4-6]. An important feature of the new circumscription operation is its close relationship with the circumscription of prepositional formulas. For instance, we have CIRC[Bp] = B(CIRC[p]), where p is a prepositional formula. We give several examples to show that the new notion of circumscription fits quite well with intuition.
39684197
A sound and complete proof theory for the generalized logic of only knowing
We present a sound and complete proof theory for the generalized logic of only knowing (GOL). The GOL logic is a modal logic proposed recently (Chen 1997) as a unified logical framework for non-monotonic reasoning, which extends the logic of only knowing (OL) of Levesque (1990), and covers the important notion of epistemic specification (Gelfond 1991, Gelfond and Przymusinska 1993) which is very useful for knowledge representation. The OL logic has been shown to have close connections with Lifschitz's minimal belief and negation as failure (Lifschitz 1994) and various other major non-monotonic formalisms. The GOL logic is more general and powerful than OL. The completeness result for the GOL proof theory establishes the theoretical foundation for answering queries in epistemic theories. This work is restricted to the propositional case only.
1250825
Enhancing Training Set for Face Detection
We present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. The motivation is from support vector machines (SVM) that, for classification problems, examples lying close to class boundary usually have more influence and thus are more informative than those far from the boundary. We utilize a nonlinear technique - reduced set (RS) method and a new image distance metric to generate new examples, and then add them to the original collected database to enhance it. Extensive experiments show that the proposed approach has an encouraging performance
14208339
Using Nondeterminism to Design Efficient Deterministic Algorithms
Abstract
In this paper we illustrate how nondeterminism can be used
conveniently and effectively in designing efficient deterministic
algorithms. In particular, our method gives a parameterized
algorithm of running time O((5.7 k)k n) for the 3-D matching
problem, which significantly improves the previous algorithm by
Downey et al. The algorithm can be generalized to
yield an improved algorithm for the r-D matching problem for any
positive integer r. The method can also be employed in designing
deterministic algorithms for other optimization problems as well.


2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
33412733
Learning a Hidden Graph Using O(log n) Queries Per Edge
We consider the problem of learning a general graph using edge-detecting queries. In this model, the learner may query whether a set of vertices induces an edge of the hidden graph. This model has been studied for particular classes of graphs by Kucherov and Grebinski [1] and Alon et al.[2], motivated by problems arising in genome sequencing. We give an adaptive deterministic algorithm that learns a general graph with n vertices and m edges using O(m log n) queries, which is tight up to a constant factor for classes of non-dense graphs. Allowing randomness, we give a 5-round Las Vegas algorithm using \(O(m {\rm log}n+\sqrt{m}{\rm log}^{2}n)\) queries in expectation. We give a lower bound of Ω((2m/r) r/2) for learning the class of non-uniform hypergraphs of dimension r with m edges. For the class of r-uniform hypergraphs with bounded degree d, where d≤ n 1/( r− − 1)/(2r 1 + 2/( r− − 1)), we give a non-adaptive Monte Carlo algorithm using O(dnlog n) queries, which succeeds with probability at least 1–n − − c, where c is any constant.
7794101
Effective Density Queries for Moving Objects in Road Networks
Recent research has focused on density queries for moving objects in highly dynamic scenarios. An area is dense if the number of moving objects it contains is above some threshold. Monitoring dense areas has applications in traffic control systems, bandwidth management, collision probability evaluation, etc. All existing methods, however, assume the objects moving in the Euclidean space. In this paper, we study the density queries in road networks, where density computation is determined by the length of the road segment and the number of objects on it. We define an effective road-network density query guaranteeing to obtain useful answers. We then propose the cluster-based algorithm for the efficient computation of density queries for objects moving in road networks. Extensive experimental results show that our methods achieve high efficiency and accuracy for finding the dense areas in road networks.