Edit Costs Research Articles

Rewarding the upland poor for saving the commons? Evidence from Southeast Asia

Acknowledgement: We are indebted to eleven reviewers whose critical and constructive comments helped to improve earlier versions of the papers in this special issue. Previous versions of the papers were presented at the International Symposium on “Interdependencies between upland and lowland agriculture and resource management” held at the University of Hohenheim, Stuttgart, Germany, from 1–4 April 2008. The financial support of the German Research Foundation (Deutsche Forschungsgemeinschaft – DFG) towards the editing costs of this special issue is gratefully acknowledged.

An Edit-Distance Model for the Approximate Matching of Timed Strings

An edit-distance model that can be used for the approximate matching of contiguous and non-contiguous timed strings is presented. The model extends the concept of the weighted string-edit distance by introducing timed edit operations and by making the edit costs time dependent. Special attention is paid to the timed null symbols that are associated with the timed insertions and deletions. The usefulness of the presented model is demonstrated on the classification of phone-recognition errors using the TIMIT speech database.

Approximate graph edit distance computation by means of bipartite graph matching

In recent years, the use of graph based object representation has gained popularity. Simultaneously, graph edit distance emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning, and data mining. The key advantages of graph edit distance are its high degree of flexibility, which makes it applicable to any type of graph, and the fact that one can integrate domain specific knowledge about object similarity by means of specific edit cost functions. Its computational complexity, however, is exponential in the number of nodes of the involved graphs. Consequently, exact graph edit distance is feasible for graphs of rather small size only. In the present paper we introduce a novel algorithm which allows us to approximately, or suboptimally, compute edit distance in a substantially faster way. The proposed algorithm considers only local, rather than global, edge structure during the optimization process. In experiments on different datasets we demonstrate a substantial speed-up of our proposed method over two reference systems. Moreover, it is emprically verified that the accuracy of the suboptimal distance remains sufficiently accurate for various pattern recognition applications.

A study of graph spectra for comparing graphs and trees

The spectrum of a graph has been widely used in graph theory to characterise the properties of a graph and extract information from its structure. It has also been employed as a graph representation for pattern matching since it is invariant to the labelling of the graph. There are, however, a number of potential drawbacks in using the spectrum as a representation of a graph. Firstly, more than one graph may share the same spectrum. It is well known, for example, that very few trees can be uniquely specified by their spectrum. Secondly, the spectrum may change dramatically with a small change structure. There are a wide variety of graph matrix representations from which the spectrum can be extracted. Among these are the adjacency matrix, combinatorial Laplacian, normalised Laplacian and unsigned Laplacian. Spectra can also be derived from the heat kernel matrix and path length distribution matrix. The choice of matrix representation clearly has a large effect on the suitability of spectrum in a number of pattern recognition tasks. In this paper we investigate the performance of the spectra as a graph representation in a variety of situations. Firstly, we investigate the cospectrality of the various matrix representations over large graph and tree sets, extending the work of previous authors. We then show that the Euclidean distance between spectra tracks the edit distance between graphs over a wide range of edit costs, and we analyse the accuracy of this relationship. We then use the spectra to both cluster and classify the graphs and demonstrate the effect of the graph matrix formulation on error rates. These results are produced using both synthetic graphs and trees and graphs derived from shape and image data.

Learning probabilistic models of tree edit distance

Nowadays, there is a growing interest in machine learning and pattern recognition for tree-structured data. Trees actually provide a suitable structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, computer music, or conversion of semi-structured data (e.g. XML documents). Many applications in these domains require the calculation of similarities over pairs of trees. In this context, the tree edit distance (ED) has been subject of investigations for many years in order to improve its computational efficiency. However, used in its classical form, the tree ED needs a priori fixed edit costs which are often difficult to tune, that leaves little room for tackling complex problems. In this paper, to overcome this drawback, we focus on the automatic learning of a non-parametric stochastic tree ED. More precisely, we are interested in two kinds of probabilistic approaches. The first one builds a generative model of the tree ED from a joint distribution over the edit operations, while the second works from a conditional distribution providing then a discriminative model. To tackle these tasks, we present an adaptation of the expectation–maximization algorithm for learning these distributions over the primitive edit costs. Two experiments are conducted. The first is achieved on artificial data and confirms the interest to learn a tree ED rather than a priori imposing edit costs; The second is applied to a pattern recognition task aiming to classify handwritten digits.

Shape recognition using fuzzy string-matching technique

Object recognition is a very important task in industrial applications. Attributed string matching is a well-known technique for pattern matching. The present paper proposes a fuzzy string-matching approach for two-dimensional object recognition. The fuzzy numbers are used to represent the edit costs. Therefore, the edit distances are also presented as fuzzy numbers. The attributed string-matching problem is then equivalent to a fuzzy shortest path problem. The edit distance between two shapes is presented as a fuzzy number. By ranking the fuzzy edit distances, the input shape is classified as the reference shape that has the minimum fuzzy edit distance. The experimental results show that the proposed method can effectively recognize two-dimensional objects.

Learning stochastic edit distance: Application in handwritten character recognition

Many pattern recognition algorithms are based on the nearest-neighbour search and use the well-known edit distance, for which the primitive edit costs are usually fixed in advance. In this article, we aim at learning an unbiased stochastic edit distance in the form of a finite-state transducer from a corpus of ( input, output) pairs of strings. Contrary to the other standard methods, which generally use the Expectation Maximisation algorithm, our algorithm learns a transducer independently on the marginal probability distribution of the input strings. Such an unbiased way to proceed requires to optimise the parameters of a conditional transducer instead of a joint one. We apply our new model in the context of handwritten digit recognition. We show, carrying out a large series of experiments, that it always outperforms the standard edit distance.

Automatic learning of cost functions for graph edit distance

Graph matching and graph edit distance have become important tools in structural pattern recognition. The graph edit distance concept allows us to measure the structural similarity of attributed graphs in an error-tolerant way. The key idea is to model graph variations by structural distortion operations. As one of its main constraints, however, the edit distance requires the adequate definition of edit cost functions, which eventually determine which graphs are considered similar. In the past, these cost functions were usually defined in a manual fashion, which is highly prone to errors. The present paper proposes a method to automatically learn cost functions from a labeled sample set of graphs. To this end, we formulate the graph edit process in a stochastic context and perform a maximum likelihood parameter estimation of the distribution of edit operations. The underlying distortion model is learned using an Expectation Maximization algorithm. From this model we finally derive the desired cost functions. In a series of experiments we demonstrate the learning effect of the proposed method and provide a performance comparison to other models.

Self-Organizing Maps for Learning the Edit Costs in Graph Matching

Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively.

Graph edit distance from spectral seriation

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that they lack some of the formality and rigor of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that string matching techniques can be used. To do this, we use a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We show how the serial ordering can be established using the leading eigenvector of the graph adjacency matrix. We pose the problem of graph-matching as a maximum a posteriori probability (MAP) alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression in which the edit cost is the negative logarithm of the a posteriori sequence alignment probability. We compute the edit distance by finding the sequence of string edit operations which minimizes the cost of the path traversing the edit lattice. The edit costs are determined by the components of the leading eigenvectors of the adjacency matrix and by the edge densities of the graphs being matched. We demonstrate the utility of the edit distance on a number of graph clustering problems.

Name that tune: A pilot study in finding a melody from a sung query

We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. The sung theme (query) is converted into a sequence of pitch-intervals and rhythms. This sequence is compared to musical themes (targets) stored in a database. The top pieces are returned to the user in order of similarity to the sung theme. We describe, in detail, two different approaches to measuring similarity between database themes and the sung query. In the first, queries are compared to database themes using standard string-alignment algorithms. Here, similarity between target and query is determined by edit cost. In the second approach, pieces in the database are represented as hidden Markov models (HMMs). In this approach, the query is treated as an observation sequence and a target is judged similar to the query if its HMM has a high likelihood of generating the query. In this article we report our approach to the construction of a target database of themes, encoding, and transcription of user queries, and the results of preliminary experimentation with a set of sung queries. Our experiments show that while no approach is clearly superior to the other system, string matching has a slight advantage. Moreover, neither approach surpasses human performance.

Technological Advances that Enhance Teaching Using Animals, and the Application of the Three Rs

The inventions that have progressively contributed to education have never offered opportunities as vast as in the digital era, even though this is still scarcely 25 years old. In this time, digital handling of data led first to text processing, then to bitmap and vector graphics, and now, to digital sound and movies. As these advanced, the storage methods became larger, faster, easier and cheaper. The advancement of technology has been so rapid that the standard of most teaching aids now available is well below what can currently be achieved. We are confronted with an unprecedented opportunity for applying the principles of reduction, refinement and replacement of animals in education. Not only are the visual teaching aids improved by digitising, but all aspects of their development, including the ease, higher speed, and low cost of creation, editing, copying, distribution and access, are improved, as well. Several examples will be given, including access to interactive panoramic movies, animated sequences to explain difficult concepts, on-line tutorials and image databases using digital photography, radiography and other diagnostic methods, as well as the production of desktop movies. The speed of technical advance brings its own problems, but the challenges and possibilities for developing viable alternatives to the use of animals in teaching are vast.

Computing approximate tree edit distance using relaxation labeling

This paper presents a new method for computing the tree edit distance problem with uniform edit cost. We commence by showing that any tree obtained with a sequence of cut operations is a subtree of the transitive closure of the original tree, we show that the necessary condition for any subtree to be a solution can be reduced to a clique problem in a derived structure. Using this idea we transform the problem of computing tree edit distance into a series of maximum weight clique problems. We, then use relaxation labeling to find an approximation to the tree edit distance.

New Algorithm for Ordered Tree-to-Tree Correction Problem

The ordered tree-to-tree correction problem is to compute the minimum edit cost of transforming one ordered tree to another one. This paper presents a new algorithm for this problem. Given two ordered trees S and T, our algorithm runs in O(|S||T|+min{L2S|T|+L2.5SLT,L2T|S|+L2.5TLS) time, where LS denotes the number of leaves of S and DS denotes the depth of S. The previous best algorithms for this problem run in O(|S||T|min{LS,DS}min{LT,DT}) time (K. Zhang and D. Shasha, SIAM J. Comput.18, No. 6 (1989), 1245–1262) and in O(min{|S|2|T|log2|T|,|T|2|S|log2|S|}) time (P. N. Klein, in “Algorithms—ESA'98, 6th Annual European Symposium” (G. Bilardi, G. F. Italiano, A. Pietracaprina, and G. Pucci, Eds.), Lecture Notes in Computer Science, Vol. 1461, pp. 91–102, Springer-Verlag, Berlin/New York, 1998). As a comparison, our algorithm is asymptotically faster for certain kind of trees.

The New View on Editing

SummaryNumerous evaluation and other studies show that the heavy cost of editing cannot be justified by quality improvement. It is necessary to replace the old paradigm‐the more and tighter the checks and recontacts, the better the quality‐by a new‐focus the editing on identifying and collecting data on errors, problem areas, and error causes to provide a basis for a continuous improvement of the whole survey vehicle.The corner stone of the new view on editing is that the entire set of the query edits should be designed meticulously, be focused on errors influencing the estimates, and be targeted on existing error types which can be identified by edits, and finally that the effects of the edits should be continuously evaluated by analysis of performance measures and other diagnostics, which the process should be designed to produce.The paper is a short introduction to modern editing under the new view. It presents, also, some facts to the low efficiency of traditional editing, and explains it by giving a historical review of editing.

Parametric string edit distance and its application to pattern recognition

A generalized version of the string matching algorithm by Wagner and Fischer (1974) is proposed. It is based on a parametrization of the edit cost. We assume constant cost for any delete and insert operation, but the cost for replacing a symbol is given as a parameter /spl tau/. For any two strings A and B, our algorithm computes their edit distance in terms of the parameter /spl tau/. We give the new algorithm, study some of its properties, and discuss potential applications to pattern recognition. >

CASM: a VLSI chip for approximate string matching

The edit distance between two strings a1, ..., a/sub m/ and b/sub 1/, ..., b/sub n/ is the minimum cost s of a sequence of editing operations (insertions, deletions and substitutions) that convert one string into the other. This paper describes the design and implementation of a linear systolic array chip for computing the edit distance between two strings over a given alphabet. An encoding scheme is proposed which reduces the number of bits required to represent a state in the computation. The architecture is a parallel realization of the standard dynamic programming algorithm proposed by Wagner and Fischer (1974), and can perform approximate string matching for variable edit costs. More importantly, the architecture does not place any constraint on the lengths of the strings that can be compared. It makes use of simple basic cells and requires regular nearest neighbor communication, which makes it suitable for VLSI implementation. A prototype of this array has been built at the University of South Florida. >

Visual perception and sequences of eye movement fixations: a stochastic modeling approach

Sequences of visual fixations, while looking at an object, are modeled as Markov processes, and statistical properties of such processes are derived by means of simulations. The sequences are also abstracted as character strings, and a quantitative method of measuring their similarity, based on minimum string editing cost (actually dissimilarity distance), is introduced. Interrelationships between the structure and size of the generating Markov matrices and the string editing distance shed light on the relative roles of deterministic and probabilistic processes in producing human visual scanpaths.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Proofreading in vivo: editing of homocysteine by methionyl-tRNA synthetase in the yeast Saccharomyces cerevisiae.

Homocysteine thiolactone is a product of an error-editing reaction, catalyzed by Escherichia coli methionyl-tRNA synthetase, which prevents incorporation of homocysteine into tRNA and protein, both in vitro and in vivo. Here, the thiolactone is also shown to occur in cultures of the yeast Saccharomyces cerevisiae. In yeast, the thiolactone is made from homocysteine in a reaction catalyzed by methionyl-tRNA synthetase. One molecule of homocysteine is edited as thiolactone per 500 molecules of methionine incorporated into protein. Homocysteine, added exogenously to the medium or overproduced by some yeast mutants, is detrimental to cell growth. The cost of homocysteine editing in yeast is minimized by the presence of a pathway leading from homocysteine to cysteine, which keeps intracellular homocysteine at low levels. These results not only directly demonstrate that editing of errors in amino acid selection by methionyl-tRNA synthetase operates in vivo in yeast but also establish the importance of proofreading mechanisms in a eukaryotic organism.

SERIALS FUNDS: A SURPLUS IS PREDICTED

Managing the serials budget presents an ongoing challenge to librarians. Today, no other budget item is scrutinized as carefully as serials expenditures. Information carries an ever‐escalating price tag; the process of turning information into a published format is an expensive one. There are people costs, editing costs, printing costs, paper costs, binding costs, packaging costs and fulfillment costs. While our society recognizes access to information as a fundamental right of each individual, librarians today find themselves faced with the loaded problem of deciding what serial tides to cancel or what important new ones to simply not purchase.