The model of abstract interpretation of programs developed by Cousot and Cousot [2nd ISOP, 1976], Cousot and Cousot [POPL 1977] and Cousot [PhD thesis 1978] is applied to the static determination of linear equality or inequality invariant relations among numerical variables of programs.

This paper is a survey of approaches and algorithms used for the automatic discovery of patterns in biosequences. Patterns with the expressive power in the class of regular languages are considered, and a classification of pattern languages in this class is developed, covering those patterns which

is represented by a joint probability density function on the shape of the constellation and the appearance of the parts. Our method automatically identifies distinctive features in the training set. The set of model parameters is then learned using expectation maximization (see the companion paper [11

We deal with the issue of automatic discovery of similar words (synonyms and near-synonyms) from different kind of sources: from a large corpora of documents, from the Web, and from monolingual dictionaries. We present in detail three algorithms that extract similar words from a large corpus

malicious activity. It is important to find not only maliciouslyacting benign processes, but also the actual parasitic malware that may have infected those processes. In this paper, we present techniques for automatic discovery of unknown parasitic malware present on an infected system. We design

the adposition typologies for less-studied languages by manual analysis of large text corpora can be quite expensive, yet automatic discovery of the same has received very little attention till date. This research presents a simple unsupervised technique to automatically predict the adpo-sition typology for a

We present a technique for automating the discovery of loop invariants based upon the analysis of failed proof attempts. Previously we have shown how failure analysis may be used productively in the search for inductive proofs. This work had direct application to the verification of functional

Introduction Most methods for representing protein motifs emphasize the amino acids conserved during evolution. These conserved residues are often of critical importance in the structure or function of the protein. Dictionaries of such conserved motifs have been compiled and are extremely valuable in discovering structural and functional attributes of novel protein sequences (Bairoch, 1991). Many protein motifs are not conserved sufficiently to be represented as a consensus sequence (Dodd and Egan, 1987). When motifs are highly variable in sequence, one usually employs probabilistic methods such as weight matrices or profiles (Staden, 1984; Gribskov, McLachlan and Eisenberg, 1987). Weight matrices give the likelihood of finding each amino acid at each position in the motif based on a large set of examples. The likelihood of finding each amino acid is calculated relative to the overall frequency of finding that residue in the proteins or in the database being examined. Like th

Language Computer Corporation An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part–whole relations in text. First

Information-flow analysis is a powerful technique for reasoning about the sensitive information exposed by a program during its execution. We present the first automatic method for information-flow analysis that discovers what information is leaked and computes its comprehensive quantitative