Abstract: A method and system for generating a bilingual dictionary that maps words of the source language to words of a target language is provided. A Cross-Lingual Information Retrieval (“CLIR”) system accesses a parallel collection that is comprised of a parallel source collection and a parallel target collection, and generates a similarity score for sentences of the parallel target collection indicating the similarity of those sentences to sentences of the target collection. When the CLIR system generates a bilingual dictionary from the sentences of the parallel collection, it factors in the similarities of the sentences of the parallel target collection to sentences in the target collection. By factoring these similarities, the CLIR system allows sentences with a high similarity to have a greater influence on the mapping of words of the source language to the words of the target language than sentences with a low similarity.

Abstract: Size of a software application testing project is determined, and person/hours required for the testing project is estimated. The software application is sized by counting the number of different parameter types that occur within testing activities associated with the application. The parameter type numbers are then divided by a scaling weight to arrive at a Testing Unit number, which is then divided by a Testing Unit rate, e.g., person hours associated with each testing unit, to arrive at an estimated testing project effort. Some embodiments include an uncertainty calculation that potentially increases testing time based on clarity of the project requirements, the tester familiarity with the application area and the tester familiarity with the domain. Some embodiments calculate separate testing project times for different phases of the testing project.

Abstract: A source sentence is decoded in an iterative manner. At each step a set of partially constructed target sentences are collated, each of which has a score or an associated probability, computed from a language model score and a translation model score. At each iteration, a family of exponentially many alignments is constructed and the optimal translation for this family is found out. To construct the alignment family, a set of transformation operators is employed. The described decoding algorithm is based on the Alternating Optimization framework and employs dynamic programming. Pruning and caching techniques may be used to speed up the decoding.

Abstract: Methods, apparatuses and computer program products for decoding source text in a first language to target text in a second language are disclosed. The source text is decoded into an intermediate text portion based on a fixed alignment between words in the source text and words in the intermediate text portion and an alignment between words in the source text and words in the intermediate text portion is determined. The steps of decoding the source text and determining an alignment are alternately repeated while a decoding improvement in the intermediate text portion can be obtained. Finally, the intermediate text portion is output as the target text. The step of alternately repeating the source text decoding and alignment determination steps may be repeated for each of a plurality of lengths of the intermediate text portion.

Abstract: A method of matching a query fingerprint to a plurality of file fingerprints. The method comprises the steps of determining a plurality of partial features of each of the file fingerprints. For each partial feature, derive a list of all file fingerprints which have said partial feature as one of their partial features. Determine a plurality of query partial features of the query fingerprint, and derive a ranked list of the file fingerprints based on identifying the individual query partial features in the partial features of the respective file fingerprints. Then perform one-to-one matching of the query fingerprint with selected ones of the ranked list of the file fingerprints.

Abstract: Fingerprint verification matching a claimant fingerprint with one of a number of recorded enrollee fingerprints uses various adaptations of model alignment techniques involving affine transformation to provide an algorithm providing relatively fast and accurate verification suitable for realtime applications. These techniques involve sampling minutiae into subsets based on geometrical proximity to a particular index minutiae for each subset, eliminating minutiae which are near the fingerprint boundary, ordering the minutiae of each subset in a predetermined manner, classifying each subset in bins which discriminate on the properties of the subsets, checking transformations proposed as matches between fingerprints for consistency with each other, and checking the topological as well as the geometrical correspondence of potentially matching fingerprints. In a preferred form, a score indicative of the correlation between fingerprints is used to ultimately decide whether the fingerprints match.