Abstract: Methods and apparatus to identify a type of acoustic event Mel Frequency Cepstral Coefficients (MFCCs). In one embodiment, received sound is processed using the MFCCs to locate a peak for comparison with a stored event to determine if the peak corresponds to the acoustic event.

Abstract: Methods and apparatus to identify a type of acoustic event Mel Frequency Cepstral Coefficients (MFCCs). In one embodiment, received sound is processed using the MFCCs to locate a peak for comparison with a stored event to determine if the peak corresponds to the acoustic event.

Abstract: In accordance with particular embodiments, a method includes receiving data from disparate data sources comprising data associated with different people. At least one of the data sources comprises data associated with at least one common person. The method also includes extracting at least one record from each of the disparate data sources. Each extracted record is associated with a corresponding person and comprises at least one feature associated with the corresponding person. The method additionally includes distributing the records to computers configured to identify matches between the records based on at least one matching feature associated with two or more records. The method further includes merging the at least one feature from each of the records of each corresponding match into merged records, each merged record corresponding to a different match. The method also includes generating at least one fused view associated with the at least one common person and comprising a merged record.

Abstract: In accordance with particular embodiments, a method includes receiving data from disparate data sources comprising data associated with different people. At least one of the data sources comprises data associated with at least one common person. The method also includes extracting at least one record from each of the disparate data sources. Each extracted record is associated with a corresponding person and comprises at least one feature associated with the corresponding person. The method additionally includes distributing the records to computers configured to identify matches between the records based on at least one matching feature associated with two or more records. The method further includes merging the at least one feature from each of the records of each corresponding match into merged records, each merged record corresponding to a different match. The method also includes generating at least one fused view associated with the at least one common person and comprising a merged record.

Abstract: Disclosed is a system and method for expanding variables within a computational model. The computational model, which can be a Bayesian-network, includes input and output variables that are interrelated via a conditional probability table. Term expansion is accomplished via a lexical database and a logic engine to determine semantic equivalents that are relevant to the computational model. The expanded terms allow the computational model to be related to instance data, which may be in the form of a dynamic ontology. Input variable expansion permits the computational model to be populated with semantically relevant instance data from the ontology, and output variable expansion permits the computational model to be associated with semantically relevant ontology nodes.

Abstract: A system and method for detecting a group of interest from travel information based on a suspect traveler and a co-travel count threshold. The system comprises a database comprised of traveler names, each having respective destinations and corresponding travel dates, and a detection module in communication with the database. The detection module is operable to search the database to determine traveler names having a co-travel count based on the suspect traveler, form a co-travel group based on traveler names having respective co-travel counts greater than or equal to the co-travel count threshold, determine co-travel within said co-travel group, identify cliques within said co-travel group based on said co-travel, and determine the maximal clique thereby detecting the group of interest. The method involves providing a co-travel count threshold, selecting a suspect traveler, and, based on such, detecting a group of interest from travel information.