Abstract: A system for security screening at a venue includes one or more security screening stations, each station having a security level and configured to screen a plurality of subjects in a queue for admitting the subject to the venue. The system may determine the current queue length of all the queues associated with security screening stations based on data received from one or more sensors. The system may also determine whether the throughput for at least one security screening station should be increased or decreased. If the system so determines, the system may decrease or increase, for a variation time period, the security level of the security screening station associated with the queue. The system may also adjust configuration of the stations or place a secondary and complementary screening process to dynamically improve the performance of the security system.

Abstract: Input data queries directed at a plurality of target databases and originating from any of a plurality of sources are first converted to validated canonical forms, which are then used to query the target databases. Specifically, upon receiving an input data query, a relatively accurate reference database is selected based on the type of the input data. This reference is then queried for the input data with the intent of finding an exact matching record or a near-matching record that can be considered an exact match and thereby validating the input data. Otherwise, the requesting source is instructed to provide a new query. Once having a validated record, it is converted to a canonical form, which is then used to query the target databases intended to be searched. In a further embodiment, multiple reference databases are queried to determine a canonical form of the data or to determine multiple canonical forms of the data.

Abstract: Input data queries directed at a plurality of target databases and originating from any of a plurality of sources are first converted to validated canonical forms, which are then used to query the target databases. Specifically, upon receiving an input data query, a relatively accurate reference database is selected based on the type of the input data. This reference is then queried for the input data with the intent of finding an exact matching record or a near-matching record that can be considered an exact match and thereby validating the input data. Otherwise, the requesting source is instructed to provide a new query. Once having a validated record, it is converted to a canonical form, which is then used to query the target databases intended to be searched. In a further embodiment, multiple reference databases are queried to determine a canonical form of the data or to determine multiple canonical forms of the data.

Abstract: Input data queries directed at a plurality of target databases and originating from any of a plurality of sources are first converted to validated canonical forms, which are then used to query the target databases. Specifically, upon receiving an input data query, a relatively accurate reference database is selected based on the type of the input data. This reference is then queried for the input data with the intent of finding an exact matching record or a near-matching record that can be considered an exact match and thereby validating the input data. Otherwise, the requesting source is instructed to provide a new query. Once having a validated record, it is converted to a canonical form, which is then used to query the target databases intended to be searched. In a further embodiment, multiple reference databases are queried to determine a canonical form of the data or to determine multiple canonical forms of the data.

Abstract: Indexing, searching, and retrieving the content of speech documents (including but not limited to recorded books, audio broadcasts, recorded conversations) is accomplished by finding and retrieving speech documents that are related to a query term at a conceptual level, even if the speech documents does not contain the spoken (or textual) query terms. Concept-based cross-media information retrieval is used. A term-phoneme/document matrix is constructed from a training set of documents. Documents are then added to the matrix constructed from the training data. Singular Value Decomposition is used to compute a vector space from the term-phoneme/document matrix. The result is a lower-dimensional numerical space where term-phoneme and document vectors are related conceptually as nearest neighbors. A query engine computes a cosine value between the query vector and all other vectors in the space and returns a list of those term-phonemes and/or documents with the highest cosine value.

Abstract: Techniques for using latent semantic structure of textual content ascribed to the items to provide automatic recommendations to the user. A user inputs a selected item and, in turn, a latent semantic algorithm is applied to the user selection and the textual content of the items in a database to generate a conceptual similarity between the selection and the items. A set of nearest items to the selected item is provided as a recommendation to the user of other items that may be of particular interest or relevance to the user's original selection based upon the conceptual similarity measure.

Abstract: Techniques for using latent semantic structure of textual content ascribed to the items to provide automatic recommendations to the user. A user inputs a selected item and, in turn, a latent semantic algorithm is applied to the user selection and the textual content of the items in a database to generate a conceptual similarity between the selection and the items. A set of nearest items to the selected item is provided as a recommendation to the user of other items that may be of particular interest or relevance to the user's original selection based upon the conceptual similarity measure.