Abstract: Determining proximity of data terms based on Walsh-Hadamard transforms is disclosed. One example is a system including a modifier, a Walsh-Hadamard transformer, an indexer, and an evaluator. A dataset, including a plurality of numerical data terms, is received via a processing system. The modifier extends a given data term of the plurality of data terms, the extension based on multiple concatenations of the given data term with itself. The Walsh-Hadamard transformer provides coefficients of the Walsh-Hadamard transform of the modified given data term. The indexer provides a set of keys based on the coefficients, and associates the set of keys with the given data term. The evaluator determines a similarity measure for a pair of data terms of the plurality of data terms, the similarity measure based on a number of overlaps between respective sets of keys, and indicative of proximity of the pair of data terms.

Abstract: Low-level nodes (LLNs) that are communicatively connected to one another each have sensing capability and processing capability. High-level nodes (HLNs) that are communicatively connected to one another and to the LLNs each have processing capability more powerful than the processing capability of each LLN. The LLNs and the HLNs perform processing based on sensing events captured by the LLNs. The processing is performed by the LLNs and the HLNs to minimize data communication among the LLNs and the HLNs, and to provide for software-defined sensing.

Abstract: Interactive sequential pattern mining is disclosed. One example is a system including a sequence miner, and an interaction processor. A sequence database is received, the sequence database including a plurality of input sequences, where each sequence of the plurality of input sequences is an ordered list of events, and each event in the list of events includes at least one item. The sequence miner mines the sequence database for a plurality of candidate sequence patterns, the mining based on an interaction with a user. The interaction processor processes the interaction with the user, the interaction based on domain relevance of the plurality of candidate sequence patterns to the user.

Abstract: Secure information retrieval is disclosed. One example is a system including an information retriever comprising a collection of nodes that receive a hash count from a first dataset, the first dataset including a first data term, and provide the hash count to a second dataset, the second dataset including a plurality of second data terms. A hash transformer transforms the data terms based on the hash count. A modifier modifies, for a given node, the transformed data terms. An evaluator evaluates, for each node, a similarity value between the first data term and each given second data term based on shared data elements between the modified first data term and a given modified second data term associated with the given second data term. The information retriever provides to the first dataset, at least one term identifier associated with a second data term.

Abstract: In one embodiment, for a first image, a first vector of similarity to a set of reference images is calculated as a first face descriptor, and for a second image, a second vector of similarity to the set of reference images is calculated as a second face descriptor. A similarity measure between the first face descriptor and the second face descriptor is then calculated.

Abstract: A system and method for classification of passenger vehicles and measuring their properties, and more particularly to a stochastic multi-class vehicle classification system, which classifies a vehicle (given its direct rear-side view) into one of four classes Sedan, Pickup truck, SUV/Minivan, and unknown, and wherein a feature pool of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector, and the feature vector is then processed by a Hybrid Dynamic Bayesian Network (HDBN) to classify each vehicle.

Abstract: A system and method for classification of passenger vehicles and measuring their properties, and more particularly to a stochastic multi-class vehicle classification system, which classifies a vehicle (given its direct rear-side view) into one of four classes Sedan, Pickup truck, SUV/Minivan, and unknown, and wherein a feature pool of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector, and the feature vector is then processed by a Hybrid Dynamic Bayesian Network (HDBN) to classify each vehicle.

Abstract: In one embodiment, for a first image, a first vector of similarity to a set of reference images is calculated as a first face descriptor, and for a second image, a second vector of similarity to the set of reference images is calculated as a second face descriptor. A similarity measure between the first face descriptor and the second face descriptor is then calculated.