The present disclosure provides a method and apparatus for constructing an organizational collaboration network, and relates to the field of artificial intelligence, and particularly to the field of big data analysis. A specific implementation includes: acquiring collaborative data between at least one pair of organizations; calculating at least one collaboration index between each pair of organizations according to the collaborative data; calculating, for each pair of organizations, a degree of closeness between the pair of organizations according to a weighted sum of the at least one collaboration index between the pair of organizations; and using each organization as a node, a relationship between each pair of organizations as an edge, and the degree of closeness between each pair of organizations as a weight of the edge, to construct the organizational collaboration network.

An apparatus for providing anonymity in geographic data for probe devices in a geographic region for a location-based service includes at least a database, a clustering calculator and an anonymity controller. The database stores trajectory data based on sequences of sensor measurements of the probe devices. The clustering calculator clusters the trajectory data, according to a first iteration threshold, into clusters each defined by a cluster point and compares distance for a first cluster from the clusters to cluster points of other clusters of the clusters. The clustering calculator selects a second cluster from the clusters based on the comparison of distances and merges the first cluster and the second cluster into a merged cluster. The anonymity controller modifies the trajectory data to provide a predetermined level of anonymity to locations from the trajectory data in response to the merged cluster.

A method to adaptively predict non-default actions against unstructured utterances by an automated assistant operating in a computing-system is provided. The method includes extracting voice-features based on receiving an input utterance from at-least one speaker by an automatic speech recognition (ASR) device, identifying the input utterance as an unstructured utterance based on the extracted voice-features and a mapping between the input utterance with one or more default actions as drawn by the ASR, obtaining at least one probable action to be performed in response to the unstructured utterance through a dynamic bayesian network (DBN). The method further includes providing the at least one probable action obtained by the DBN to the speaker in an order of the posterior probability with respect to each action.

A method implemented in a computing device is provided, which includes collecting nature information proximate a location, and providing an output to a user characterizing the quantity, quality, and/or type of nature areas and/or elements proximate the location. In an enhanced embodiment, a method implemented in a computing device is provided which includes collecting information about the location and character of a user's activities, and providing a dynamic output to the user to dynamically monitor the quantity and quality of an individual's exposure to natural areas.

A computing device determines a disaggregated solution vector of a plurality of variables. A first value is computed for a known variable using a predefined density distribution function, and a second value is computed for an unknown variable using the computed first value, a predefined correlation value, and a predefined aggregate value. The predefined correlation value indicates a correlation between the known variable and the unknown variable. A predefined number of solution vectors is computed by repeating the first value and the second value computations. A solution vector is the computed first value and the computed second value. A centroid vector is computed from solution vectors computed by repeating the computations. A predefined number of closest solution vectors to the computed centroid vector are determined from the solution vectors. The determined closest solution vectors are output.

A method for multivariate time series prediction is provided. Each time series from among a batch of multiple driving time series and a target time series is decomposed into a raw component, a shape component, and a trend component. For each decomposed component, select a driving time series relevant thereto from the batch and obtain hidden features of the selected driving time series, by applying the batch to an input attention-based encoder of an Ensemble of Clustered dual-stage attention-based Recurrent Neural Networks (EC-DARNNS). Automatically cluster the hidden features in a hidden space using a temporal attention-based decoder of the EC-DARNNS. Each Clustered dual-stage attention-based RNN in the Ensemble is dedicated and applied to a respective one of the decomposed components. Predict a respective value of one or more future time steps for the target series based on respective prediction outputs for each of the decomposed components by the EC-DARNNS.

A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

The disclosed computer-implemented method may include generating a three-dimensional (3D) feature map for a digital image using a fully convolutional network (FCN). The 3D feature map may be configured to identify features of the digital image and identify an image region for each identified feature. The method may also include generating a region composition graph that includes the identified features and image regions. The region composition graph may be configured to model mutual dependencies between features of the 3D feature map. The method may further include performing a graph convolution on the region composition graph to determine a feature aesthetic value for each node according to the weightings in the node's weighted connecting segments, and calculating a weighted average for each node's feature aesthetic value to provide a combined level of aesthetic appeal for the digital image. Various other methods, systems, and computer-readable media are also disclosed.

A method, a computing unit and a system for token-based information exchange between a computing unit of a first entity (400A) and a computing unit of one second entity (400B) are presented. The method comprises obtaining (110) a token set (200A) associated with the first entity (400A) and a token set (200B) associated with the one second entity (400B), clustering (120) the token set (200A) associated with the first entity (400A) into clusters, requesting (130) information on tokens (205, 205A, 205B) from the computing unit of the one second entity (400B), receiving (140) information on said tokens (205, 205 A, 205B) from the computing unit of the one second entity (400B), determining (150) an active cluster associated with the first entity (400A), modifying (160) the token subset (310, 320) associated with the determined active cluster of the first entity (400A) at least partly with information on the received tokens (205, 205A, 205B) associated with the second entity (400B).

A gated truncated readout system for position sensitive or imaging detectors that improves resolution over traditional readout systems. The readout system includes two or more amplifiers that receive a multichannel output analog data from the detector. Analog gates control circuitry, included in the readout circuit, receives the signals from the amplifiers, determines a fractional value of the sum-integral of the signals, and enables analog gates operation around an area of interest, disabling all other channels where noise dominates the signal value and thereby improving interpolation accuracy of the signals centroid position and the detector resolution. Filtered signals are transmitted to a centroid interpolation signal processing device for computation of the centroid position. As a result disabling all channels where noise dominates the signal value, the gated truncated readout system provides better accuracy improved detector resolution.