Abstract: A computer-executed method is disclosed for collective navigation of distributed virtual reality (VR) devices. The method obtains a source vertex and a destination vertex for a VR device. The source vertex and the destination vertex include vertices of a graph model of a navigable space having a plurality of vertices. The vertices represent a point within the navigable space and the plurality of edges represent a path segment between two corresponding vertices. A subset of possible vertices, selected from the plurality of vertices, is determined for a navigable path. A vertex traffic potential is determined for each vertex of the subset of possible vertices. The navigable path, including one or more consecutive path segments selected to minimize both segment path lengths and vertex traffic potentials, is determined from the source vertex to the destination vertex.

Abstract: At each of a plurality of unmanned aerial vehicles operating in a three-dimensional space, current relative locations of other ones of the plurality of unmanned aerial vehicles which are visible to each of the plurality of unmanned aerial vehicles are determined based on two-dimensional camera images from on-board dual-lens cameras. The current relative locations are wirelessly transmitted to a controller. At the plurality of unmanned aerial vehicles, specification of a path to be followed by each of the plurality of unmanned aerial vehicles is wirelessly received from the controller. At each of the plurality of unmanned aerial vehicles, on-board collision avoidance is carried out based on the two-dimensional camera images from the on-board dual-lens cameras, while following the path specification.

Abstract: A method for clustering vertices of streaming graphs includes: maintaining one or more clusters, assigning a random number to an incoming edge, computing a sampling threshold based on the current clusters, and adjusting the current clusters based on the random number and the sampling threshold.

Abstract: A query image is obtained. In a database including a plurality of reference image graphs, at least one of the reference image graphs, with feature vectors similar to the query image, is identified. Image querying is carried out by graph traversal on the at least one of the reference image graphs with the feature vectors similar to the query image. An image from the at least one of the reference image graphs having a highest matching score in the graph traversal is returned as a response to the query image. techniques for building the database are also disclosed.

Abstract: A computer-implemented method, computer program product, and system for determination of critical parts and component correlations in a circuit using a correlation graph and centrality analysis including; receiving a circuit layout portion of a larger circuit layout, converting the circuit layout portion into a correlation graph representing components as nodes and connecting wires as edges, determining, using ground truth and Naïve Bayes to determine correlation weighting, scaling the correlation graph to represent the larger circuit, and presenting the larger correlation graph on a graphical user interface (GUI).

Abstract: Techniques that facilitate heterogeneous runahead processing for a processor core are provided. In one example, a first core performs a first execution of a first sequence of instructions, where the first core is communicatively coupled to a first cache memory. A second core performs a second execution of at least a portion of the first sequence of instructions and a first determination that data associated with the first sequence of instructions fails to be stored in the first cache memory, where the first determination is performed concurrent with the first execution, and the first core executes a second sequence of instructions based on a second determination that the second core is performing the second execution of at least a portion of the first sequence of instructions.

Abstract: Determining a number of kernels within a model is provided. A number of kernels that include data samples of a majority data class of an imbalanced training data set is determined based on a set of generated artificial data samples for a minority data class of the imbalanced training data set. The number of kernels within the model is generated based on the set of generated artificial data samples. A likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set is calculated. Parameters of each kernel in the number of kernels are updated based on the likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set. Each kernel in the number of kernels is adjusted based on the updated parameters.

Abstract: A system, method and computer program product for real-time recognition of individuals. The method comprises: receiving at a hardware processor, a first sensor data associated with the individual. The first sensor data associated with the individual is processed to determine an activity the individual is currently engaged in. Additionally, a second sensor data associated with the individual is received and processed to determine a feature associated with a muscle of said individual while engaged in activity. Based on the received sensor data and determined activity and muscle features, the method generates a model that correlates determined activities and corresponding muscle features of the individual user with the individual's identity. The model is subsequently used to identify the individual. By receiving and inputting real-time, sensor data from an individual into said generated model, the model generates and determines: an activity and a muscle feature for use in identifying the individual.

Abstract: A system and method for painting a structure. The system comprises: a computer vision processing system configured to obtain images of a target structure and generate first instructions signals for real time communication to an unmanned aerial vehicle (UAV) having a paint fluid dispensing system provided thereon; a control device at the UAV responsive to received the first instruction signals for controlling real time navigating of the UAV to a location at the target structure; and the computer vision processing system configured to generate second instruction signals for real time communication to the UAV, wherein the control device at the UAV is configured to automatically actuate the paint fluid dispensing system to apply a paint fluid at the location on the target structure in response, the received second instruction signals configuring the UAV to render a desired visual image on the target structure.

Abstract: Methods and systems for inferring performance include a chain graph that has multiple nodes, one or more of which has a known value at a first timestamp. A value associated with a first node at the first timestamp influences a value associated with a second node connected to the first node by an edge at the first timestamp. Each node has an associated chain that represents values of the node at different timestamps. Known values of each node in the graph are propagated within each timestamp to all neighboring nodes. Known values of each node in the graph are propagated across different timestamps.

Abstract: One or more biological signals are obtained. The one or more biological signals are converted to one or more graph structures. Correlation between two or more of the biological signals are determined using the one or more graph structures. One or more changes in the one or more graph structures within a time window are recorded. A signal graph model is generated based on the recorded changes.

Abstract: Identity recognition is achieved using user's living signatures, such as a heartbeat signal, from multiple devices. A user requests identity recognition for using a first registered device, such as a mobile device. The request is provided to a cloud. When the cloud confirms the mobile device is a registered device, the cloud directs the first registered device to upload the user living signature to the cloud. The cloud also sends a request to a user second registered device to upload the user living signature from the second registered device to the cloud. The user second registered device may be a headband monitor, watch, Google glasses, or the like capable of monitoring and uploading the user living signature to the cloud. The cloud compares both living signature signals from the first registered device and from the second registered device. If the signals are matched, the user identity is confirmed. If the signals are not matched, the user identity is not confirmed.

Abstract: A method for predicting insider threat includes mining electronic data of an organization corresponding to activity of an entity, determining features of the electronic data corresponding to the activity of the entity, classifying the features corresponding to the activity of the entity, determining sequences of classified features matching one or more patterns of insider threat, scoring the entity according to matches of the classified features to the one or more patterns of insider threat, and predicting an insider threat corresponding to the entity according to the score.

Abstract: A server and/or a client stores a metadata hash map that includes one or more entries associated with keys for data records stored in a cache on a server, wherein the data records comprise a directed acyclic graph (DAG), and the directed acyclic graph is comprised of a collection of one or more nodes connected by one or more edges, each of the nodes representing one or more tasks ordered into a sequence, and each of the edges representing one or more constraints on the nodes connected by the edges. Each of the entries stores metadata for a corresponding data record, wherein the metadata comprises a server-side remote pointer that references the corresponding data record stored in the cache.

Abstract: A server and/or a client stores a metadata hash map that includes one or more entries associated with keys for data records stored in a cache on a server, wherein the data records comprise a directed acyclic graph (DAG), and the directed acyclic graph is comprised of a collection of one or more nodes connected by one or more edges, each of the nodes representing one or more tasks ordered into a sequence, and each of the edges representing one or more constraints on the nodes connected by the edges. Each of the entries stores metadata for a corresponding data record, wherein the metadata comprises a server-side remote pointer that references the corresponding data record stored in the cache.

Abstract: A server and/or a client stores a metadata hash map that includes one or more entries associated with keys for data records stored in a cache on a server, wherein the data records comprise a directed acyclic graph (DAG), and the directed acyclic graph is comprised of a collection of one or more nodes connected by one or more edges, each of the nodes representing one or more tasks ordered into a sequence, and each of the edges representing one or more constraints on the nodes connected by the edges. Each of the entries stores metadata for a corresponding data record, wherein the metadata comprises a server-side remote pointer that references the corresponding data record stored in the cache.

Abstract: Methods and systems for inferring performance include a chain graph that has multiple nodes, one or more of which has a known value at a first timestamp. A value associated with a first node at the first timestamp influences a value associated with a second node connected to the first node by an edge at the first timestamp. Each node has an associated chain that represents values of the node at different timestamps. Known values of each node in the graph are propagated within each timestamp to all neighboring nodes. Known values of each node in the graph are propagated across different timestamps.

Abstract: A server and/or a client stores a metadata hash map that includes one or more entries associated with keys for data records stored in a cache on a server, wherein the data records comprise a directed acyclic graph (DAG), and the directed acyclic graph is comprised of a collection of one or more nodes connected by one or more edges, each of the nodes representing one or more tasks ordered into a sequence, and each of the edges representing one or more constraints on the nodes connected by the edges. Each of the entries stores metadata for a corresponding data record, wherein the metadata comprises a server-side remote pointer that references the corresponding data record stored in the cache.

Abstract: A server and/or a client stores a metadata hash map that includes one or more entries associated with keys for data records stored in a cache on a server, wherein the data records comprise a directed acyclic graph (DAG), and the directed acyclic graph is comprised of a collection of one or more nodes connected by one or more edges, each of the nodes representing one or more tasks ordered into a sequence, and each of the edges representing one or more constraints on the nodes connected by the edges. Each of the entries stores metadata for a corresponding data record, wherein the metadata comprises a server-side remote pointer that references the corresponding data record stored in the cache.

Abstract: Injecting generated data samples into a minority data class of an imbalanced training data set is provided. In response to receiving an input to balance the imbalanced training data set that includes a majority data class and the minority data class, a set of data samples is generated for the minority data class. A distance is calculated from each data sample in the set of generated data samples to a center of a kernel that includes a set of data samples of the majority data class. Each data sample in the set of generated data samples is stored within a corresponding distance score bucket based on the calculated distance of a data sample. Generated data samples are selected from a number of highest ranking distance score buckets. The generated data samples selected from the number of highest ranking distance score buckets are injected into the minority data class.