Abstract: Described is a system for producing indicators and warnings of adversarial activities. The system receives multiple networks of transactional data from different sources. Each node of a network of transactional data represents an entity, and each edge represents a relation between entities. A worldview graph is generated by merging the multiple networks of transactional data. Suspicious subgraph regions related to an adversarial activity are identified in the worldview graph through activity detection. The suspicious subgraph regions are used to generate and transmit an alert of the adversarial activity.

Abstract: Described is a system for detecting adversarial activities. During operation, the system generates a multi-layer temporal graph tensor (MTGT) representation based on an input tag stream of activities. The MTGT representation is decomposed to identify normal activities and abnormal activities, with the abnormal activities being designated as adversarial activities. A device can then be controlled based on the designation of the adversarial activities.

Abstract: A method, apparatus, system, and computer program product for managing a platform. Sensor information for a platform health of the platform is received from a sensor system for the platform. The sensor information for the platform health of the platform is sent by a computer system into a machine learning model trained using historical sensor information indicating a historical platform health and historical context information corresponding to the historical sensor information in which the historical context information is for a set of operating conditions. A remaining useful life of a component in the platform is received by the computer system from the machine learning model.

Abstract: Described is a system for feature selection that extends supervised hierarchical clustering to neural activity signals. The system generates, using a hierarchical clustering process, a hierarchical dendrogram representing a set of neural activity data comprising individual neural data elements having neural activity patterns. The hierarchical dendrogram is searched for an optimal cluster parcellation using a stochastic supervised search process. An optimal cluster parcellation of the hierarchical dendrogram is determined that provides a classification of the set of neural activity data with respect to a supervised classifier, resulting in a reduced neural activity feature set. The set of neural activity data is classified using the reduced neural activity feature set, and the classified set of neural activity data is decoded.

Abstract: Described is a system for health assessment. The system is implemented on a mobile device having at least one of an accelerometer, a geographic location sensor, and a camera. In operation, the system obtains sensor data related to an operator of the mobile device from one of the sensors. A network of networks (NoN) is generated based on the sensor data, the NoN having a plurality of layers with linked nodes. Tuples are thereafter generated. Each tuple contains a node from each layer that optimizes importance, diversity, and coherence. Storylines are created based on the tuples that solves a longest path problem for each tuple. The storylines track multiple symptom progressions of the operator. Finally, a disease prediction of the operator is provided based on the storylines.

Abstract: Described is a system for activity and behavior detection in a target system. Raw data extracted from various heterogeneous sources of the target system is fused across spatial and temporal scales into a multi-graph representation. Information flows of the multi-graph representation are analyzed using a set of multi-layer information dynamic measures. Based on the set of multi-layer information dynamic measures, at least one of an economic and social indicator of emerging activity of interest in the target system is derived. The indicator is then used for prediction of future activity of interest in the target system.

Abstract: Described is a system for selecting measurement nodes in a distributed physical system of agents. In operation, the distributed physical system is represented as a multi-layer network having a communication layer and an agent layer. The communication layer represents the amount of collective communication activities between any pair of areas and the agent layer represents movement of agents within the distributed physical system such that the communication layer and agent layer collectively generate network dynamics. The network dynamics are modeled as hybrid partial differential equations (PDEs) with measurable interconnected states in the communication layer. Notably, placement of a minimum set of measurement nodes is determined within the distributed physical system to provide full-state observability of the distributed physical system. The system can then track the full system state and apply compensation to one or more agents in the distributed physical system based on tracking the full system state.

Abstract: Described is a system for predicting future social activity. The system extracts social activities from spatial-temporal social network data collected in a first time period ranging from hours to days to capture spatial structures of social activities in a graph network representation. A graph matching technique is applied over a set of spatial-temporal social network data collected in a second time period ranging from weeks to months to capture temporal structures of the social activities. A spatial-temporal structure of each social activity is represented as an activity core, where each activity core is defined as active nodes that participate in the social activity with a frequency over a predetermined threshold over the second time period. For each activity core, the system computes statistics of the social activity and uses the statistics to generate a prediction of future behaviors of the social activity.

Abstract: Systems and methods according to one or more embodiments are provided for registration of synthetic aperture range profile data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory comprising a plurality of executable instructions. The SAR-based navigation system further includes a processor adapted to receive range profile data associated with observed views of a scene, compare the range profile data to a template range profile data of the scene, and estimate registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.

Abstract: Systems and methods according to one or more embodiments are provided for mapping and registration of synthetic aperture raw radar data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory including executable instructions and a processor adapted to receive phase history data associated with observation views of a scene. The processor further converts the received phase history data associated with the observation views to a range profile of the scene. The range profile is compared to a range profile template of the scene to estimate a geometric transformation of the scene encoded in the received phase history data with respect to a reference template.

Abstract: A method of and apparatus removing of a plurality of relatively narrow banded signals in a relatively wide banded input signal. The method involves and the apparatus provides for compressively sensing one relatively narrow banded signal in the relatively wide banded input signal and removing one relatively narrow banded signal from the relatively wide banded input signal before detecting and removing another relatively narrow banded signal in the relatively wide banded input signal, the step of and apparatus for compressing sensing occurring with respect to both (i) the input signal with the previously detected narrow banded signals removed therefrom and (ii) a frequency shifted version of (i).

Abstract: Described is a system for characterizing communication devices by device type. The system obtains device information for a variety of communication device types, each device type associated with a user account of a bidirectional network. The communication device types are analyzed to perform regional and temporal device characterization, behavioral and feature device characterization, and device homophily analysis on the bidirectional network. The analysis is then used for targeted regional marketing.

Abstract: Described is a system for determining how opinions spread through a network. Opinion dynamics are applied to a network, each node having a corresponding opinion. Each node is described by an active state or an inactive state such that inactive nodes can update their opinions, and active nodes are fixed in their opinion at the time of activation. Inactive nodes can be influenced by both active nodes and inactive nodes. The opinion dynamics proceed in discrete time steps with an influence step for updating each inactive node's opinion, and a stochastic action step for determining whether an inactive node becomes activated. The system identifies how opinions spread through the network using the applied opinion dynamics, resulting in a set of opinion dynamics data. The opinion dynamics data is used to control information that a device or account is allowed to post to social media platform.

Abstract: Described is a low power system for mobile devices that provides continuous, behavior-based security validation of mobile device applications using neuromorphic hardware. A mobile device comprises a neuromorphic hardware component that runs on the mobile device for continuously monitoring time series related to individual mobile device application behaviors, detecting and classifying pattern anomalies associated with a known malware threat in the time series related to individual mobile device application behaviors, and generating an alert related to the known malware threat. The mobile device identifies pattern anomalies in dependency relationships of mobile device inter-application and intra-applications communications, detects pattern anomalies associated with new malware threats, and isolates a mobile device application having a risk of malware above a predetermined threshold relative to a risk management policy.

Abstract: Systems and methods according to one or more embodiments are provided for registration of synthetic aperture range profile data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory comprising a plurality of executable instructions. The SAR-based navigation system further includes a processor adapted to receive range profile data associated with observed views of a scene, compare the range profile data to a template range profile data of the scene, and estimate registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.

Abstract: Described is a system for automated event summarization. A multi-layer network representing a multimodal data set is generated, where nodes within a given layer represent information tokens in a given modality. A topically diverse set of nodes is ranked and selected from each layer to represent temporal event highlights. Temporal event highlights are linked into storylines. Using the storylines, the system monitors a progression of an event or opinions regarding a topic. A temporal summary of the progression of the event or the opinions regarding the topic is generated.

Abstract: Described is an automatic anomaly detector that receives a time-series of normal and abnormal activities that include features related to entities within a computing system. A feature coherence graph for the features is constructed, with the graph then clustered such that feature spaces of entities are expanded to include features that live within a same cluster but belong to separate entities. The feature spaces are unified by mapping representations of the features spaces into a Euclidean space of feature vectors. The feature vectors related to each feature are then aligned. Sets of clusters of related abnormal activities are then generated by regressing each feature vector over only those features that it possesses. The sets of clusters are used to detect anomalous behavior. The system then identifies a node within the computer system generating the anomalous behavior and initiates an action to minimize a threat posed by the node.

Abstract: Described is a system for neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.

Abstract: Described is a system for inferring network dynamics and their sources within the network. During operation, a vector representation is generated based on states of agents in a network. The vector representation including attribute vectors that correspond to the states of the agents in the network. A matrix representation is then generated based on the changing states of agents by packing the attribute vectors at each time step into an attribute matrix. Time-evolving states of the agents are learned using dictionary learning. Influential source agents in the network are then identified by performing dimensionality reduction on the attribute matrix. Finally, in some aspects, an action is executed based on the identity of the influential source agents. For example, marketing material may be directed to a source agent's online account, or the source agent's online account can be deactivated or terminated or some other desired action can be taken.

Abstract: Described is a system for identifying communication behavior patterns in communication activity time series. For each pair of variables in the communication activity time series, the system determines a transfer entropy measure, an effective transfer entropy measure from a randomly reordered version of the communication activity time series, and a partial effective transfer entropy measure. A dependency matrix is generated using pair-wised effective transfer entropy measures and partial effective transfer entropy measures, where each element in the matrix represents a total influence of a communication activity time series on another communication activity time series in the future. The dependency matrix is compared with dependency matrices generated from a predefined set of communication patterns to identify the communication behavior pattern.