Abstract: A vehicle system prognosis apparatus including sensor(s) for detecting a characteristic of a vehicle system and generating at least one time series of condition indicator values, and a processor that receives the at least one time series and generates an analysis model, for the characteristic, that is trained with one or more of the at least one time series, that are obtained from the one or more sensors with the vehicle system operating under normal conditions, extracts from the at least one time series one or more features embodying an indication of a health of the vehicle system, generates a quantified health assessment of the vehicle system by quantifying the one or more features based on a normal distribution of the one or more features from the analysis model, and communicates the quantified health assessment of the vehicle system to an operator or crew member of the vehicle.

Abstract: A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

Abstract: Described is a system for predicting temporal evolution of contagions on multilayer networks. The system determines a functional epidemic threshold for disappearance of a contagion on a multilayer network model according to a score value s=??/?, where ? corresponds to an adjacency matrix of the first layer of the multilayer network model, ? represents a spread rate of the contagion, and ? represents a recovery rate. A prediction of future behavior of the contagion on the multilayer network model using the functional epidemic threshold is output and utilized to inform decisions regarding connectivity within a multilayer network in order to prevent spread of the contagion on a multilayer network.

Abstract: Described is a system for multiscale monitoring. During operation, the system receives surveillance data of a scene having a plurality of zones. The surveillance data includes an object flow tensor V indicating a number of objects flowing from one zone to another zone at time t and an object communication tensor C indicating a number of communications sending from one zone to another zone at time t. The system then determines a cluster membership of the plurality of zones. Dependency links between communications and flows are then determined. At least one cluster of one or more zones is designated as a region of interest based on the dependency links, which allows the system to control a device based on the designated region(s) of interest.

Abstract: Described is a system for modeling strategic network formation. A network formation model is generated using a concatenation of joint strategies s of a set of N agents, such that s=sSsAsC. The N agents include a group of source agents NS, a group of aggregator agents NA, and a group of consumer agents NC, each group of agents having a distinct joint strategy for accessing a set of information. sS represents a joint strategy of the group of source agents, sA represents a joint strategy of the group of aggregator agents, and sC represents a joint strategy of the group of consumer agents. The network formation model is operated according to the joint strategies of the set of N agents. Data relating to the set of N agents of the network formation model is output for the investigation of the formation of information diffusion networks.

Abstract: A computer-implemented method, system, and computer program product are provided. A plurality of maintenance messages (MMSGs) are identified. Each MMSG is associated with at least one shut-off valve. A sensor parameter is identified based on an analysis of sensor parameters associated with the shut-off valves of each MMSG. A threshold value for the sensor parameter is identified as being associated with abnormal operation of the respective shut-off valves. A sensor associated with a first shut-off valve captures values for the sensor parameter during a first and second predefined time period, the first and second predefined time periods associated with an opening and a closing of the first shut-off valve. Upon determining that a difference between the maximum values of the sensor values captured during the first and second predefined time periods exceeds the first threshold value, a determination is made that the first shut-off valve is operating abnormally.

Abstract: Described is a system for inducing a desired behavioral effect using an electrical current stimulation. A brain monitoring subsystem includes monitoring electrodes for sensing brain activity, and a brain stimulation subsystem includes stimulating electrodes for applying an electrical current stimulation. Multi-scale distributed data is registered into a graphical representation. The system identifies a sub-graph in the graphical representation and maps the sub-graph onto concept features, generating a concept lattice which relates the concept features to a behavioral effect. Finally, an electrical current stimulation to be applied to produce the behavioral effect is determined.

Abstract: Described is a system for real world event summarization with microblog data. The system collects microblog image posts related to a real world event based on an event tag and generates a heterogeneous network (having nodes and edges) based on the microblog image posts. The nodes are ranked and images are then extracted from top-ranked nodes for display to the user as a real world event summarization.

Abstract: Described is a system for determining reliability of nodes in a mobile wireless network. The system is operable for receiving an Exploitation Network (Xnet) database. The Xnet database has an Xnet structure formed of a physical node layer (NetTopo), a network dependent (NetDep) layer, and an application dependent (AppDep) layer. The NetTopo layer includes NetTopo graphs reflecting connectivity between the nodes. The NetDep layer includes NetDep graphs reflecting connectivity dependencies amongst the nodes, and the AppDep layer includes Appdep graphs reflecting software application dependencies amongst the nodes. An Xnet Analytics Engine is run that monitors and evaluates reliability of each node in the mobile wireless network to provide a reliability estimate of each node.

Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, obtaining a plurality of sensor signals corresponding to the predetermined characteristic, receiving the plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, generating, a matrix of time series data based on the input time series of data, clustering the matrix of time series data based on predetermined clustering criteria into a predetermined number of clusters, generating a sparse temporal matrix based on the predetermined number of clusters, extracting extracted features that are indicative of an operation of a vehicle system from the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: Described is a system for detecting attacks on networks. A hierarchical representation of activity of a communication network is used to detect and predict sources of misinformation in the communication network. The hierarchical representation includes temporal patterns of communication between at least one pair of nodes, each temporal pattern representing a motif, having a size, in the hierarchical representation. Changes in motifs provide a signal for a misinformation attack.

Abstract: A system for monitoring aircraft operational messages is provided. The system includes a computing device including a processor in communication with a memory. The computing device is programmed to receive a plurality of historical messages for a plurality of aircraft. Each message of the plurality of historical messages is a message from one of the plurality of aircraft. The computing device is also programmed to receive a plurality of historical maintenance operations performed on the plurality of aircraft, compare the plurality of historical messages to the plurality of historical maintenance operations to determine at least one message type associated with at least one maintenance operation type, and generate a plurality of message type correlations between message types and maintenance operation types based on the comparison.

Abstract: A vehicle may include at least one operative sub-system that includes at least one sensor configured to output one or more sensor signals related to the at least one operative sub-system. A monitoring system is in communication with the operative sub-system(s). The monitoring system is configured to correlate the one or more sensor signals with respect to time, compile initial statistics of the one or more sensor signals with respect to a plurality of variables; and correlate the plurality of variables.

Abstract: Network of networks (NoN) structure reconstruction employs compressed sensing with multivariate time series data and graph partitioning to reconstruct a node-to-node connection structure of an NoN. The NoN structure reconstruction includes determining an adjacency matrix of the NoN from the multivariate time series data using the compressed sensing. Partitioning a graph representing the determined adjacency matrix into subgraphs provides the reconstruction of the node-to-node connection structure.

Abstract: Described is a system for tracking and predicting social events. The system filters a time series of data obtained from a social media source. Enhanced filtered signals (EFS) are extracted from the filtered time series data based on an amplification signal obtained via a summation of signals relevant to a process of interest in the filtered time series data. A level of human social activity in the social media source is monitored by comparing the extracted EFS to an event database to detect an increase in a number of social activity events in the social media source compared to the event database.

Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.

Abstract: Described is a system for discovering user interests through online social media, and more specifically, to a way of doing so by means of a bi-directional graph model. During operation, the system generates a confidence matrix F based on user interactions and co-occurring tags on a social media platform. The confidence matrix F indicates a likelihood of the users in the social media platform as being interested in a particular topic. Based on such likelihoods, an action can be initiated regarding a particular topic for those users whose likelihood of being interested in the particular topic exceeds a predetermined threshold. For example, the system generates and presents an online advertisement to users regarding a particular topic to those users whose likelihood of being interested in the particular topic exceeds a predetermined threshold.

Abstract: Described is a system for detecting attacks on networks. A hierarchical representation of activity of a communication network is used to detect and predict sources of misinformation in the communication network. The hierarchical representation includes temporal patterns of communication between at least one pair of nodes, each temporal pattern representing a motif, having a size, in the hierarchical representation. Changes in motifs provide a signal for a misinformation attack.

Abstract: Described is a system for identifying emerging trends in a consumer product from heterogeneous online data sources. Data extracted from heterogeneous data sources is fused, and consumer product data is identified from the fused data. A baseline distribution for consumer issues related to consumer products is generated from the set of consumer product data. A deviation value from the baseline distribution is determined for a specific consumer product. Indicators for future consumer issues regarding the specific consumer product are identified based on the deviation value. The indicators are reported to a system analyst.

Abstract: Described is a system for using social media data to supplement survey data for discrete choice analysis. Survey data from consumers is segmented into demographic groups. Individual demographic attributes and consumer product attribute preferences are extracted from a set of social media data. Consumer product attribute preferences are determined for each demographic group using the set of social media data. Consumers' preference coefficients are generated for each demographic group. Finally, individualized incentives for a target consumer product are determined using the consumers' preference coefficients.