Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

Abstract: Apparatus located at a first mobile telephony network, said network comprising a detector for detecting at least one of roaming registration signals and session management signals of roaming mobile telephony users of said first mobile telephony network who are roaming at another mobile telephony networks; and an analytics unit, associated with said detector, for analyzing said detected signals per individual roaming users, and identifying, from said signals, any one of a plurality of predefined cost saving behaviors of respective individual roaming users. The analytics unit provides an identification output for any of said individual roaming users who is identified to be engaging in one of said predefined cost saving behaviors.

Abstract: A system for manipulating a communication device, the system comprising at least one re-configuration server for remotely re-configuring software installed on a SIM card residing on the communication device by sending a re-configuration to the communication device from a remote location.

Abstract: Apparatus located at a first mobile telephony network, said network comprising a detector for detecting at least one of roaming registration signals and session management signals of roaming mobile telephony users of said first mobile telephony network who are roaming at another mobile telephony networks; and an analytics unit, associated with said detector, for analyzing said detected signals per individual roaming users, and identifying, from said signals, any one of a plurality of predefined cost saving behaviors of respective individual roaming users. The analytics unit provides an identification output for any of said individual roaming users who is identified to be engaging in one of said predefined cost saving behaviors.

Abstract: Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

Abstract: Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

Abstract: Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

Abstract: Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

Abstract: Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

Abstract: Described are methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.