Abstract: A Support Vector Machine (SVM) classifier employs a kernel ƒ(xi,y)??d=1DF(xi,d,yd) or ƒ(xi,y)??d=1DF(xi,d,yd) where D is the dimensionality of Support Vectors xi and vector y is a vector being classified, e.g. representing a message communicated on a Controller Area Network (CAN) bus of a vehicle. The SVM classifier comprises a microprocessor or microcontroller programmed to perform classification using the SVM classifier with at least some terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table. For those terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table, the dimension yd can only assume values defined by a finite set of values. In an embedded system, the microprocessor or microcontroller performs an embedded system function such as engine control, brake control, entertainment system operation, or so forth, or an embedded system function for a vehicle, ocean buoy, or satellite.

Abstract: A security monitoring system for a Controller Area Network (CAN) comprises an Electronic Control Unit (ECU) operatively connected to the CAN bus. The ECU is programmed to classify a message read from the CAN bus as either normal or anomalous using an SVM-based classifier with a Radial Basis Function (RBF) kernel. The classifying includes computing a hyperplane curvature parameter ? of the RBF kernel as ?=f(D) where f( ) denotes a function and D denotes CAN bus message density as a function of time. In some such embodiments ?=f(Var(D)) where Var(D) denotes the variance of the CAN bus message density as a function of time. The security monitoring system may be installed in a vehicle (e.g. automobile, truck, watercraft, aircraft) including a vehicle CAN bus, with the ECU operatively connected to the vehicle CAN bus to read messages communicated on the CAN bus.

Abstract: A security monitoring system for a Controller Area Network (CAN) comprises an Electronic Control Unit (ECU) operatively connected to the CAN bus. The ECU is programmed to classify a message read from the CAN bus as either normal or anomalous using an SVM-based classifier with a Radial Basis Function (RBF) kernel. The classifying includes computing a hyperplane curvature parameter ? of the RBF kernel as ?=ƒ(D) where ƒ( ) denotes a function and D denotes CAN bus message density as a function of time. In some such embodiments ?=ƒ(Var(D)) where Var(D) denotes the variance of the CAN bus message density as a function of time. The security monitoring system may be installed in a vehicle (e.g. automobile, truck, watercraft, aircraft) including a vehicle CAN bus, with the ECU operatively connected to the vehicle CAN bus to read messages communicated on the CAN bus.

Abstract: Systems, methods, and computer-readable media are provided for processing geographic data and identifying geographic locations. In one implementation, a computer-implemented method is provided that is performed by one or more processors, the method comprising operations including receiving a query including an address, parsing the address into tokens, and sending the tokens to one or more harvesters. The method also includes receiving one or more geographic location candidates from the one or more harvesters in response to the tokens, comparing the one or more candidates, and returning one or more geographic locations based on the comparison.

Abstract: A security monitoring system for a Controller Area Network (CAN) comprises an Electronic Control Unit (ECU) operatively connected to the CAN bus. The ECU is programmed to classify a message read from the CAN bus as either normal or anomalous using an SVM-based classifier with a Radial Basis Function (RBF) kernel. The classifying includes computing a hyperplane curvature parameter ? of the RBF kernel as ?=f(D) where f( ) denotes a function and D denotes CAN bus message density as a function of time. In some such embodiments ?=f(Var(D)) where Var(D) denotes the variance of the CAN bus message density as a function of time. The security monitoring system may be installed in a vehicle (e.g. automobile, truck, watercraft, aircraft) including a vehicle CAN bus, with the ECU operatively connected to the vehicle CAN bus to read messages communicated on the CAN bus.

Abstract: Computer-implemented systems and methods are provided for parsing search queries. In accordance with some embodiments, search records including character strings are retrieved, and search query templates are generated comprising sequences of categories corresponding to character substrings of the character strings. Also, in accordance with some embodiments, search queries are parsed into character substrings and matched with a search query template. The search query template may then be used to associate categories with the character substrings. A search engine may use the categories to focus or otherwise refine a search based on parsed search query.

Abstract: A security monitoring system for a Controller Area Network (CAN) comprises an Electronic Control Unit (ECU) operatively connected to the CAN bus. The ECU is programmed to classify a message read from the CAN bus as either normal or anomalous using an SVM-based classifier with a Radial Basis Function (RBF) kernel. The classifying includes computing a hyperplane curvature parameter ? of the RBF kernel as ?=ƒ(D) where ƒ( ) denotes a function and D denotes CAN bus message density as a function of time. In some such embodiments ?=ƒ(Var(D)) where Var(D) denotes the variance of the CAN bus message density as a function of time. The security monitoring system may be installed in a vehicle (e.g. automobile, truck, watercraft, aircraft) including a vehicle CAN bus, with the ECU operatively connected to the vehicle CAN bus to read messages communicated on the CAN bus.

Abstract: A security monitoring system for a Controller Area Network (CAN) comprises an Electronic Control Unit (ECU) operatively connected to the CAN bus. The ECU is programmed to classify a message read from the CAN bus as either normal or anomalous using an SVM-based classifier with a Radial Basis Function (RBF) kernel. The classifying includes computing a hyperplane curvature parameter ? of the RBF kernel as ?=ƒ(D) where ƒ( ) denotes a function and D denotes CAN bus message density as a function of time. In some such embodiments ?=ƒ(Var(D)) where Var(D) denotes the variance of the CAN bus message density as a function of time. The security monitoring system may be installed in a vehicle (e.g. automobile, truck, watercraft, aircraft) including a vehicle CAN bus, with the ECU operatively connected to the vehicle CAN bus to read messages communicated on the CAN bus.

Abstract: A Support Vector Machine (SVM) classifier employs a kernel ƒ(xi,y)??d=1DF(xi,d,yd) or ƒ(xi,y)??d=1DF(xi,d,yd) where D is the dimensionality of Support Vectors xi and vector y is a vector being classified, e.g. representing a message communicated on a Controller Area Network (CAN) bus of a vehicle. The SVM classifier comprises a microprocessor or microcontroller programmed to perform classification using the SVM classifier with at least some terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table. For those terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table, the dimension yd can only assume values defined by a finite set of values. In an embedded system, the microprocessor or microcontroller performs an embedded system function such as engine control, brake control, entertainment system operation, or so forth, or an embedded system function for a vehicle, ocean buoy, or satellite.

Abstract: Computer-implemented systems and methods are provided for parsing search queries. In accordance with some embodiments, search records including character strings are retrieved, and search query templates are generated comprising sequences of categories corresponding to character substrings of the character strings. Also, in accordance with some embodiments, search queries are parsed into character substrings and matched with a search query template. The search query template may then be used to associate categories with the character substrings. A search engine may use the categories to focus or otherwise refine a search based on parsed search query.

Abstract: Computer-implemented systems and methods are provided for parsing search queries. In accordance with some embodiments, search records including character strings are retrieved, and search query templates are generated comprising sequences of categories corresponding to character substrings of the character strings. Also, in accordance with some embodiments, search queries are parsed into character substrings and matched with a search query template. The search query template may then be used to associate categories with the character substrings. A search engine may use the categories to focus or otherwise refine a search based on parsed search query.

Abstract: Computer-implemented systems and methods are provided for parsing search queries. In accordance with some embodiments, search records including character strings are retrieved, and search query templates are generated comprising sequences of categories corresponding to character substrings of the character strings. Also, in accordance with some embodiments, search queries are parsed into character substrings and matched with a search query template. The search query template may then be used to associate categories with the character substrings. A search engine may use the categories to focus or otherwise refine a search based on parsed search query.