Abstract: A method and system for modeling a cloud environment as a security graph are provided. The method includes identifying security objects in the cloud environment; collecting object data of the identified security objects; constructing security graph based on collected object data of the identified security objects; determining relationships among the identified security objects, wherein the relationships are determined based on the collected object data of the identified security objects and using a static analysis process; updating the constructed security graph with the determined relationships among the identified security objects; and storing the constructed security graph in a graph database.

Abstract: A system and method for generation of unified graph models for network entities are provided. The method includes collecting, for at least one network entity of a plurality of network entities, at least one network entity data feature, wherein the at least one network entity data feature is a network entity property; genericizing the collected at least one network entity; generating at least a network graph, wherein the generated network graph is a multi-dimensional data structure providing a representation of the plurality of network entities and relations between the network entities of the plurality of network entities; and storing the generated at least a network graph.

Abstract: A method for managing distributed computing. The method comprises estimating, for each of a plurality of local computing nodes, a distribution of multidimensional values in a space. Each multidimensional value is calculated according to a plurality of locally monitored parameters. The method further includes calculating safe zones in the space where each safe zone is defined according to a respective estimated distribution under a global geometric constraint in the space and setting local geometric constraints for the local computing nodes according to the respective safe zones. Each local geometric constraint is defined such that a detection of at least one monitored multidimensional value violating it by a respective local computing node induces a communication event between the respective local computing node and one or more central computing nodes.

Abstract: A method for distributed computing includes processing multiple sets of data at respective computing nodes (24), and calculating respective local values of one or more statistical parameters characterizing the sets of the data. A global condition is defined, such that the condition is violated when a function defined over a weighted average of the respective local values crosses a predetermined threshold. The global condition is separated into a plurality of local constraints, which include a respective local constraint to be evaluated by each of the nodes based on the respective local values, such that violation of the respective local constraint in at least one of the nodes indicates a violation of the global condition. The local constraint is evaluated independently at each of the nodes. When at least one of the nodes detects that the respective local constraint is violated, an indication that the global condition has been violated is produced.

Abstract: A method for managing distributed computing. The method comprises estimating, for each of a plurality of local computing nodes, a distribution of multidimensional values in a space. Each multidimensional value is calculated according to a plurality of locally monitored parameters. The method further includes calculating safe zones in the space where each safe zone is defined according to a respective estimated distribution under a global geometric constraint in the space and setting local geometric constraints for the local computing nodes according to the respective safe zones. Each local geometric constraint is defined such that a detection of at least one monitored multidimensional value violating it by a respective local computing node induces a communication event between the respective local computing node and one or more central computing nodes.

Abstract: A method for distributed computing includes processing multiple sets of data at respective computing nodes (24), and calculating respective local values of one or more statistical parameters characterizing the sets of the data. A global condition is defined, such that the condition is violated when a function defined over a weighted average of the respective local values crosses a predetermined threshold. The global condition is separated into a plurality of local constraints, which include a respective local constraint to be evaluated by each of the nodes based on the respective local values, such that violation of the respective local constraint in at least one of the nodes indicates a violation of the global condition. The local constraint is evaluated independently at each of the nodes. When at least one of the nodes detects that the respective local constraint is violated, an indication that the global condition has been violated is produced.

Abstract: This disclosure provides a system for classifying images, used in image detection, image recognition, or other computer vision. The system processes directory images to obtain eigenvectors and eigenvalues, and selects a set of “smooth” basis vectors formed by linear combinations of these eigenvectors to be applied against a target image. Contrary to conventional wisdom, however, a group of the eigenvectors having the weakest eigenvalues are used to select the basis vectors. A second process is then performed on this group of “weakest” eigenvectors to identify a set of candidate vectors, ordered in terms of “smoothness.” The set of basis vectors (preferably 3-9) is then chosen from the candidate vectors in order of smoothness, which are then applied in an image detection or image recognition process.

Abstract: A method for operating a data processing system to generate a second image from a first image having partially sampled color values at each pixel. The first image includes a two-dimensional array of pixel values, each of the pixel values corresponding to the light intensity in one of a plurality of spectral bands at a location in the first image. The second image includes a second two-dimensional array of color vectors. Each color vector has a light intensity value for each of the spectral bands. There is one such vector corresponding to each location having a pixel value in the first image. One component of the vector is equal to the pixel value in the first image at that location. The present invention computes the missing color components at each location. The method begins by providing an estimate for each component that is not equal to one of the pixel values from the first image for each vector.

Abstract: A method for operating a data processing system to generate a full color image from a partially sampled version of the image. The full color image includes a first two-dimensional array of vectors having components representing the intensity of a pixel in the full color image in a corresponding spectral band at a location determined by the location of the vector in the first two-dimensional array. The method generates the first two-dimensional array from a two-dimensional array of scalars. Each scalar determines one of the first, second, or third intensity values at a corresponding location in the two-dimensional array of vectors. The method determines the remaining ones of the first, second, and third intensity values. The method starts by assigning a value to each one of the components of the vectors in the first two-dimensional array of vectors that is not determined by one of the scalars.

Abstract: This disclosure provides a system for classifying images, used in image detection, image recognition, or other computer vision. The system processes directory images to obtain eigenvectors and eigenvalues, and selects a set of “smooth” basis vectors formed by linear combinations of these eigenvectors to be applied against a target image. Contrary to conventional wisdom, however, a group of the eigenvectors having the weakest eigenvalues are used to select the basis vectors. A second process is then performed on this group of “weakest” eigenvectors to identify a set of candidate vectors, ordered in terms of “smoothness.” The set of basis vectors (preferably 3-9) is then chosen from the candidate vectors in order of smoothness, which are then applied in an image detection or image recognition process.

Abstract: This disclosure provides a system for classifying images, used in image detection, image recognition, or other computer vision. The system processes directory images to obtain eigenvectors and eigenvalues, and selects a set of “smooth” basis vectors formed by linear combinations of these eigenvectors to be applied against a target image. Contrary to conventional wisdom, however, a group of the eigenvectors having the weakest eigenvalues are used to select the basis vectors. A second process is then performed on this group of “weakest” eigenvectors to identify a set of candidate vectors, ordered in terms of “smoothness.” The set of basis vectors (preferably 3-9) is then chosen from the candidate vectors in order of smoothness, which are then applied in an image detection or image recognition process.

Abstract: A method for operating a data processing system to generate a full color image from a partially sampled version of the image. The full color image includes a first two-dimensional array of vectors having components representing the intensity of a pixel in the full color image in a corresponding spectral band at a location determined by the location of the vector in the first two-dimensional array. The method generates the first two-dimensional array from a two-dimensional array of scalars. Each scalar determines one of the first, second, or third intensity values at a corresponding location in the two-dimensional array of vectors. The method determines the remaining ones of the first, second, and third intensity values. The method starts by assigning a value to each one of the components of the vectors in the first two-dimensional array of vectors that is not determined by one of the scalars.

Abstract: A method for operating a data processing system to generate a full color image from a partially sampled version of the image. The full color image includes a first two-dimensional array of vectors having components representing the intensity of a pixel in the full color image in a corresponding spectral band at a location determined by the location of the vector in the first two-dimensional array. The method generates the first two-dimensional array from a two-dimensional array of scalars. Each scalar determines one of the first, second, or third intensity values at a corresponding location in the two-dimensional array of vectors. The method determines the remaining ones of the first, second, and third intensity values. The method starts by assigning a value to each one of the components of the vectors in the first two-dimensional array of vectors that is not determined by one of the scalars.