Abstract: Described embodiments include a system that includes a monitoring agent, configured to automatically monitor usage of a computing device by a user, and a processor. The processor is configured to compute, based on the monitoring, a score indicative of a cyber-security awareness of the user, and to generate an output indicative of the score.

Abstract: When using Web intelligence (“Webint”) to collect information regarding a target social network user, one of the most valuable pieces of information is the target user's List-Of-Friends (LOF). In some cases, however, the LOF of the target user is not accessible in his profile. Herein are described methods and systems for identifying the LOF of a target user. An analysis system crawls the profiles of social network users, other than the target user, and reconstructs the LOF of the target user from the crawled profiles.

Abstract: When using Web intelligence (“Webint”) to collect information regarding a target social network user, one of the most valuable pieces of information is the target user's List-Of-Friends (LOF). In some cases, however, the LOF of the target user is not accessible in his profile. Herein are described methods and systems for identifying the LOF of a target user. An analysis system crawls the profiles of social network users, other than the target user, and reconstructs the LOF of the target user from the crawled profiles.

Abstract: Described embodiments include a system that includes a monitoring agent, configured to automatically monitor usage of a computing device by a user, and a processor. The processor is configured to compute, based on the monitoring, a score indicative of a cyber-security awareness of the user, and to generate an output indicative of the score.

Abstract: A method for generating synthetic data records which include datasets that capture state-based transitions, according to which a state transition family is randomly selecting, according to the distribution of samples between the different clusters of users and the context variables are randomly sampled according to their distribution within the chosen cluster. The relevant Markov Chains models are selected according to the sampled context and the initial state of the sequence is randomly selected according to the distribution of states. A random walk process is initialized on the graph models and the random walk is performed process on each context separately, assuming context independency. The cause condition of the current transition is sampled for each state transition, based on the distributions on the selected edge.

Abstract: Described embodiments include a system that includes a monitoring agent, configured to automatically monitor usage of a computing device by a user, and a processor. The processor is configured to compute, based on the monitoring, a score indicative of a cyber-security awareness of the user, and to generate an output indicative of the score.

Abstract: When using Web intelligence (“Webint”) to collect information regarding a target social network user, one of the most valuable pieces of information is the target user's List-Of-Friends (LOF). In some cases, however, the LOF of the target user is not accessible in his profile. Herein are described methods and systems for identifying the LOF of a target user. An analysis system crawls the profiles of social network users, other than the target user, and reconstructs the LOF of the target user from the crawled profiles.

Abstract: A method for detecting and diagnosing sensor faults in an autonomous system that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined. Predefined suspicious patterns are identified by online and continuously tracking the data readings from each sensor and detecting correlation breaks over time. The readings from sensors that match at least one of the patterns are marked as uncertain. For each online reading of the sensors, whenever sensors that used to be correlated show a different behavior, reporting that the reading indicates a fault. Upon identifying fault detection, diagnosing which of the internal components or sensors caused the fault, based on a function that returns the state of the sensor which is associated with the fault detection.

Abstract: When using Web intelligence (“Webint”) to collect information regarding a target social network user, one of the most valuable pieces of information is the target user's List-Of-Friends (LOF). In some cases, however, the LOF of the target user is not accessible in his profile. Herein are described methods and systems for identifying the LOF of a target user. An analysis system crawls the profiles of social network users, other than the target user, and reconstructs the LOF of the target user from the crawled profiles.

Abstract: Described embodiments include a system that includes a monitoring agent, configured to automatically monitor usage of a computing device by a user, and a processor. The processor is configured to compute, based on the monitoring, a score indicative of a cyber-security awareness of the user, and to generate an output indicative of the score.

Abstract: A method for detecting and diagnosing sensor faults in an autonomous system that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined. Predefined suspicious patterns are identified by online and continuously tracking the data readings from each sensor and detecting correlation breaks over time. The readings from sensors that match at least one of the patterns are marked as uncertain. For each online reading of the sensors, whenever sensors that used to be correlated show a different behavior, reporting that the reading indicates a fault. Upon identifying fault detection, diagnosing which of the internal components or sensors caused the fault, based on a function that returns the state of the sensor which is associated with the fault detection.

Abstract: A system for generating fabricated pattern data records (XDRs) based on data from accessible data sources, which comprises an XDR core module containing one or more modeling and pattern creation modules for modeling original data received from the data sources; one or more synthetic data generation modules for generating fabricated data, based on the patterns created by the modeling and pattern creation modules; a data splitting module for splitting the data into training and testing sets according to a predetermined policy; an XDR storage database for storing created patterns and fabricated data; a configuration manager for controlling the operation of the modeling and pattern creation modules and of the synthetic data generation modules; a plurality of XDR agents being software components for communicating with the data sources and accessing relevant data, using a unique API of each data source.

Abstract: A method for generating synthetic data records which include datasets that capture state-based transitions, according to which a state transition family is randomly selecting, according to the distribution of samples between the different clusters of users and the context variables are randomly sampled according to their distribution within the chosen cluster. The relevant Markov Chains models are selected according to the sampled context and the initial state of the sequence is randomly selected according to the distribution of states. A random walk process is initialized on the graph models and the random walk is performed process on each context separately, assuming context independency. The cause condition of the current transition is sampled for each state transition, based on the distributions on the selected edge.

Abstract: A method of enabling a specific mobile communication service provider (Dt) to estimate his market share on a street level in real time during a specific period of time over a particular point of interest (POI) is disclosed.

Abstract: A recommender system for recommending items to a user based on geo-Tagged information related to him, in which items associated with a GeoTag are stored in a database. Feedback regarding the various items is obtained from the user and the provided rating of items is propagated to closely located items based on their associated GeoTags. A user-to-user similarity matrix is calculated and a predicted score is assigned for each user and item, using a recommendation server. All the items in the catalog of items are sorted according to their predicted scores as calculated for the user, and all items that have been already rated by the user are filtered out. Then, items from the catalog of items are presented to the user, according to their scores.

Abstract: A recommender system for recommending items to a user based on geo-Tagged information related to him, in which items associated with a GeoTag are stored in a database. Feedback regarding the various items is obtained from the user and the provided rating of items is propagated to closely located items based on their associated GeoTags. A user-to-user similarity matrix is calculated and a predicted score is assigned for each user and item, using a recommendation server. All the items in the catalog of items are sorted according to their predicted scores as calculated for the user, and all items that have been already rated by the user are filtered out. Then, items from the catalog of items are presented to the user, according to their scores.

Abstract: A recommender system for recommending items to a user based on geo-Tagged information related to him, in which items associated with a GeoTag are stored in a database. Feedback regarding the various items is obtained from the user and the provided rating of items is propagated to closely located items based on their associated GeoTags. A user-to-user similarity matrix is calculated and a predicted score is assigned for each user and item, using a recommendation server. All the items in the catalog of items are sorted according to their predicted scores as calculated for the user, and all items that have been already rated by the user are filtered out. Then, items from the catalog of items are presented to the user, according to their scores.

Abstract: A method for improving stacking schema for classification tasks, according to which predictive models are built, based on stacked-generalization meta-classifiers. Classifications are combined to build a new scheme from at least two layers and multiclass classification problems are converted into binary classification problems. One-against-all class binarization and regression learners are used for each class model and ensemble classifiers are improved using stacking. Accuracy differences, accuracy ratio, and runtime classification in multiclass datasets are also improved and the class of a value is then predicted.

Abstract: A hybrid recommender system, in which the initial stereotype is manually defined by an expert and an affinity vector of stereotypes relating to each specific user who registers onto the system, is created to define a specific profile for each user. Recommendations for a specific user are generated according to the initial stereotype and the affinity vector of stereotypes. A binary feedback, from user regarding specific items picked by him is received (e.g., while of the item), which can be either positive or negative. Then the affinity vector of stereotypes is updated.

Abstract: a method for improving stacking schema for classification tasks, according to which predictive models are built, based on stacked-generalization meta-classifiers. Classifications are combined to build a new scheme from at least two layers and multiclass classification problems are converted into binary classification problems. One-against-all class binarization and regression learners are used for each class model and ensemble classifiers are improved using stacking. Accuracy differences, accuracy ratio, and runtime classification in multiclass datasets are also improved and the class of a value is then predicted.