Abstract: A system for generating a statistical model for fault diagnosis comprising at least one hardware processor, adapted to: extract a plurality of structured values, each associated with at least one of a plurality of semantic entities of a semantic model or at least one of a plurality of semantic relationships of the semantic model, from structured historical information organized in an identified structure and related to at least some of a plurality of historical events, the semantic model represents an ontology of an identified diagnosis domain, each of the plurality of semantic entities relates to at least one of a plurality of domain entities existing in the identified diagnosis domain, and each of the plurality of semantic relationships connects two of the plurality of semantic entities and represents a parent-child relationship therebetween; extract a plurality of unstructured values, each associated with at least one of the plurality of semantic entities.

Abstract: An example system includes a processor that can obtain a circuit describing operations of sequential secure computation code. The processor can modify the circuit based on a cost function. The processor can partition the circuit into a number of sub-circuits. The processor can assign the number of the sub-circuits to different processors for execution.

Abstract: An example system includes a processor to receive historical data, a formal quality measure, a quality threshold, and a mathematical optimization model. At least part of the mathematical optimization model is generated from the historical data. The processor can measure a quality of the mathematical optimization model using the formal quality measure. The processor can then augment the mathematical optimization model such that the measured quality of the augmented mathematical optimization model exceeds the target quality threshold.

Abstract: Aspects of the present invention disclose a method for managing the control policies associated with processing of a set of resources by a distribution system. The method includes one or more computer processors determining information associated with a configuration of a distribution system. The method further includes determining information related to a set of resource to process utilizing the distribution system. The method further includes determining respective values for a plurality of parameters related to processing the set of resources utilizing the distribution system. The method further includes selecting an initial set of control policies; The method further includes updating the initial set of control policies. The method further includes instructing the distribution system to process the set of resources utilizing the updated set of control policies.

Abstract: An example system includes a processor that can obtain a circuit describing operations of sequential secure computation code. The processor can modify the circuit based on a cost function. The processor can partition the circuit into a number of sub-circuits. The processor can assign the number of the sub-circuits to different processors for execution.

Abstract: An example system includes a processor to receive parameters of a fluid network control system. The processor can formulate an optimal control problem as proportions of server effort. The processor can also solve the optimal control problem using a robust counterpart of a separated continuous linear programming (SCLP). The processor can further adjust a parameter of the fluid network control system based on the optimal solution.

Abstract: Aspects of the present invention disclose a method for managing the control policies associated with processing of a set of resources by a distribution system. The method includes one or more computer processors determining information associated with a configuration of a distribution system. The method further includes determining information related to a set of resource to process utilizing the distribution system. The method further includes determining respective values for a plurality of parameters related to processing the set of resources utilizing the distribution system. The method further includes selecting an initial set of control policies; The method further includes updating the initial set of control policies. The method further includes instructing the distribution system to process the set of resources utilizing the updated set of control policies.

Abstract: An example system includes a processor to receive parameters of a fluid network control system. The processor can formulate an optimal control problem as proportions of server effort. The processor can also solve the optimal control problem using a robust counterpart of a separated continuous linear programming (SCLP). The processor can further adjust a parameter of the fluid network control system based on the optimal solution.

Abstract: A system and a method for increasing the classification confidence, with lesser dependence on large sets of training data, obtained by one or more machine learning based algorithms, by analyzing unstructured information using unstructured analysis pipeline comprising a probabilistic network such as a Bayesian network. The probabilistic network may comprise nodes associated with elements and cues defined by experts, and require fewer labelled data samples to train. The confidence level of the elements may be determined by machine learning and unstructured analysis methods and processed by the probabilistic network to estimate the confidence for a characterization quantity.

Abstract: In an approach, a processor stores a dictionary set, including simplex dictionaries, for saving processing time when calculating an optimal control policy for at least one linearly time dependent value function of a plurality of variables complying with a plurality of linearly time dependent constraints. A processor calculates a storage limit for the dictionary set, based on a number of the plurality of variables, the plurality of constraints, and size of a memory. A processor removes at least one of the plurality of simplex dictionaries in accordance with the storage limit, while maintaining a neighbor density measure, where the neighbor density measure is based on a distance between the at least one of the simplex dictionaries and a non-removed simplex dictionary and the distance corresponds to a number of simplex pivots required to construct the at least one of the simplex dictionaries from the non-removed simplex dictionary.

Abstract: A system for generating a statistical model for fault diagnosis comprising at least one hardware processor, adapted to: extract a plurality of structured values, each associated with at least one of a plurality of semantic entities of a semantic model or at least one of a plurality of semantic relationships of the semantic model, from structured historical information organized in an identified structure and related to at least some of a plurality of historical events, the semantic model represents an ontology of an identified diagnosis domain, each of the plurality of semantic entities relates to at least one of a plurality of domain entities existing in the identified diagnosis domain, and each of the plurality of semantic relationships connects two of the plurality of semantic entities and represents a parent-child relationship therebetween; extract a plurality of unstructured values, each associated with at least one of the plurality of semantic entities.

Abstract: A system for generating a statistical model for fault diagnosis comprising at least one hardware processor, adapted to: extract a plurality of structured values, each associated with at least one of a plurality of semantic entities of a semantic model or at least one of a plurality of semantic relationships of the semantic model, from structured historical information organized in an identified structure and related to at least some of a plurality of historical events, the semantic model represents an ontology of an identified diagnosis domain, each of the plurality of semantic entities relates to at least one of a plurality of domain entities existing in the identified diagnosis domain, and each of the plurality of semantic relationships connects two of the plurality of semantic entities and represents a parent-child relationship therebetween; extract a plurality of unstructured values, each associated with at least one of the plurality of semantic entities.

Abstract: A method for predicting at least one score for at least one item, comprising in at least one iteration of a plurality of iterations: receiving a user profile having a plurality of user attribute values; computing the at least one score according to a similarity between the user profile and a plurality of other user profiles by inputting the user profile and a plurality of items into a prediction model trained by: in each of a plurality of training iterations: receiving a training user profile of a plurality of training user profiles, the training user profile having a plurality of training user attribute values; computing by the prediction model a plurality of predicted scores, each for one of a plurality of training items, in response to the training user profile and the plurality of training items, where each of the plurality of training items has a plurality of training item.

Abstract: A system for generating a statistical model for fault diagnosis comprising at least one hardware processor, adapted to: extract a plurality of structured values, each associated with at least one of a plurality of semantic entities of a semantic model or at least one of a plurality of semantic relationships of the semantic model, from structured historical information organized in an identified structure and related to at least some of a plurality of historical events, the semantic model represents an ontology of an identified diagnosis domain, each of the plurality of semantic entities relates to at least one of a plurality of domain entities existing in the identified diagnosis domain, and each of the plurality of semantic relationships connects two of the plurality of semantic entities and represents a parent-child relationship therebetween; extract a plurality of unstructured values, each associated with at least one of the plurality of semantic entities.

Abstract: Method, system and product for decomposing a simulation model. The method comprising automatically decomposing the simulation model into a predetermined number of co-simulation components, wherein each co-simulation component is allocated to a different simulation platform, wherein said automatically decomposing comprises: defining a target optimization function, wherein the target optimization function computes an estimated run time of the simulation model, wherein the target optimization function is based on a communication time within each co-simulation component and a communication time between each pair of co-simulation components; and determining a decomposition of the simulation model that optimizes a value of the target optimization function. The method further comprises executing the decomposed simulation model by executing in parallel each co-simulation component on a different simulation platform, whereby the simulation model is executed in a distributed manner.

Abstract: A method, system, and product for simulation of Internet of Things (IoT) environment. The method performed by a simulation node in the IoT environment, which comprises the simulation node and a cloud server connected by a computerized network. The method comprises selecting a simulated IoT device to simulate from a plurality of simulated IoT devices that are being simulated by the simulation node; invoking a real-world model to obtain real-world simulated values; determining a simulated behavior of the selected simulated IoT device by invoking a device model and providing the real-world simulated values thereto, o wherein the simulated behavior comprises transmitting a message to the cloud server; setting a next simulated action of the simulation node to occur at a designated time, wherein the next simulated action is the simulated behavior; and performing the next simulated action at the designated time.

Abstract: Method, system and product for decomposing a simulation model. The method comprising automatically decomposing the simulation model into a predetermined number of co-simulation components, wherein each co-simulation component is allocated to a different simulation platform, wherein said automatically decomposing comprises: defining a target optimization function, wherein the target optimization function computes an estimated run time of the simulation model, wherein the target optimization function is based on a communication time within each co-simulation component and a communication time between each pair of co-simulation components; and determining a decomposition of the simulation model that optimizes a value of the target optimization function. The method further comprises executing the decomposed simulation model by executing in parallel each co-simulation component on a different simulation platform, whereby the simulation model is executed in a distributed manner.

Abstract: A method, system, and product for simulation of Internet of Things (IoT) environment. The method performed by a simulation node in the IoT environment, which comprises the simulation node and a cloud server connected by a computerized network. The method comprises selecting a simulated IoT device to simulate from a plurality of simulated IoT devices that are being simulated by the simulation node; invoking a real-world model to obtain real-world simulated values; determining a simulated behavior of the selected simulated IoT device by invoking a device model and providing the real-world simulated values thereto, o wherein the simulated behavior comprises transmitting a message to the cloud server; setting a next simulated action of the simulation node to occur at a designated time, wherein the next simulated action is the simulated behavior; and performing the next simulated action at the designated time.

Abstract: A computer implemented method of optimizing an execution of a process by applying adjustable robust optimization, comprising: 1) Designating an optimization function for calculating an optimal solution for a process execution comprising a plurality of ordered events. The process execution depends on a plurality of uncertain variables. 2) Identifying an adjustment which sets a value for one or more adjusted variables of the plurality of uncertain variables. The adjustment is derived from one or more decisions made by a user. The one or more decisions define an order of one or more ordered events to execute a particular realization of the process execution. 3) Applying the one or more adjusted variables to the optimization function which calculates the optimal solution. 4) Outputting the optimal solution to the user.