Abstract: An approach is disclosed that receives a set of descriptive material with logic that verifies whether a solution satisfies one or more problem constraints. The descriptive material also computes a value of an objective function that is achieved. The approach generates an output to input to an optimization engine. The output is based on analyzing the set of descriptive material. The approach then processes the output with the optimization engine with the processing resulting in a set of optimization results.

Abstract: Aspects of the present invention disclose a method for calculating a risk resulting from a network of networks that includes unknown relationships in a privacy preserving manner. The method includes one or more processors determining a set of conditions corresponding to a user of a network. The method further includes transmitting a compliance request corresponding to the set of conditions to one or more members of the network utilizing a privacy preserving algorithm. The method further includes determining a respective risk factor of one or more members of the network, wherein the respective risk factor corresponds to a response of each of the one or more members to the compliance request. The method further includes determining an overall risk of the network based at least in part on the risk factors of the one or more members.

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 terminal server of a virtual assistant system for proactively triggering notifications is disclosed. The terminal server is configured to: receive data indicative of a change of a service related state associated with a user of at least one terminal client; generate accordingly a close-ended type question; instruct a transmission of the close-ended type question to the at least one terminal client; in response to a retransmission request, received from the at least one terminal client in relation to the transmission: not perform the close-ended type question, access a storage of the service related state to generate accordingly a new close-ended type question, instruct a transmission of the new close-ended type question to the at least one terminal client, analyze a closed type answer provided by the at least one terminal client, and instruct transmission of a current response to the answer provided by the user.

Abstract: In an approach for computing trade-offs between privacy and accuracy of data analysis on building a learning model, a processor receives a dataset for training a model. The dataset includes one or more pre-identified sensitive data fields. The processor determines a weight of each sensitive data field for the model. The processor evaluates resource cost of applying a privacy preservation technique to the one or more pre-identified sensitive data fields. The processor identifies correlation among the sensitive data fields. The processor presents a comparison of options for training the model, in terms of tradeoffs of accuracy for training the model and the resource cost of the privacy preservation technique.

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: In some examples, a system for generating optimization constraints includes a memory device to store human-generated constraint and/or objective definitions that have been programmed in a general-purpose programming language by a human user, and a processor configured to generate labeled data for a plurality of solutions to an optimization problem using the stored constraint and/or objective definitions. The processor is also configured to generate a formal constraint and/or objective model from the labeled constraint and/or objective data, wherein the formal constraint and/or objective model comprises automatically generated constraint and/or objective definitions that are syntactically different from the human-generated constraint and/or objective definitions and syntactically correct for a specific optimization engine.

Abstract: Methods and systems for generating an environment include training transformer models from tabular data and relationship information about the training data. A directed acyclic graph is generated, that includes the transformer models as nodes. The directed acyclic graph is traversed to identify a subset of transformers that are combined in order. An environment is generated using the subset of transformers.

Abstract: In an approach for computing trade-offs between privacy and accuracy of data analysis on building a learning model, a processor receives a dataset for training a model. The dataset includes one or more pre-identified sensitive data fields. The processor determines a weight of each sensitive data field for the model. The processor evaluates resource cost of applying a privacy preservation technique to the one or more pre-identified sensitive data fields. The processor identifies correlation among the sensitive data fields. The processor presents a comparison of options for training the model, in terms of tradeoffs of accuracy for training the model and the resource cost of the privacy preservation technique.

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: Aspects of the present invention disclose a method for calculating a risk resulting from a network of networks that includes unknown relationships in a privacy preserving manner. The method includes one or more processors determining a set of conditions corresponding to a user of a network. The method further includes transmitting a compliance request corresponding to the set of conditions to one or more members of the network utilizing a privacy preserving algorithm. The method further includes determining a respective risk factor of one or more members of the network, wherein the respective risk factor corresponds to a response of each of the one or more members to the compliance request. The method further includes determining an overall risk of the network based at least in part on the risk factors of the one or more members.

Abstract: A method, system, and computer program product for learning parameters of Bayesian network using uncertain evidence, the method comprising: receiving input comprising graph representation and at least one sample of a Bayesian network, the graph comprising plurality of nodes representing random variables and plurality of directed edges representing conditional dependencies, wherein each of the at least one sample comprising for each node a value selected from the group consisting of: a known value; an unknown value; and an uncertain value; and applying on the input a Bayesian network learning process configured for calculating estimates of conditional probability tables of the Bayesian network using probabilities inferred by applying on the input a Bayesian network uncertain inference process configured for performing inference in a Bayesian network from uncertain evidence.

Abstract: A computer-implemented method, a computer program product, and a computer system for data processing. An embodiment includes providing a Bayesian network model including a special model structure. The embodiment further includes learning probabilities between at least one node of the Bayesian network model and a parent node of the Bayesian network model, wherein learning the probabilities is performed by assuming no special model structure is included in the Bayesian network model. The embodiment further includes optimizing parameters that describe learned probabilities of the Bayesian network model including the special model structure and updating the Bayesian network model including the special model structure using the optimized parameters.

Abstract: A terminal server of a virtual assistant system for proactively triggering notifications is disclosed. The terminal server is configured to: receive data indicative of a change of a service related state associated with a user of at least one terminal client; generate accordingly a close-ended type question; instruct a transmission of the close-ended type question to the at least one terminal client; in response to a retransmission request, received from the at least one terminal client in relation to the transmission: not perform the close-ended type question, access a storage of the service related state to generate accordingly a new close-ended type question, instruct a transmission of the new close-ended type question to the at least one terminal client, analyze a closed type answer provided by the at least one terminal client, and instruct transmission of a current response to the answer provided by the user.

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: According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving two or more predefined transition probability values of a Markov decision process (MDP) of a control system, where each of the predefined transition probability values is associated with a transition between a first state and a second state, both from two or more system states, resulting from execution of one or more control actions of the control system. The method comprises an action of computing one or more new transition probability values by an analysis of the predefined transition probability values, the system states and the control actions. The method comprises an action of determining one or more recommended control actions for the respective system state based on the new transition probability value.

Abstract: According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving measured transition probability values of a Markov decision process (MDP) and receiving simulated transition probability values generated by performing a control system simulation. New transition probability values are computed by calculating a measured data count of some of the sensor measurements and a simulated data count of some of the simulated transition data. New transition probability values are computed from a weighted average between the measured transition probability values and the simulated transition probability values using the measured data count and the simulated data count. A new control action is determined based on the one or more new transition probability value.

Abstract: According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving measured transition probability values of a Markov decision process (MDP) and receiving simulated transition probability values generated by performing a control system simulation. New transition probability values are computed by calculating a measured data count of some of the sensor measurements and a simulated data count of some of the simulated transition data. New transition probability values are computed from a weighted average between the measured transition probability values and the simulated transition probability values using the measured data count and the simulated data count. A new control action is determined based on the one or more new transition probability value.

Abstract: According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving two or more predefined transition probability values of a Markov decision process (MDP) of a control system, where each of the predefined transition probability values is associated with a transition between a first state and a second state, both from two or more system states, resulting from execution of one or more control actions of the control system. The method comprises an action of computing one or more new transition probability values by an analysis of the predefined transition probability values, the system states and the control actions. The method comprises an action of determining one or more recommended control actions for the respective system state based on the new transition probability value.

Abstract: A computerized method for scalable management optimization of pressurized water distribution networks, comprises receiving a network model representing a pressurized water distribution physical network having water flow variables. The network model is simulated by solving non-linear mathematical equations representing the behavior of the water flow variables. One or more result sets are fed to the non-linear mathematical equations. The non-linear mathematical equations are linearized. Network model optimization requirements are received from a user. The linearized mathematical equations are optimized according to the received network model optimization requirements. A local search starting from the at least one optimized solution is performed using the non-linearized mathematical equations, thereby generating a solution.