Abstract: Disclosed herein are methods and systems for executing verifiable computation modules to process private data at private data owner platform, comprising obtaining a computation module having a unique identifier recorded in a distributed ledger controlled by a plurality of computing nodes, generating a key pair comprising a signing key and a verification key derived from the signing key, recording, in the distributed ledger, an execution record associating an execution instance of the computation module with the verification key, initiating the execution instance of the computation module to process a private dataset incorporated with the signing key. outputting a computation outcome, computing an execution result signature for the execution instance based on the unique identifier and the private data and the signing key, and recording the execution result signature in the distributed ledger to enable verification of the execution instance.

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: 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 processor may assign an indicator to each participant in a blockchain network. The indicator may respectively indicate a role of each participant. The processor may transform the indicator of each participant into respective scores. The respective scores may be directly associated with the role of each participant. The processor may aggregate the respective scores into an endorsement score. The processor may determine if the endorsement score exceeds an endorsement threshold.

Abstract: Disclosed herein are methods and systems for executing verifiable computation modules to process private data at private data owner platform, comprising obtaining a computation module having a unique identifier recorded in a distributed ledger controlled by a plurality of computing nodes, generating a key pair comprising a signing key and a verification key derived from the signing key, recording, in the distributed ledger, an execution record associating an execution instance of the computation module with the verification key, initiating the execution instance of the computation module to process a private dataset incorporated with the signing key. outputting a computation outcome, computing an execution result signature for the execution instance based on the unique identifier and the private data and the signing key, and recording the execution result signature in the distributed ledger to enable verification of the execution instance.

Abstract: A processor may assign an indicator to each participant in a blockchain network. The indicator may respectively indicate a role of each participant. The processor may transform the indicator of each participant into respective scores. The respective scores may be directly associated with the role of each participant. The processor may aggregate the respective scores into an endorsement score. The processor may determine if the endorsement score exceeds an endorsement threshold.

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: 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: Embodiments of the present systems and methods may provide quality-driven incentives for continuous data maintenance and fair value-exchange in business eco-systems that may ensure high quality data over time. For example, a method of data management processing may be implemented in a computer system and the method may comprise determining a quality of data submitted by a data provider over a plurality of quality dimensions, curating the submitted data over multiple data providers based on the plurality of quality dimensions, and rewarding the data provider based on the determined quality of the data submitted by the data provider, an expected lifetime of the data, and the data consumers that access the data so as to incentivize the data provider to provide the data and to maintain or improve the quality of the data.

Abstract: A system for executing a code for training an insurance response system, comprising at least one hardware processor adapted to: using a plurality of insurance attribute values, extracted from a received insurance claim to retrieve: at least one inquiry related to the insurance claim and at least one inquiry result, and historical insurance data related to the insurance claim; computing a similarity value indicative of a difference between the at least one inquiry result and a claim result; identifying in the historical insurance data a plurality of insurance conditions relevant to the claim result and the at least one inquiry result; identifying at least one insurance condition effecting the similarity value by analyzing data comprising the at least one inquiry and the plurality of insurance conditions; and training a protocol model of an insurance response system using the at least one insurance condition and the at least one inquiry.

Abstract: In an example computer-implemented method, unstructured interactions between an unstructured conversation bot and a plurality of users are logged. A process flow model is generated based on the logged unstructured interactions. Instructions based on the process flow model are presented to a user in real time via the conversation bot.

Abstract: In an example computer-implemented method, unstructured interactions between an unstructured conversation bot and a plurality of users are logged. A process flow model is generated based on the logged unstructured interactions. Instructions based on the process flow model are presented to a user in real time via the conversation bot.

Abstract: An example system includes a processor to receive temperature data from a plurality of sensors disposed at locations more sensitive to temperature change in a plurality of server racks. The processor is to also generate a statistical model comprising a continuous spatial statistical distribution of temperature values based on the temperature data. The processor is to further generate a pseudo heat map based on the statistical model.

Abstract: A computer-implemented method, computerized apparatus and computer program product for efficient solution of nearest object problems. A set of objects having non-point shapes representable by one or more geometric elements are obtained. For each object in the set the one or more geometric elements are sampled to obtain a set of points representing the set of objects. A Voronoi diagram is constructed for the set of points, whereby a subdivision of a space accommodating the set of objects into a plurality of cells, each cell containing a single point of the set of points and defining a region of closest neighbors of the point, is obtained. Cells containing points representing the same object are aggregated into a unified cell defining an estimated region of closest neighbors of the object.

Abstract: A system for executing a code for training an insurance response system, comprising at least one hardware processor adapted to: using a plurality of insurance attribute values, extracted from a received insurance claim to retrieve: at least one inquiry related to the insurance claim and at least one inquiry result, and historical insurance data related to the insurance claim; computing a similarity value indicative of a difference between the at least one inquiry result and a claim result; identifying in the historical insurance data a plurality of insurance conditions relevant to the claim result and the at least one inquiry result; identifying at least one insurance condition effecting the similarity value by analyzing data comprising the at least one inquiry and the plurality of insurance conditions; and training a protocol model of an insurance response system using the at least one insurance condition and the at least one inquiry.

Abstract: According to one embodiment, a method for solving a plurality of spatiotemporal planning problems. The method may include creating a domain model. The method may also include assigning a solution bound associated with the created domain model to infinity. The method may further include calculating a solution to the created domain model based on the assigned solution bound using at least one of a best-first search algorithm. The method may also include updating the assigned solution bound based on the calculated solution. The method may further include presenting the calculated solution to a user based on the updated solution bound not equaling infinity.

Abstract: A method for natural language instantiation of a planning problem includes receiving, by a computer, a natural language representation of a user's schedule, the user's schedule including a plurality of activities, associating a plurality of biomedical parameters of the user with execution of the plurality of activities to determine an effect of each activity on the plurality of biomedical parameters, based on the association of the plurality of biomedical parameters with the execution of the plurality of activities and a predefined evaluation criteria, calculating a quality score for the user's schedule, optimizing the user's schedule by performing a local search, based on a generic ontology, automatically constructing an ontology including information associated with the plurality of activities and the plurality of biomedical parameters of the user, and based on the constructed ontology, processing the natural language representation of the user's schedule.

Abstract: A computer-implemented method, computerized apparatus and computer program product for efficient solution of nearest object problems. A set of objects having non-point shapes representable by one or more geometric elements are obtained. For each object in the set the one or more geometric elements are sampled to obtain a set of points representing the set of objects. A Voronoi diagram is constructed for the set of points, whereby a subdivision of a space accommodating the set of objects into a plurality of cells, each cell containing a single point of the set of points and defining a region of closest neighbors of the point, is obtained. Cells containing points representing the same object are aggregated into a unified cell defining an estimated region of closest neighbors of the object.

Abstract: A computer-implemented method, computerized apparatus and computer program product for efficient solution of nearest object problems. A set of objects having non-point shapes representable by one or more geometric elements are obtained. For each object in the set the one or more geometric elements are sampled to obtain a set of points representing the set of objects. A Voronoi diagram is constructed for the set of points, whereby a subdivision of a space accommodating the set of objects into a plurality of cells, each cell containing a single point of the set of points and defining a region of closest neighbors of the point, is obtained. Cells containing points representing the same object are aggregated into a unified cell defining an estimated region of closest neighbors of the object.

Abstract: According to an aspect of some embodiments of the present invention there is provided a computerized method of analyzing code of a software program for dominance relationships between a plurality of functions of the software program, the method comprising: receiving source code of a software program, the source code having a plurality of functions; identifying a plurality of intraprocedural dominator graphs each for another of the plurality of functions; combining the plurality of intraprocedural dominator graphs to create an interprocedural dominance graph with edges that logically connect between nodes of the plurality of functions; identifying a plurality of interprocedural dominance relations between nodes in different functions of the plurality of functions using the interprocedural dominance graph; and analyzing the software program according to the plurality of interprocedural dominance relations.