International Business Machines Corporation

Frank Liu

Eli M Dow

Campbell D Watson

Bishop Chapman Brock

Thomas S Hubregtsen

Ernesto Arandia

Inseok Hwang

Jinho Lee

Minsik Cho

Fearghal O'Donncha

Fearghal O'donncha

Cheng-Wei Yu

Vincent Moriarty

A method, computer program product and computer system to generate safety alerts is provided. A processor retrieves a plurality of measurements associated with a location. A processor determines a set of features based on the plurality of measurements. A processor identifies a set of membership functions for the set of features. A processor determines a safety index for the body of water based on the set of membership functions and one or more input value ranges for the set of features. In response to the safety index being above a threshold value, a processor sends an alert to one or more users regarding the location.

Alert system with machine learning and fuzzy logic

An example operation may include one or more of receiving an example in a blockchain network, distributing the example to a plurality of endorsing peers of the blockchain network, performing, by one or more of the endorsing peers, automated analysis of the example to determine an inference for the example, determining if there is a consensus of inference amongst the plurality of endorsing peers, and committing the example to a blockchain of the blockchain network when there is a consensus of inference.

Verified permissioned blockchains

A method and system for outputting a state of a physical system using a calibrated model of the physical system, where the calibrated model is used to generate a model prediction. The system includes a plurality of sensors connected to a routing node are used to monitor measured data of the physical system. A first sensor of the plurality of sensors includes a logic module configured to determine an uncertainty quantification, and to combine the uncertainty quantification with the model prediction to output the state of the physical system.

Integration of physical sensors in a data assimilation framework

Methods, systems and computer program products for determining an accurate flow rate based on limited observations are provided. Aspects include receiving a limited sample of flow rate data associated with a test site of a test river. Aspects also include determining topographical characteristics of the test site. Aspects also include determining a plurality of similar tested sites and generating a test site rating curve based on a combination of the limited sample of flow rate data and sufficient flow rate data associated with one or more of the plurality of similar tested sites. Aspects also include obtaining a stage reading associated with the test site from a sensor disposed at the test site. Aspects also include determining a flow rate of the test site based on the stage reading and the test site rating curve.

Flow rate determination based on limited observations

Aspects of the present disclosure provide a computer-implemented method that includes providing a layered index to variable length data, the layered index comprising a plurality of layers. Each layer of the plurality of layers has an index array, a block offset array, and a per-block size array. The index array identifies a next level index of a plurality of indices or data. The indices represent a delta value from a first index of a block. The block offset array identifies a starting location of the index array. The per-block array identifies a shared integer size of a block of indices. The method further includes performing a random access read of the variable length data using the layered index.

Providing random access to variable-length data

Authenticity of Artificial Intelligence (AI) results may be verified by creating, for an AI system, from a plurality of original inputs to form a plurality of original inference results, a plurality of original signatures of representative elements of an internal state of the AI system constructed from each individual original inference result of the plurality of original inference results. During deployment of the AI system, a matching of a plurality of deployment time inference results with a plurality of deployment time signatures, to the plurality of original signatures and the plurality of original inference results, may be verified.

Deployment verification of authenticity of machine learning results

A computer-implemented method comprises: inputting into an autoencoder sets of input samples, each of the sets of input samples comprising: a reference input sample of a reference dataset and one or more target input samples of one or more target datasets, the autoencoder being trained using the reference dataset. The autoencoder generates a respective set of outputs for each set of the input samples to thereby form one or more respective sets of outputs, each of the one or more sets of outputs comprising the reference output and the one or more target outputs for a respective set of input samples; and determining the similarity of each of the one or more target datasets to the reference dataset by comparing each of the one or more target outputs to respective target input samples of each of the sets of input samples.

Method to measure similarity of datasets for given AI task

A simulation model recovery method, system, and computer program product include initiating a simulation model, during an operation of a model, periodically writing a solution space of the model to a checkpoint restart file, during an operation of the model, periodically writing diagnostic information on model progression to a log file, detecting a failure of the model, based on the log of the model, determining a time of the failure, based on the model outputs and restart files, determining a period of a numerical instability preceding the failure, selecting a checkpoint of the model preceding the period of the numerical instability, based on the numerical instability and diagnostic information in log files, modifying a configuration of the model, and restarting the model based on the selected checkpoint and the modified configuration.

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Austin, TX, United States of America

Frank Liu