International Business Machines Corporation

The Weather Company, LLC

Alecio Pedro Delazari Binotto

Vagner Figueredo De Santana

Fernando Luiz Koch

Aladin Djuhera

Ana Paula Appel

Renato Luiz De Freitas Cunha

Sagar Tayal

Rob High

Mansura Habiba

Maja Curic

Jose Luis Spagnuolo Sobrinho

Lisa Seacat DeLuca

Carlos Henrique Cardonha

Ricardo Luis Ohta

Renato Fontoura De Gusmao Cerqueira

Eduardo Rocha Rodrigues

Ademir Ferreira Da Silva

Emilio Ashton Vital Brazil

Rodrigo Da Silva Ferreira

Viviane Torres Da Silva

Rachel Ostrand

Christian Van Maastricht

Percival Silva De Lucena

Joseph Michael Ziskovsky

David Jason Hunt

Michael Raghib Moreno

Swanand Ravindra Kadhe

Fabio Latuf Gandour

Bernardo Nunes Goncalves

Rafflesia Khan

Dwarkanath Pralhad Rao

Alexander Körner

Francisco Javier Garnica Alcazar

Samir Nassif Palma

Stefany Santos-Greiner

Farhan Ahmed

A computer-implemented method (CIM), according to one embodiment, includes performing a detecting and monitoring adversarial jamming (MDJ) process for an edge environment that includes a first edge device and a central hub. In response to a determination that a jamming attack event is likely to occur, a pre-emptive edge user device protection process (PPP) is caused to be deployed. In response to a determination that a jamming attack event has occurred, a post-process jammed data (PJD) process is caused to be deployed at a recipient of a wireless transmission subject to the jamming attack event. The CIM further includes generating, based on results of deploying the PPP and/or the PJD process, a first set of new parameters for a model of a device that sent the wireless transmission, and causing the first set of new parameters to be distributed to the model of the device that sent the wireless transmission.

Dynamically applied processes for mitigating adversarial jamming attack events

A method, computer system, and a computer program product for executing workloads in a distributed system is provided. The present invention may include: determining a current resource utilization status in a distributed system; inputting, to a second AI model, the current resource utilization status and an in-use split configuration of a first AI model, the second AI model configured for predicting a split configuration for the first AI model; receiving an output from the second AI, the output indicating a current split configuration for the first AI model; splitting the first AI model using the current split configuration; deploying the split first AI model such that the input and output blocks may be executed on first computer systems of the set of first computer systems and such that the intermediate block may be executed on a second computer system of the at least one second computer system; executing the workload.

AI model based deployment of an AI model

A computer-implemented method for creating a data transformation adapter for non-standardized data between a data producer unit and a data consumer unit in a data communication environment is disclosed. The method comprises analyzing first data specifications of the data producer unit, and analyzing second data specifications related to the data consumer unit. The method also comprises matching data attributes of the first specifications with data attributes of the second specifications, generating dynamically a plurality of data transformation adapters using the matched data attributes, selecting a verified adapter ensuring that the matched data attributes of the first specifications and the data attributes of the second specifications are transformed correctly, and generating a minimal data transformation child adapter from the verified adapter based on current dynamic performance requirements of a data consumer, and data attributes currently used by an application of the data consumer unit.

Create and optimize dynamic data adaptation layer in edge computing

A method, computer program product, and computer system are provided for geographic edge node data access control. The method carried out at an edge node includes: obtaining a regulation control template for controlling access to data on an edge node for a new regulation region; and adjusting active regulation controls of a set of operation parameters of the edge node based on the regulation control template for the new regulation region. The method also includes: inferring an impact of the adjustment of active regulation controls, wherein the impact is a variation of the operation parameters of the edge node; and, when an impact is negative, further adjusting active regulation controls based on alterative rules in the regulation control template. The method verifies a validity of the adjustment of active regulation controls for the set of operation parameters of the edge node.

Geographic edge node data access control

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors, collect information associated with a target edge platform. The one or more processors, generate a set of rules for a target edge platform for different situations that can occur on the target edge platform. The one or more processors in response to receiving additional information, dynamically adjust the generated set of rules for the target platform.

Real-time adjustment and deployment of regulatory policies

Embodiments of the invention provide a computer-implemented method that includes intercepting, using a controller of a programmable network (PN), a deployment of a control application in the PN. Responsive to intercepting the deployment of the control application, a user experience (UE) analysis is performed. The UE analysis includes determining, based at least in part on a set of digital twins, a UE impact of the control application; and, based at least in part on the UE impact, generating a UE-based control application deployment recommendation. The UE analysis further includes deploying the control application based at least in part on the UE-based control application deployment recommendation.

Identifying the user experience and SLA fulfillment impact of control applications prior to deployment

The present disclosure relates to a method comprising receiving a request to execute a workload using an artificial intelligence model. A current resource utilization status in the distributed system may be determined. The current resource utilization status may be used to define a deployment configuration of the artificial intelligence model, wherein the deployment configuration is defined by: a number and structure of input blocks, a number and structure of output blocks and the intermediate block of the artificial intelligence model, a second computer system to execute the intermediate block, and one or more first computer systems to execute the input and output blocks. The artificial intelligence model may be deployed in accordance with the defined deployment configuration and the workload may be executed.

Orchestration of workloads involving an AI model

The exemplary embodiments disclose a method, a computer program product, and a computer system for determining a personalized weather forecast. The exemplary embodiments may include collecting data of a user and weather conditions of a location, extracting one or more features from the collected data, and determining a personalized weather forecast of the location for the user based on the extracted one or more features and one or more models.

Personalized weather forecast

A method, computer system, and a computer program product for detecting face mask usage based on a crowd sound is provided. The present invention may include capturing an audio stream including a crowd voice data. The present invention may also include analyzing the crowd voice data using a machine learning model to determine an amount of people wearing masks. The present invention may further include in response to determining that the amount of people wearing masks does not meet a compliance threshold, displaying a content to promote face mask usage.

Acoustic analysis of crowd sounds

Techniques for data evaluation and optimized resource allocation are provided. Usage data is received collected from at least one sensor in a physical location, where the usage data indicates consumption of an item. The usage data is aggregated within a predefined cluster, where the predefined cluster corresponds to a geographic area. Future consumption of the item is predicted based at least in part on evaluating the usage data using one or more cognitive models. A recommended reconfiguration of one or more aspects of production for the item is generated, based on the predicted future consumption.

Cognitive evaluation of sensor environments and resource allocation

An example operation may include one or more of storing existing chaincode relationships of a group of blockchain peers within a blockchain network, identifying a new chaincode to implement for one or more blockchain peers among the group of blockchain peers of the blockchain network based on the existing chaincode relationships among the group of blockchain peers, and transmitting a message to the one or more blockchain peers with a suggestion to implement the new chaincode.

Chaincode recommendation based on existing chaincode

Techniques for maintaining a knowledge database system based on user interactions with the database system are described herein. An aspect includes generating a user interface based on a knowledge database comprising a plurality of entities, wherein the user interface comprises a plurality of user interface elements, each user interface element of the plurality of user interface elements corresponding to one or more of the plurality of entities of the knowledge database. Another aspect includes receiving a user interaction corresponding to the user interface. Another aspect includes updating the knowledge database based on the received user interaction, wherein updating the knowledge database based on the received user interaction includes determining a user interface element associated with the received user interaction, mapping the determined user interface element to a first entity of the plurality of entities of the knowledge database, and performing a weight update corresponding to the first entity.

Maintaining a knowledge database based on user interactions with a user interface

Dynamically changing a content based on a user-defined context includes receiving, by one or more processors, input data from a user, the input data includes at least one document with an annotated part identifying a first content element, the first content element is associated with a first content type. The one or more processors determine a context information associated with the annotated part and extract the annotated part. A first replacement for the first content element is retrieved from a first data source selected based on the content information. The one or more processors replace the first content element in the at least one document with the first replacement.

Automated content modification based on a user-specified context

A method of managing livestock includes selecting an area of interest (AOI) from among a plurality of AOIs as a target AOI, defining a route along which livestock is to be guided to the target AOI, guiding the livestock to the target AOI via the defined route using an unmanned aerial vehicle (UAV), identifying a stress condition of the livestock using the UAV, and reducing the stress condition of the livestock using the UAV. Identifying the stress condition includes identifying an animal not belonging to the livestock causing stress in at least one animal belonging to the livestock. Reducing the stress condition includes diverting the identified animal not belonging to the livestock away from the livestock by adjusting the distance of the UAV relative to the identified animal.

Livestock management

Resource usage is monitored for a hybrid cloud computing environment having on-premises computing resources and off-premises computing resources, for at least a first time period. For the time period, the amount of the on-premises computing resources and off-premises computing resources are related to the demand for computing services from the environment. Future demand for computing services from the environment is dynamically predicted based on the relating step and a learned function of the demand and/or a projection of future business growth for a business utilizing the on-premises and off-premises computing resources. Based on the predicted future demand, the amount of the on-premises computing resources is dynamically optimized for a future time period, so as to minimize costs for the future time period; and new amounts of on-premises computing resources and off-premises computing resources are provisioned for the future time period, in accordance with the optimizing step.

Growing community of inventors

Munich, Germany

Alecio Pedro Delazari Binotto