Abstract: 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.

Abstract: In an approach to coordinated emotion representation and expression in multi-agent digital assistants, one or more computer processors monitor a conversation of a first user. One or more computer processors determine an intervention opportunity is present in the conversation. One or more computer processors retrieve a profile associated with the first user. One or more computer processors determine a context of the conversation. Based on the profile associated with the first user and the context of the conversation, one or more computer processors generate one or more emotion-based responses. One or more computer processors determine a most appropriate response of the one or more emotion-based responses. One or more computer processors present the most appropriate response to the first user.

Abstract: 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.

Abstract: A computer implemented method of deployment and resource allocation of microservices of a distributed computing environment is disclosed. The distributed computing environment includes a microservice deployment scheduler and one or more computing nodes. The microservice deployment scheduler includes a reinforcement learning based dynamic workload orchestration module. The method includes receiving microservice constraints descriptive of a microservice computing task by the microservice deployment scheduler. The method further includes receiving node specific properties from the one or more computing nodes by the microservice deployment scheduler. The node specific properties are descriptive of a computing capacity and/or computing capabilities of the one or more computing nodes.

Abstract: 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.

Abstract: A computer-implemented method, according to one embodiment, includes determining, based on capacity profiling information and descriptions associated with a plurality of initial edge devices, a plurality of possible AI model architectures that fulfill memory constraints of the initial edge devices, and generating a pareto-optimal configuration curve based on a sub-set of the possible AI model architectures. In response to receiving a request for determining an AI model architecture for a first edge device, the pareto-optimal configuration curve is used to determine one of the possible AI model architectures with a relatively highest degree of adherence to constraints of the first edge device. The method further includes causing the determined AI model architecture to be deployed to the first edge device.

Abstract: Aspects of the present invention, determine, by a specific first computer system of the first computer systems, a need to fine-tune a first artificial intelligence model for performing a specific task; perform a federated learning for a set of trained second artificial intelligence models for generating a combined artificial intelligence model, the second artificial intelligence models being configured to perform the specific task, each second artificial intelligence model having a structure which is at least a substructure of the first artificial intelligence model; and use learnable parameters of the combined artificial intelligence model for fine-tuning at the specific first computer system the first artificial intelligence model.

Abstract: 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.

Abstract: 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.

Abstract: An embodiment senses an object from an object screening system, and responsive to the sensed object, generates a context information based on the sensed object. The embodiment generates a cultural classification by a Culturally Aware Objects Model based in part on the context information wherein the Culturally Aware Objects Model is trained on a cultural factor of an object. The embodiment generates a moderated context information by a Sensitive Information Moderation Model based in part on the cultural classification and the context information wherein the Sensitive Information Moderation Model is trained on a privacy parameter. The embodiment also computes a recommendation by a Recommendation Component based in part on the moderated context information wherein the object screening system is updated with the recommendation.

Abstract: An embodiment senses an interaction among a software agent and a plurality of humans, responsive to the sensed interaction, computes a mood pattern in a Mood Pattern Observation Component based on the sensed interaction. The embodiment computes a prevalent mood pattern in a Mood Pattern Grouping Component based on the mood pattern. The embodiment decides by an Orchestration of Agent Interactions Component based on the prevalent mood pattern to adapt a response of the software agent to influence at least one of the plurality of humans towards the prevalent mood pattern, the deciding further comprises training a Text Generation Component to generate a text sequence based on the response wherein the software agent emits the text sequence and the sensed interaction among the software agent and the plurality of humans is updated with the text sequence.

Abstract: 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.

Abstract: The present disclosure relates to a method for executing an artificial intelligence model, including receiving an input for execution of the AI model. An input block of the AI model can be executed by a first computer system using the received input, producing a first output. The first output can be encoded. The encoded first output can be sent to a second computer system. The second computer system can decode the encoded first output. The second computer system can execute an intermediate block of the AI model using the first output, producing a second output. The second output can be encoded. The encoded second output can be sent to the first computer system. The first computer system can decode the encoded second output. The first computer system can execute an output block of the ML model using as input the second output, producing a result output.

Abstract: A computer-implemented method, according to one embodiment, includes causing a client DNS resolution request to be stored to a predetermined query caching database, and receiving a request from a firewall to determine a validity of a connection request received by the firewall from a client device. The connection request is for a resolved IP address associated with the client DNS resolution request. Contents of the predetermined query caching database are used to determine the validity of the connection request. The method further includes using the determined validity results to control whether the client device is allowed to connect to the resolved IP address. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: Methods, computer program products, and systems are presented.

Abstract: Aspects of the invention include a computer-implemented method, including performing simulations of a form of cyber-attack based on different input parameters to determine a respective time to perform each cyber-attack on a plurality of features of a sensor-based device. Additionally, performing simulations of a plurality of mitigating processes for each cyber-attack based on different input parameters to determine a respective time to perform each mitigating process. An associated risk level of each cyber-attack is determined based at least in part on the simulations. A mitigation process is selected based at least in part on the associated risk levels.

Abstract: Aspects of the invention include a computer-implemented method, which includes retrieving an installation protocol for a sensor-based device. Receiving negative feedback data representing negative feedback about directions for a step in the installation protocol. Performing an installation analysis of an installation of the sensor-based device. Amending the directions, based at least in part on the installation analysis.

Abstract: Aspects of the invention include a computer-implemented method, which includes retrieving an installation protocol for a sensor-based device. Receiving negative feedback data representing negative feedback about directions for a step in the installation protocol. Performing an installation analysis of an installation of the sensor-based device. Amending the directions, based at least in part on the installation analysis.

Abstract: Aspects of the invention include a computer-implemented method, including performing simulations of a form of cyber-attack based on different input parameters to determine a respective time to perform each cyber-attack on a plurality of features of a sensor-based device. Additionally, performing simulations of a plurality of mitigating processes for each cyber-attack based on different input parameters to determine a respective time to perform each mitigating process. An associated risk level of each cyber-attack is determined based at least in part on the simulations. A mitigation process is selected based at least in part on the associated risk levels.

Abstract: Aspects of the invention include a computer-implemented method that includes detecting, by a processor, a performance of a step of an installation protocol for a sensor-based device. The processor performs a negative outcome analysis comprising determining a likelihood that the performance of the step will cause a negative outcome. Based at least in part on a result of the negative outcome analysis, an interruption alarm is issued.