Abstract: The illustrative embodiments provide for supervision and reaction-aware adaptive intervention in an area. An embodiment includes detecting a behavior of a non-compliant entity over a threshold in a supervised area using one or more sensors. The threshold is determined by processing an input of the sensor using a first processing algorithm. The embodiment includes deploying a response into the supervised area. The response is based on input from the sensor. The embodiment includes identifying, using a second processing algorithm, a reaction of the non-compliant entity to the initial response. The embodiment includes determining fulfilment of a target state of the non-compliant entity using a third algorithm. The target state may include a change in behavior of the non-compliant entity. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the embodiment.

Abstract: Embodiments of the present invention provide an approach for automating a configuration of a server infrastructure for cloud applications by leveraging monitoring data from both the infrastructure and the applications that run on it. Specially, input information including an application which has been submitted is received along with a target dataset, a cloud provider and values for a specific performance measure. The application is mapped to a specific class and a performance model is selected based on the class. A set of resource configurations is generated and estimates of a target measure (e.g., run time) are provided for each configuration option using the selected model. A resource configuration option that provides either the best value of the measure or closest to the application objectives is selected and committed to an application deployment file.

Abstract: Systems and techniques that facilitate policy-driven views of datasets are provided. In various embodiments, a system can comprise a data component that can access a dataset. In various instances, the system can further comprise a view policy component that can respectively correlate a set of security credentials to a set of view instructions associated with the dataset. In various embodiments, the system can further comprise an input component that can receive as input a first security credential that is listed in the set of security credentials. In various aspects, the system can further comprise an output component that can identify a first view instruction that is listed in the set of view instructions associated with the dataset, wherein the first view instruction is respectively correlated to the first security credential. In various cases, the output component can generate as output a view of the dataset based on the first view instruction.

Abstract: Local memory and disaggregated memory may be identified and monitored for integrating disaggregated memory in a computing system. Candidate data may be migrated between the local memory and disaggregated memory to optimize allocation of disaggregated memory and migrated data according to a dynamic set of migration criteria.

Abstract: A request may be identified having one or more constraints for accessing disaggregated resources in a computing environment. One or more resources in a plurality of disaggregated resources may be identified based on the request. Computing server instances may be dynamically orchestrated using the one or more resources in the plurality of disaggregated resources based on the one or more constraints.

Abstract: Embodiments for processing data with multiple machine learning models are provided. Input data is received. The input data is caused to be evaluated by a first machine learning model to generate a first inference result. The first inference result is compared to at least one quality of service (QoS) parameter. Based on the comparison of the first inference result to the at least one QoS parameter, the input data is caused to be evaluated by a second machine learning model to generate a second inference result.

Abstract: A request may be identified having one or more constraints for accessing disaggregated resources in a computing environment. One or more resources in a plurality of disaggregated resources may be identified based on the request. Computing server instances may be dynamically orchestrated using the one or more resources in the plurality of disaggregated resources based on the one or more constraints.

Abstract: In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

Abstract: Local memory and disaggregated memory may be identified and monitored for integrating disaggregated memory in a computing system. Candidate data may be migrated between the local memory and disaggregated memory to optimize allocation of disaggregated memory and migrated data according to a dynamic set of migration criteria.

Abstract: Embodiments for an intelligent directing service in an Internet of Things (IoT) computing environment by a processor. One or more objects may be identified within a defined region relative to an entity. At least a portion of an extremity of the entity may be directed to select or avoid the one or more objects according to one or more internet of things (IoT) devices.

Abstract: Systems and techniques that facilitate policy-driven views of datasets are provided. In various embodiments, a system can comprise a data component that can access a dataset. In various instances, the system can further comprise a view policy component that can respectively correlate a set of security credentials to a set of view instructions associated with the dataset. In various embodiments, the system can further comprise an input component that can receive as input a first security credential that is listed in the set of security credentials. In various aspects, the system can further comprise an output component that can identify a first view instruction that is listed in the set of view instructions associated with the dataset, wherein the first view instruction is respectively correlated to the first security credential. In various cases, the output component can generate as output a view of the dataset based on the first view instruction.

Abstract: In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

Abstract: Embodiments for preventing data loss in a computing storage environment by a processor. One or more offline events in a locality failing to have continuous network connectivity may be detected. An ad-hoc distributed file system, having one or more edge devices associated with the locality, may be created for storing data produced during the one or more offline events.

Abstract: Embodiments for optimization of storage and network usage in an Internet of Things (IoT) environment by a processor. A data stream production rate for one or more applications may be dynamically adjusted based on current network conditions, a network status forecast, storage availability, network disruption patterns, quality of service (QoS) policies, application or network priorities, or a combination thereof to reduce data loss at a locality caused by network disruption in the IoT computing environment.

Abstract: Embodiments for processing data with multiple machine learning models are provided. Input data is received. The input data is caused to be evaluated by a first machine learning model to generate a first inference result. The first inference result is compared to at least one quality of service (QoS) parameter. Based on the comparison of the first inference result to the at least one QoS parameter, the input data is caused to be evaluated by a second machine learning model to generate a second inference result.

Abstract: Embodiments for implementing a fault tolerant color palette in a device having potential hardware faults by a processor. A fault tolerant color palette, assistive of user color interpretation for a display, may be generated according to a set of colors in a predetermined color palette and one or more user preferences.

Abstract: Embodiments for an intelligent directing service in an Internet of Things (IoT) computing environment by a processor. One or more objects may be identified within a defined region relative to an entity. At least a portion of an extremity of the entity may be directed to select or avoid the one or more objects according to one or more internet of things (IoT) devices.

Abstract: Embodiments for implementing a fault tolerant color palette in a device having potential hardware faults by a processor. A fault tolerant color palette, assistive of user color interpretation for a display, may be generated according to a set of colors in a predetermined color palette and one or more user preferences.

Abstract: Embodiments for preventing data loss in a computing storage environment by a processor. One or more offline events in a locality failing to have continuous network connectivity may be detected. An ad-hoc distributed file system, having one or more edge devices associated with the locality, may be created for storing data produced during the one or more offline events.

Abstract: Embodiments for optimization of storage and network usage in an Internet of Things (IoT) environment by a processor. A data stream production rate for one or more applications may be dynamically adjusted based on current network conditions, a network status forecast, storage availability, network disruption patterns, quality of service (QoS) policies, application or network priorities, or a combination thereof to reduce data loss at a locality caused by network disruption in the IoT computing environment.