Abstract: A provenance method, system, and non-transitory computer readable medium for a plurality of eidetic systems having logs, include crawling the logs of each node of a plurality of nodes of the eidetic systems to cluster segments across the logs of temporally correlated events into clustered segments and analyzing the correlated segments to interleave an order of processes in the logs and assign a probability to the order of the processes occurring.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: A network function optimization method, system, and computer program product include optimizing network function chain components of a software by modifying a structure of the network function chain components by removing a function of the network function chain components.

Abstract: A training process of a machine learning model is executed at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset and a global model parameter. A resource parameter set indicative of resources available at the edge node is estimated. The model parameter and the resource parameter set are sent to a synchronization node. Updates to the global model parameter and the number of iterations are received from the synchronization node based at least in part on the model parameter and the resource parameter set of edge nodes. The training process of the machine learning model is repeated at the edge node to determine an update to the model parameter based at least in part on the local dataset and updates to the global model parameter and the number of iterations from the synchronization node.

Abstract: Examples of computing task scheduling based on an intrusiveness metric are described. In an example, an intrusiveness metric that indicates an impact of a computing task on performance of a computing device may be determined with an intrusiveness machine learning model. The intrusiveness metric may be sent to a scheduler device to determine distribution of additional computing tasks according to a scheduling machine learning model.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: Systems and methods are described herein to orchestrate the execution of an application, such as a machine learning or artificial intelligence application, using distributed compute clusters with heterogeneous compute resources. A discovery subsystem may identify the different compute resources of each compute cluster. The application is divided into a plurality of workloads with each workload associated with resource demands corresponding to the compute resources of one of the compute clusters. Adaptive modeling allows for hyperparameters to be defined for each workload based on the compute resources associated with the compute cluster to which each respective workload is assigned and the associated dataset.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: According to examples, an apparatus may include a processor and a non-transitory computer readable medium on which is stored instructions that the processor may execute to determine whether to modify a transmission rate at which time series data is transmitted to a remote computer. The determination is based on a prediction of the time series data.

Abstract: Examples of scheduling of a cyber-physical system (CPS) process through a utility function are described. In an example, a plurality of computing resources to perform a process for the CPS may be evaluated based on a utility function. A first computing resource may be onboard the CPS and a second computing resource may be a remote computing resource. A computing resource that optimizes the utility function may be scheduled to perform the process for the CPS.

Abstract: For each of a number of workloads, time intervals within execution performance information that was collected during execution of the workload on a first hardware platform are correlated with corresponding time intervals within execution performance information that was collected during execution of the workload on a second hardware platform. For a workload, the time intervals within the execution performance information on the second hardware platform are correlated to the time intervals within the execution performance information the first hardware platform during which the same parts of the workload were executed. A machine learning model that outputs predicted performance on the second hardware platform relative to known performance on the first hardware platform is trained. The model is trained from the correlated time intervals within the execution performance information for each workload on the hardware platforms.

Abstract: A meeting summarization method, system, and computer program product, include capturing notes of a user including a time stamp from the user associated with a meeting, synchronizing an agenda of the meeting and the notes of the user based on a correlation between a time stamp of a topic on the agenda and a time stamp of the notes of the user, and analyzing the synchronized topic and the notes to determine highlights of the meeting based on a co-occurrence of the time stamp of the notes of the user and the time stamp of the topic on the agenda.

Abstract: A meeting summarization method, system, and computer program product, include compiling notes from a meeting between a plurality of users and providing a single document that summarizes the meeting based on the compiled notes.

Abstract: A network function optimization method, system, and computer program product include optimizing network function chain components of a software by modifying a structure of the network function chain components by removing a function of the network function chain components.

Abstract: A network function optimization method, system, and computer program product, include optimizing network function chain components by modifying a structure of the network function chain components by removing one of the functions of the network function chain components in response to a constraint according to a policy requirement.

Abstract: A provenance method, system, and non-transitory computer readable medium for a plurality of eidetic systems having logs, include crawling the logs of each node of a plurality of nodes of the eidetic systems to cluster segments across the logs of temporally correlated events into clustered segments and analyzing the correlated segments to interleave an order of processes in the logs and assign a probability to the order of the processes occurring.

Abstract: A provenance method, system, and non-transitory computer readable medium for a plurality of eidetic systems having logs, include a log-segment clustering circuit configured to crawl the logs of each of the eidetic systems to cluster segments across the logs of temporally correlated events into clustered segments, a probabilistic interleaving circuit configured to analyze the correlated segments to interleave an order of processes in the logs and assign a probability to the order of the processes occurring, and a probabilistic linearization circuit configured to create a probability tree which includes a total probability that a process in the clustered segments causes a next process in the clustered segments until an end of the temporal event of the clustered segments for each of the interleaved order of processes interleaved by the probabilistic interleaving circuit.

Abstract: A network function optimization method, system, and computer program product, include optimizing network function chain components by modifying a structure of the network function chain components by removing one of the functions of the network function chain components in response to a constraint according to a policy requirement.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.