Abstract: A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.

Abstract: A method and system for enhancing a facial image of a user in real time by digital generation of a portion of a facial image using artificial intelligence (AI) during a video conference with a plurality of participants. The method and system including receiving a digital image of a first portion of a user's face in real time from a camera viewing the first portion of the user's face. The camera is unable to view the second portion of the user's face. The method and system includes improving resolution and/or digitally completing the second portion of the user's facial image, using an AI system. The improving resolution including, receiving the digital image at the AI system which includes a Generative Adversarial Network (GAN). The method and system includes generating, in real time, a complete enhanced digital facial image of the user's face using the GAN.

Abstract: Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

Abstract: Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

Abstract: Intelligent lighting is provided to motorists traveling down a stretch of road by sequentially turning on adjacent lighting devices in a lighting segment only when required, such as when vehicles are approaching the lighting devices, and turning off the lighting devices or decreasing a light intensity output of the lighting devices to a predefined minimum lighting intensity output level when no vehicles are present. In addition, which bulb to use in a multi-bulb lighting device is determined, as well as the optimal lighting intensity level of the selected bulb. Further, it is determined which lighting devices in a lighting segment may be turned off or dimmed while maintaining a predefined minimum safe light/brightness level along a pathway associated with the lighting segment.

Abstract: Virtual machines are allocated among servers in a virtual environment, whereby each virtual machine has a current placement. A current fitness score is calculated for each virtual machine based on its current placement. Proposed placement plans are then generated, each plan including a proposed placement of each virtual machine. For each plan, a plan score is created. Each plan score is created by calculating a proposed fitness score for each virtual machine based on a proposed placement of that virtual machine in accordance with that plan, generating a virtual machine score for each virtual machine based on a comparison of that virtual machine's current fitness score and proposed fitness score, and then combining the virtual machine scores. The plan scores are then compared, and a target plan is selected from among the plans. The virtual machines are then reallocated among the servers in accordance with the target proposed placement plan.

Abstract: Virtual machines are allocated among servers in a virtual environment, whereby each virtual machine has a current placement. A current fitness score is calculated for each virtual machine based on its current placement. Proposed placement plans are then generated, each plan including a proposed placement of each virtual machine. For each plan, a plan score is created. Each plan score is created by calculating a proposed fitness score for each virtual machine based on a proposed placement of that virtual machine in accordance with that plan, generating a virtual machine score for each virtual machine based on a comparison of that virtual machine's current fitness score and proposed fitness score, and then combining the virtual machine scores. The plan scores are then compared, and a target plan is selected from among the plans. The virtual machines are then reallocated among the servers in accordance with the target proposed placement plan.

Abstract: Virtual machines are allocated among servers in a virtual environment, whereby each virtual machine has a current placement. A current fitness score is calculated for each virtual machine based on its current placement. Proposed placement plans are then generated, each plan including a proposed placement of each virtual machine. For each plan, a plan score is created. Each plan score is created by calculating a proposed fitness score for each virtual machine based on a proposed placement of that virtual machine in accordance with that plan, generating a virtual machine score for each virtual machine based on a comparison of that virtual machine's current fitness score and proposed fitness score, and then combining the virtual machine scores. The plan scores are then compared, and a target plan is selected from among the plans. The virtual machines are then reallocated among the servers in accordance with the target proposed placement plan.

Abstract: Virtual machines are allocated among servers in a virtual environment, whereby each virtual machine has a current placement. A current fitness score is calculated for each virtual machine based on its current placement. Proposed placement plans are then generated, each plan including a proposed placement of each virtual machine. For each plan, a plan score is created. Each plan score is created by calculating a proposed fitness score for each virtual machine based on a proposed placement of that virtual machine in accordance with that plan, generating a virtual machine score for each virtual machine based on a comparison of that virtual machine's current fitness score and proposed fitness score, and then combining the virtual machine scores. The plan scores are then compared, and a target plan is selected from among the plans. The virtual machines are then reallocated among the servers in accordance with the target proposed placement plan.

Abstract: Intelligent lighting is provided to motorists traveling down a stretch of road by sequentially turning on adjacent lighting devices in a lighting segment only when required, such as when vehicles are approaching the lighting devices, and turning off the lighting devices or decreasing a light intensity output of the lighting devices to a predefined minimum lighting intensity output level when no vehicles are present. In addition, which bulb to use in a multi-bulb lighting device is determined, as well as the optimal lighting intensity level of the selected bulb. Further, it is determined which lighting devices in a lighting segment may be turned off or dimmed while maintaining a predefined minimum safe light/brightness level along a pathway associated with the lighting segment.

Abstract: Intelligent lighting is provided to motorists traveling down a stretch of road by sequentially turning on adjacent lighting devices in a lighting segment only when required, such as when vehicles are approaching the lighting devices, and turning off the lighting devices or decreasing a light intensity output of the lighting devices to a predefined minimum lighting intensity output level when no vehicles are present. In addition, which bulb to use in a multi-bulb lighting device is determined, as well as the optimal lighting intensity level of the selected bulb. Further, it is determined which lighting devices in a lighting segment may be turned off or dimmed while maintaining a predefined minimum safe light/brightness level along a pathway associated with the lighting segment.

Abstract: Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

Abstract: A system, method and program product for selecting an algorithm and feature set to solve a problem. A perpetual analytics system is disclosed that provides a genetic algorithm for jointly selecting an algorithm and feature set to solve a problem, comprising: an evolutionary computing engine for processing data encoded as chromosomes, wherein each chromosome encodes an algorithm and a feature set; a domain knowledge store that maintains a plurality of algorithms and a plurality of features; a system for applying a generation of chromosomes to a set of data to provide a set of results; and a fitness function for evaluating the set of results to rate a performance of each chromosome in the set of chromosomes; wherein the evolutionary computing engine is adapted to evolve a subset of the set of chromosomes into a new generation of chromosomes.

Abstract: Intelligent lighting is provided to motorists traveling down a stretch of road by sequentially turning on adjacent lighting devices in a lighting segment only when required, such as when vehicles are approaching the lighting devices, and turning off the lighting devices or decreasing a light intensity output of the lighting devices to a predefined minimum lighting intensity output level when no vehicles are present. In addition, which bulb to use in a multi-bulb lighting device is determined, as well as the optimal lighting intensity level of the selected bulb. Further, it is determined which lighting devices in a lighting segment may be turned off or dimmed while maintaining a predefined minimum safe light/brightness level along a pathway associated with the lighting segment.

Abstract: A system, method and program product for selecting an algorithm and feature set to solve a problem. A perpetual analytics system is disclosed that provides a genetic algorithm for jointly selecting an algorithm and feature set to solve a problem, comprising: an evolutionary computing engine for processing data encoded as chromosomes, wherein each chromosome encodes an algorithm and a feature set; a domain knowledge store that maintains a plurality of algorithms and a plurality of features; a system for applying a generation of chromosomes to a set of data to provide a set of results; and a fitness function for evaluating the set of results to rate a performance of each chromosome in the set of chromosomes; wherein the evolutionary computing engine is adapted to evolve a subset of the set of chromosomes into a new generation of chromosomes.