Abstract: A method, system, and computer program product generate precomputed explanation scores in AI systems. The method includes obtaining a set of labeled transactions comprising input features and corresponding output labels generated by a machine learning (ML) model and generating an explainable artificial intelligence (XAI) module. The generating includes clustering the labeled transactions based on the input features, scoring homogeneity of the clustered transactions based on the corresponding output labels, and selecting at least one cluster from the clustered transactions based on the homogeneity scoring. The generating further includes obtaining, by an explainability model, explainability scores for transactions in the at least one cluster, generating a unified explainability score for the at least one cluster based on the explainability scores, and storing the unified explainability score in a set of precomputed explanations.

Abstract: A method, system and computer program product for visually depicting a topology of a quantum device. Quantum system calibration data is retrieved, which includes the number of qubits utilized by the quantum device and a coupling map, which provides connectivity data pertaining to how qubits of the quantum device are interconnected. Furthermore, the operational characteristics of the quantum device are retrieved. A topology diagram of the quantum device is then generated and displayed based on the quantum system calibration data and the operational characteristics of the quantum device, where the nodes in the topology diagram represent the qubits utilized by the quantum device and are interconnected in the topology diagram based on the coupling map. Furthermore, the distance of each line interconnecting two nodes is based on the operational characteristics of the quantum device, such as the value of the operational characteristic involving the qubits represented by the two nodes.

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 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 computer-implemented method for managing updates to a knowledge graph with incoming change requests is disclosed. The method comprises creating a snapshot of the knowledge graph, building sub-regions of the knowledge graph, classifying each incoming change to the knowledge graph so that it relates to one of the sub-regions, creating at least one change layer, wherein each of the at least one change layer relates to one of the sub-regions, wherein each of the at least one change layer comprises a portion of the incoming change requests, closing the at least one change layer upon reaching a predetermined threshold value for the at least one change layer, and integrating the at least one change layer into the knowledge graph, thereby generating a updated knowledge graph.

Abstract: A first input transaction is classified into a first input space cluster in a set of input space clusters. It is determined that the first input space cluster maps to a single explainability space cluster in a set of explainability space clusters. Using an interpretable model corresponding to the single explainability space cluster, a first machine learning model prediction is explained, the first machine learning model prediction resulting from processing, by a machine learning model, the first input transaction.

Abstract: A computer-implemented method for managing updates to a knowledge graph with incoming change requests is disclosed. The method comprises creating a snapshot of the knowledge graph, building sub-regions of the knowledge graph, classifying each incoming change to the knowledge graph so that it relates to one of the sub-regions, creating at least one change layer, wherein each of the at least one change layer relates to one of the sub-regions, wherein each of the at least one change layer comprises a portion of the incoming change requests, closing the at least one change layer upon reaching a predetermined threshold value for the at least one change layer, and integrating the at least one change layer into the knowledge graph, thereby generating a updated knowledge graph.

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, system, and computer program product for visualizing a machine learning model are provided. A confusion matrix and model performance metric data are received from a classification model. For each data point in the confusion matrix, a corresponding pixel is generated. The pixels are grouped into clusters. Each cluster represents a label in the confusion matrix. A centroid is generated for each cluster. Using the model performance metric data, a misclassification indicator arrow is generated for each misclassified data point. The misclassification indicator arrow indicates both the predicted class and the actual class. The clusters, the centroids, and the misclassification indicator arrow are displayed as a graphical visualization of the machine learning model.

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, computer system, and a computer program product for personalized recommendations is provided. The present invention may include determining a momentum score for each item of a training data set. The present invention may include querying a recommendation engine for an initial list of items. The present invention may include generating a blended score for each item of the initial list of items, wherein the blended score is determined based on the momentum score and the confidence score for each item of the initial list of items. The present invention may include presenting a personalized list of recommendations to the user based on the blended score for each item of the initial list of items.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems for prioritizing and recommending social media interactions. The computer-implemented method may include one or more processors configured for obtaining first profile data corresponding to a first social media account and additional profile data corresponding to additional social media accounts; determining a first unique identifier based on the first profile data and additional unique identifiers based on each respective additional profile data; determining overlap scores between the first unique identifier and each of the additional unique identifiers; prioritizing the additional social media accounts based on a first order of priority; and displaying the additional social media accounts based on the first order of priority on a user interface of a computing device associated with the first social media account.

Abstract: In an approach to creating explanatory confusion matrices, responsive to receiving a machine learning model for analysis, a confusion matrix is calculated for the machine learning model, where each cell in the confusion matrix has a corresponding set of data. A link is created from each cell in the confusion matrix to the corresponding set of data. Responsive to a user selecting in a user interface a specific cell of the confusion matrix, the corresponding set of data to the specific cell is displayed on the user interface.

Abstract: Methods, computer program products, and systems are presented for service cost prediction using machine learning. A claims risk model is formed for predicting a per member per month cost based on variables trained in early prediction models and variables of claim record data from data sources. A training dataset for the claims risk model is modified based on a preconfigured cap value and two distinctive datasets are generated, which trains the claims risk model into a capped submodel and an outlier submodel, respectively. Each submodel makes prediction of the per member per month cost and a slice model interpolates a cost predicted by the capped submodel and another predicted by the outlier submodel. The splice model redistributes the outcomes of the capped submodel and the outlier submodel.

Abstract: Gestural zoom operation of an imaging device is facilitated by receiving, by one or more processors, zoom configuration data of a user for configuring gestural zoom operation of the imaging device, where the imaging device includes a gestural zoom mode with a zoom function that is user-configurable, and by configuring for the user, by the one or more processors, the gestural zoom operation of the imaging device for the gestural zoom mode. The configuring uses the received zoom configuration data, and includes configuring the zoom function for the user and mapping a selected portion of the configured zoom function to a gestural range of the user of the imaging device.

Abstract: Technology for determining an amount of time to wait to retry requests to a representational state transfer (REST) server system for a REST resource, where the time to wait is always chosen to be a prime number of time units (for example, slots, milliseconds). While currently conventional systems will sometimes use a prime number of time units to wait for a retry request, various embodiments of the present invention will always, and invariably, use a prime number of time units. The REST resource may be, for example, a REST application programming interface (API) that is requested by and delivered to a client system using hypertext transfer protocol (HTTP).

Abstract: A method, system, and computer program product for visualizing a machine learning model are provided. A confusion matrix and model performance metric data are received from a classification model. For each data point in the confusion matrix, a corresponding pixel is generated. The pixels are grouped into clusters. Each cluster represents a label in the confusion matrix. A centroid is generated for each cluster. Using the model performance metric data, a misclassification indicator arrow is generated for each misclassified data point. The misclassification indicator arrow indicates both the predicted class and the actual class. The clusters, the centroids, and the misclassification indicator arrow are displayed as a graphical visualization of the machine learning model.

Abstract: Methods, computer program products, and systems are presented for service cost prediction using machine learning. A claims risk model is formed for predicting a per member per month cost based on variables trained in early prediction models and variables of claim record data from data sources. A training dataset for the claims risk model is modified based on a preconfigured cap value and two distinctive datasets are generated, which trains the claims risk model into a capped submodel and an outlier submodel, respectively. Each submodel makes prediction of the per member per month cost and a slice model interpolates a cost predicted by the capped submodel and another predicted by the outlier submodel. The splice model redistributes the outcomes of the capped submodel and the outlier submodel.

Abstract: Technology for determining an amount of time to wait to retry requests to a representational state transfer (REST) server system for a REST resource, where the time to wait is always chosen to be a prime number of time units (for example, slots, milliseconds). While currently conventional systems will sometimes use a prime number of time units to wait for a retry request, various embodiments of the present invention will always, and invariably, use a prime number of time units. The REST resource may be, for example, a REST application programming interface (API) that is requested by and delivered to a client system using hypertext transfer protocol (HTTP).

Abstract: An approach is provided in which the approach initiates a video conference between a first user utilizing a first device and a second user utilizing a second device. The first device uses a camera positioned at a first set of coordinates to capture a first live video feed of the first user from a first alignment perspective, and also displays a second live video feed of the second user on the screen at a second set of coordinates. The approach manipulates the first live video feed such that the manipulated first live video feed captures the first user from a second alignment perspective corresponding to the second set of coordinates, and transmits the manipulated first live video feed to the second device.