Abstract: An embodiment detects by a Clustering Component of a Recommendation System, a candidate content based on a user query, responsive to the detected candidate content, executes a clustering algorithm on the detected candidate content to output a cluster and a cluster result. The embodiment decides, by a Recommendation Clarification Component of the Recommendation System, to recommend a clarification based on the cluster result, comprising computing a distance between a cluster and a response of a large language model to the user query where an option list is updated with the clarification where the clarification is based on the cluster and the distance. The embodiment also detects by the Recommendation System a selection in the option list, responsive to the detected selection, generates a prompt based on the selection where the prompt is inputted into the Recommendation System and the large language model.

Abstract: Embodiments of the present disclosure relate to weight matrix prediction. In an embodiment, a computer-implemented method is disclosed. The method comprises sending a candidate weight matrix of a neural network to one of a plurality of computing nodes comprised in a computing system to perform a testing iteration. The method further comprises receiving a testing loss value from the one of the plurality of computing nodes based on the testing iteration. The method further comprises evaluating whether the testing loss value is applicable. The method further comprises determining that the candidate weight matrix is available to be employed in a new formal iteration in response to the testing loss value being applicable. In other embodiments, a system and a computer program product are disclosed.

Abstract: Embodiments of the present invention relate to methods, systems, and computer program products for user behavior management. In embodiments, a group of states of a user of an application system within a previous time period may be obtained. A state in the group of states may be associated with a privilege of the user for accessing resources in the application system during the previous time period. A feature of the user may be generated based on the group of states. A privilege of the user at a current time may be managed in the application system based on the feature. With these embodiments, the user behavior may be managed according to various aspect of the user's historical states and thus the user may be managed in a more accurate and effective manner.

Abstract: An approach is provided in which the approach constructs a 3-dimensional (3D) matrix based on a plurality of historical transactions performed by a user. The 3D matrix includes a set of features, a set of rows, and a set of channels. The approach trains a convolutional neural network using the 3D matrix, and then uses the trained convolutional neural network to predict a risk level of a new transaction initiated by the user. The approach transmits an alert message based on the predicted risk level.

Abstract: An approach is provided in which the approach constructs a 3-dimensional (3D) matrix based on a plurality of historical transactions performed by a user. The 3D matrix includes a set of features, a set of rows, and a set of channels. The approach trains a convolutional neural network using the 3D matrix, and then uses the trained convolutional neural network to predict a risk level of a new transaction initiated by the user. The approach transmits an alert message based on the predicted risk level.

Abstract: A computer-implemented method includes determining, by a master node, model update information at least based on a workload related to a task and a resource capacity of a computing environment. The model update information indicates respective model update suggestions for a plurality of inference models configured to perform the task. The method further includes distributing, by the master node, the model update information to a plurality of inference agents in the computing environment. The plurality of inference agents has a plurality of instances of the plurality of inference models executed thereon.

Abstract: Embodiments of the present invention relate to methods, systems, and computer program products for user behavior management. In embodiments, a group of states of a user of an application system within a previous time period may be obtained. A state in the group of states may be associated with a privilege of the user for accessing resources in the application system during the previous time period. A feature of the user may be generated based on the group of states. A privilege of the user at a current time may be managed in the application system based on the feature. With these embodiments, the user behavior may be managed according to various aspect of the user's historical states and thus the user may be managed in a more accurate and effective manner.

Abstract: Embodiments of the present disclosure relate to weight matrix prediction. In an embodiment, a computer-implemented method is disclosed. The method comprises sending a candidate weight matrix of a neural network to one of a plurality of computing nodes comprised in a computing system to perform a testing iteration. The method further comprises receiving a testing loss value from the one of the plurality of computing nodes based on the testing iteration. The method further comprises evaluating whether the testing loss value is applicable. The method further comprises determining that the candidate weight matrix is available to be employed in a new formal iteration in response to the testing loss value being applicable. In other embodiments, a system and a computer program product are disclosed.

Abstract: Techniques for allocating resources including receiving a first sub-stream of a data stream associated with a job and determining a dependency of a plurality of stages of the job. The techniques further include determining a metric for a second sub-stream of the data stream, where processing of the second sub-stream is completed and the metric indicates information associated with the processing of the second sub-stream. The techniques further include allocating resources for processing the first sub-stream based at least in part on the metric and the dependency.

Abstract: Techniques for allocating resources including receiving a first sub-stream of a data stream associated with a job and determining a dependency of a plurality of stages of the job. The techniques further include determining a metric for a second sub-stream of the data stream, where processing of the second sub-stream is completed and the metric indicates information associated with the processing of the second sub-stream. The techniques further include allocating resources for processing the first sub-stream based at least in part on the metric and the dependency.