Abstract: Embodiments of the present disclosure relate to a method, system and computer program product for semantic search based on a graph database. In some embodiments, a method is disclosed. According to the method, the user jobs of a user are obtained from a first software product. Based on the user jobs, target test cases are selected from a plurality of test cases associated with the first software product and a second software product. The target test cases are applied to the first software product and the second software product, and in accordance with a determination that a result of applying the target test cases satisfies a predetermined criterion, an instruction is provided to indicate migrating from the first software product to the second software product. In other embodiments, a system and a computer program product are disclosed.

Abstract: At least one processor identifies dependency relationships among libraries in a repository of libraries. Using the dependency relationships among libraries, at least one machine learning model can be created that predicts with a confidence value a dependency between a given library and a target library. An L layer tree-like graph can be created, using the dependency relationships among libraries and an application package. L can be configurable. Versions of the libraries to use can be determined by running the at least one machine learning model for each pair of nodes having a dependency relationship in the L layer tree-like graph, the at least one machine learning model identifying the dependency relationship with a confidence value, where pairs of nodes having largest confidence values are selected as the versions of the libraries to use in the application package.

Abstract: Embodiments of the present disclosure relate to a method, system and computer program product for semantic search based on a graph database. In some embodiments, a method is disclosed. According to the method, the user jobs of a user are obtained from a first software product. Based on the user jobs, target test cases are selected from a plurality of test cases associated with the first software product and a second software product. The target test cases are applied to the first software product and the second software product, and in accordance with a determination that a result of applying the target test cases satisfies a predetermined criterion, an instruction is provided to indicate migrating from the first software product to the second software product. In other embodiments, a system and a computer program product are disclosed.

Abstract: The present disclosure relates to privacy protection in a search process. According to a method, a target emotion vector is extracted from a search interaction, the target emotion vector representing emotional information in the search interaction. Respective emotion distances between the target emotion vector and respective emotion vectors associated with a plurality of text clusters are determined. The plurality of text clusters is clustered from a dictionary of text elements. A first number of text clusters are selected from the plurality of text clusters based on the determined respective emotion distances. The first number of text clusters have emotion distances larger than at least one unselected text cluster among the plurality of text clusters. A plurality of confused search interactions are constructed for the search interaction based on the first number of text clusters, and the plurality of confused search interactions are performed.

Abstract: A method, computer program, and computer system are provided for training a machine learning model. A feature associated with training data derived from a dataset is identified. A machine learning model is generated based on the training data. At least a portion of the training data associated with maximizing an importance value associated with the identified feature is selected. The importance value corresponds to a need associated with the machine learning model. One or more weight values is assigned to the selected portion of the training data. The machine learning model is updated based on the assigned weight values.

Abstract: Identifying node importance in a machine learning pipeline is provided. Changes in accuracy of the machine learning pipeline are recorded for each respective node setting change in a randomly generated group of node settings inputted into each corresponding node included in the machine learning pipeline. A regression model is generated to determine a relationship between each respective node setting change in the randomly generated group of node settings inputted into each corresponding node and the changes in the accuracy of the machine learning pipeline. A node of importance is identified in the machine learning pipeline using the regression model based on the relationship between each respective node setting change in the randomly generated group of node settings inputted into each corresponding node and the changes in the accuracy of the machine learning pipeline.

Abstract: Embodiments of the present disclosure relate to screenshot-based memos. In an embodiment, a computer-implemented method is disclosed. The method comprises a monitoring displaying screen on a computing device for determining whether the displaying screen reaches a preset trigger condition. The method further comprises capturing a snapshot of the displaying screen in response to the displaying screen reaching the preset trigger condition. The method further comprises matching one or more screenshots comprised in one or more screenshot-based memos and the captured snapshot for obtaining a similarity degree. The method further comprises deploying the one or more screenshot-based memos on the displaying screen in response to the similarity degree meeting a preset similarity threshold. In other embodiments, a system and a computer program product are disclosed.