Abstract: A method, computer program product, and computer system for repairing a Dockerfile. Library versions containing initial version numbers of libraries are extracted from the Dockerfile. A Monte Carlo tree search (MCTS) is executed, using the extracted library versions as input, which generates a tree that includes multiple levels populated with noses. Each node in a level represents the generic library name of a library version in the Dockrerfile and an associated randomly selected version number. At least one of the randomly selected version numbers associated with at least one node in a level differs from the initial version number associated with a versionf. A best successful installation path is selected from the at least one successful installation path. The Dockerfile is repaired by inserting randomly selected version numbers into Dockerfile as replacements for some of the initial version numbers.

Abstract: Performing a three-dimensional (3D) reconstruction process of a set of selected image frames. In some instances, a plurality of sequential image frames that are extracted from a video file (including video content created through the use of Augmented Reality) are converted into a grayscale format. Once these image files are converted to a grayscale format, a two-dimensional (2D) entropy process is performed that is ultimately used to create a set of user-defined clusters, with each of these user-defined clusters being associated with at least a sub-set set of the grayscale frames. After a cluster is selected, a 3D reconstruction process is performed on the frames associated with the selected cluster.

Abstract: A method, computer program, and computer system are provided for performing multiple machine learning tasks through a shared framework. Data corresponding to a plurality of predetermined machine learning tasks is received. One or more steps of the machine learning tasks associated with the received data is performed on the received data by a shared backbone of a machine learning model. The predetermined plurality of machine learning tasks is completed on the received data by a plurality of sub-networks associated with each of the plurality of predetermined machine learning tasks.

Abstract: An embodiment includes parsing geographical data into a path graph having a plurality of nodes and edges, and identifying first and second subsets of the nodes as source nodes and destination nodes, respectively. The embodiment generates path data for a candidate delivery route from a source node to a destination node and along an edge between the source and destination nodes. The embodiment processes the path data using first and second evaluation techniques based on respective metrics. The embodiment compares evaluation values from the evaluation techniques to evaluation values associated with another candidate delivery route, and selects the candidate delivery route as a finalized delivery route based on the comparison results. The embodiment then generates a route plan that includes the finalized delivery route.

Abstract: A method, computer program product, and computer system for finding outliers in multidimensional time series samples. Each time series sample is divided into at least 2 sub samples having equal time duration. At least one prediction model is pre-trained using the sub samples and a prediction result for each sub sample for each prediction model is obtained by executing the pre-trained prediction models with the time series samples as input. A Shapely value corresponding to each prediction result is sub samples for each prediction model to generate multiple clusters of Shapely values for each prediction model. Highest ranking Shapely value outliers are determined from analysis of the multiple clusters. Highest ranking outlier sub samples corresponding to the highest ranking Shapely value outliers are identified.

Abstract: A method, computer program product, and computer system for repairing a Dockerfile. Library versions containing initial version numbers of libraries are extracted from the Dockerfile. A Monte Carlo tree search (MCTS) is executed, using the extracted library versions as input, which generates a tree that includes multiple levels populated with noses. Each node in a level represents the generic library name of a library version in the Dockrerfile and an associated randomly selected version number. At least one of the randomly selected version numbers associated with at least one node in a level differs from the initial version number associated with a versionf. A best successful installation path is selected from the at least one successful installation path. The Dockerfile is repaired by inserting randomly selected version numbers into Dockerfile as replacements for some of the initial version numbers.

Abstract: The present invention may include a computer receives raw data. The computer converts the raw data into a dataset, where the dataset comprises independent variables and dependent variables. Then, the computer clusters the dataset to determine a corresponding target value to each of a plurality of clusters. The computer constructs a nonlinear programming problem based on a prior experience and generates an enlarged dataset by solving the nonlinear programming problem.

Abstract: A machine-learning system receives from multiple sensors a set of trace-data time series. Each time series contains a chronological sequence of sensor measurements of one attribute of one instance of a manufacturing product or process. The system partitions each series into a set of contiguous segments and selects one received series to be a standard series for each attribute. The starting and ending measurements of each non-standard time series are then time-aligned to the starting and ending points of the non-standard series' corresponding standard series, using a dynamic time-warping procedure. One or more segments of each aligned non-standard series are then aligned to each segment of the corresponding standard series. The resulting time-aligned, segmented time series are then incorporated into a corpus that is used by a machine-learning module to train a self-learning application.