Abstract: The present disclosure describes techniques for improving data subsampling for recommendation systems. A user-item graph associated with training data may be constructed. An importance of user-item interactions may be estimated via graph conductance based on the user-item graph. An importance of the training data may be measured via sample hardness using a pre-trained pilot model. A subsampling rate may be generated based on the importance estimated from the user-item graph and the importance measured by the pre-trained pilot model.

Abstract: A system allows users to define alternate scenarios in a database to allow users to analyze the data to determine what happens if certain values of some of the fields were different from the values stored in the database during its actual operation. The system stores a source dataset representing data of a source database, for example, a production database. The system receives an alternate scenario specification that includes instructions for determining alternate values for a portion of the database. The system determines an alternate dataset comprising alternate values for a portion of the database as defined by the alternate scenario specification. The system receives requests to process database queries for one or more alternate scenarios. The system combines data from the source dataset and the alternate datasets corresponding to the alternate scenarios specified by the request to process database queries.

Abstract: A method for determining relationships of entities associated with interactions may include receiving interaction data associated with interactions between first and second entities. The interaction data may include first entity identification data, second entity identification data, and relative timing data. A node may be generated for each second entity. A set of edges may be generated for each first entity to include an edge connecting the node associated with second entity identification data of each interaction to the node associated with second entity identification data of a next interaction based on the relative timing data. Sample data associated with a portion of the nodes/edges may be generated. A vector for each node of the sample may be generated. A distance between each vector and other vectors may be determined. A relationship between each second entity may be determined based on the distance. Systems and products are also disclosed.

Abstract: A method for training a control system model includes introducing a simulated fault into a software simulation of a physical system and generating emulated sensor data based on the simulated fault, where the emulated sensor data emulates output from one or more sensors of the physical system. The method further includes obtaining output data from a test control system provided with the emulated sensor data, where the test control system emulates a control system of the physical system and tagging the output data with the simulated fault to create training data. The method further includes utilizing the training data to train the control system model, where the control system model is a machine learning model for use with the control system of the physical system during operation of the physical system.

Abstract: An optimized fact checking system analyzes and determines the factual accuracy of information and/or characterizes the information by comparing the information with source information. The optimized fact checking system automatically monitors information, processes the information, fact checks the information in an optimized manner and/or provides a status of the information. In some embodiments, the optimized fact checking system generates, aggregates, and/or summarizes content.

Abstract: A storage medium storing a program that causes a computer to execute a process that includes acquiring a first model that is trained based on training data which indicates a first combination of constituent values of a target object and an environmental value in an experiment on the target object with associating with a characteristic value and that specifies a mean value and a deviation value of the characteristic value; acquiring a second model that is trained based on training data which indicates a second combination of the constituent values and an allowable condition for the experiment, and that specifies the allowable condition; and generating a solution set for the first combination by performing multi-objective optimization by a penalty term based on the allowable condition, a first objective function, and a second objective function.

Abstract: A computer-implemented method, a computer program product, and a computer system for automatic adaptive client selection in federated learning. A server sends parameters of a machine learning model to all of clients, where all of the clients compute respective gradients using the parameters. The server receives sketches of the respective gradients, where the sketches are computed by all of the clients. The server uses the sketches to compute similarity between all of the clients and clusters the all of the clients based on the similarity. The server optimizes a number of client clusters and a dimension of the sketches, subject to a constraint of memory consumption, a constraint of communication overhead, and a performance metric. The server determines a subset of the clients that send the respective gradients, by selecting the clients from the client clusters. The server aggregates the respective gradients sent by the subset of the clients.

Abstract: An inlet debris monitoring includes processing circuitry configured to: obtain a data set of electrostatic charge data from an electrostatic sensor; utilize a dimensional reduction technique to obtain a first set of basis vectors that represent the data set in a reduced dimensional space that is reduced with respect to initial dimensions of the data set; utilize the first set of basis vectors or a second set of reference basis vectors which are based on historical electrostatic charge data for one or more reference gas turbine engines, to project the data set onto the reduced dimensional space and obtain a reduced dimensional representation of the data set; utilize machine learning to determine whether the reduced dimensional representation of the data set indicates foreign object debris in the particular gas turbine engine; and based on the determination indicating detection of foreign object debris, provide a foreign object debris notification.

Abstract: An apparatus and method for causal multi-touch attribution are described. One or more aspects of the apparatus and method include a time series component configured to generate an ordered series representing a plurality of precursor events corresponding to a result event, wherein each of the precursor events is associated with an event category from a set of event categories; a temporal convolution network configured to generate a series of predictive values corresponding to the plurality of precursor events by computing a plurality of hidden vector representations for at least one of the precursor events; and an attribution component configured to compute an attribution value for each of the event categories based on the series of predictive values.

Abstract: According to various implementations, generally disclosed herein is a hybrid and hierarchical neural architecture search (NAS) approach. The approach includes performing a search space partitioning scheme to divide the search space into sub-search spaces. The approach further includes performing a first type of NAS, such as a Multi-trial NAS, to cover a search across the sub-search spaces. The approach also includes performing a second type of NAS, such as a One-Shot NAS, to cover each sub-search space. The approach further includes automatically stopping the second type of NAS based on one or more early stopping criteria.

Abstract: Most of the existing production applications in different domains are still running on. Mainframe applications in production receive data from various resources and process these data within. Understanding the structure of input data and output data is extremely important. A method and system for machine learning based understanding of a plurality of data elements in a mainframe program code has been provided. The method discloses a machine learning model that understands the structure of data elements in a Mainframe program code. The model considered is a graph neural network based architecture model. The disclosed method replicates memory mapping happening in the application program environment. The method understands the structure of the data element and the impact created by each data element on other data elements in the application and interfacing applications. The disclosed solution serves as a building block in problems such as code translation, reverse engineering etc.

Abstract: Embodiments of the invention are directed to a computer-implemented method for matching a graph-under-analysis to a technique for embedding the graph-under-analysis. In a non-limiting example, the computer-implemented method includes receiving, using a processor, graph data representing the graph-under-analysis, wherein the graph-under-analysis represents a network. The graph data is analyzed, using the processor, to extract graph property data representing properties of the graph-under-analysis. Based at least in part on a result of analyzing the graph property data, one or more embedding techniques are selected, wherein at least one of the one or more embedding techniques is configured to transform the graph data to a graph embedding that is used by a task algorithm to perform a task.

Abstract: A file connection method and apparatus, terminal device, and storage medium are provided. The file connection method is applied to a file system. The file system includes a plurality of devices. Metadata of a target file is separately stored in metabases of the plurality of devices. The method includes: A current device determines a device that accesses the target file last time, a device in which the file is located, and a connection record based on the stored metadata of the target file, and the current device obtains file data of the target file from at least one of the device that accesses the target file last time and the device in which the file is located; and the current device displays to-be-displayed content based on at least one of the connection record of the target file and the file data of the target file.

Abstract: An embodiment for monitoring machine learning models to detect and rectify model drift using governance. The embodiment may receive a plurality of machine learning models and register the plurality of machine learning models to a governance dashboard. The embodiment may automatically monitor the received plurality of machine learning models to identify factors used by each of the received plurality of machine learning models and generate corresponding clusters of similar machine learning models. The embodiment may automatically detect an incorrect decision made by a target machine learning model and then automatically calculate a correlation score between the target machine learning model and machine learning models within an associated corresponding cluster of similar machine learning models. The embodiment may, in response to detecting a correlation score above a threshold, automatically determine and output a cluster reinforcement recommendation.

Abstract: A predictor interactive learning system of the present invention includes a machine learning unit configured to perform machine learning of a predictor that outputs a predicted value indicating a likelihood of being a predetermined intrinsic expression, by using teacher data and teacher labels, an interest score calculation unit configured to obtain an interest score according to statistical data of a corresponding word in a corpus including the predicted value of the predictor for each of words of the corpus, an interactive learning frame unit configured to extract the word serving as the teacher data used in next learning of the predictor according to the interest score, and a question-response unit configured to output a question of whether the extracted teacher data is an intrinsic expression of which the likelihood is predicted by the predictor, and to acquire a teacher label corresponding to the teacher data, as a response to the question, in which the machine learning unit performs machine learning of

Abstract: A method for augmenting a building model comprises receiving a first building model associated with at least a portion of a building and having a first format, receiving a second building modeling associated with the portion of the building and having a second format, comparing the first building model to the second building model to identify at least one element in the first building model not included in the second building model, and updating the second building model to include the identified at least one element.

Abstract: The Artificial Intelligence engine can perform one or more operations. A query can be submitted to the Artificial Intelligence engine to search directly for a set of targeted properties for an unnamed molecule having the set of targeted properties. An indication of a structure of one or more candidate molecules found to have the set of targeted properties with the Artificial Intelligence engine is generated by applying one or more machine learning algorithms. The indication of the structure of the one or more candidate molecules found to satisfy the set of targeted properties in 3-dimensional space is supplied to a user in response to the query for the set of targeted properties to the Artificial Intelligence engine.

Abstract: The disclosure discloses a method, an apparatus and a device for implementing search. The method includes: in response to a comment trigger request for a video, displaying a comment page of the video; determining at least one first related search word based on video content of the video and/or comment information in the comment page, the first related search word being a search word which the user may need to search for and which is determined based on the video content and/or the comment information; displaying the first related search word in a preset region of the comment page; and displaying a result page of the first related search word in response to triggering the first related search word. By determining, based on the video content and/or the comment information, and displaying the first related search word, the user may conveniently trigger the first related search word, to implement search.

Abstract: Disclosed in the present disclosure is a fast evaluation method of site seismic liquefaction hazard based on artificial intelligence algorithm: establishing a historical seismic and site information database, the database including a demand input module, a web crawler module, a data processing module, and a database module connected in sequence; a neural network model performs prediction to acquire a post-earthquake site dominant frequency; and, on the basis of the post-earthquake site dominant frequency, acquiring a site earthquake damage degree and seismic performance parameters. The present disclosure solves the problem of fast evaluating post-earthquake site earthquake damage and site seismic performance parameters, and can rapidly evaluate the site liquefaction or softening earthquake damage degree and site seismic performance parameters in given earthquake conditions.

Abstract: A program is provided to automatically train using a training dataset a machine learning model for detecting anomalies. The machine learning model is automatically applied to a validation dataset to determine anomaly detection results. A histogram of the anomaly detection results of the machine learning model is automatically generated. The histogram is automatically analyzed, and a first peak and a second peak of the histogram is automatically identified. A threshold activation of the machine learning model is automatically determined based at least in part on the automatically identified second peak of the histogram.