Abstract: Systems, devices, computer-implemented methods, and tangible non-transitory computer-readable media for generating reports from one or more databases that store disparate datasets are provided. Specifically, the proposed systems enable the intelligent generation of reports from multiple datasets by automatically determining a proposed set of join configurations for combination of the multiple datasets. Unique identifiers can be assigned to a portion of the combined dataset and the combined dataset can be expanded for performing an aggregation operation associated with the portion of the combined dataset by referencing the one or more unique identifiers. Reports can be generated based on the combined dataset and the aggregation operation.

Abstract: There is provided a computer implemented method, system and device for automatically generating a machine learning model for forecasting a likelihood of compromise in one or more transaction devices and subsequently triggering performing an action on one or more related computing devices based on a potentially compromised transaction device.

Abstract: In some embodiments, apparatuses and methods are provided herein useful for event detection relative to catalog items available for commercial sale. In some embodiments, there is provided a system for event detection relative to catalog items available for commercial sale including an interface; a memory; and the first control circuit. The first control circuit configured to execute the computer-implemented code to: receive event data corresponding to one or more events referred to in one or more online sources; determine a corresponding score for each of the event data based on a relative level of negativity; identify one or more topics and key phrases of one or more of the event data having at least a specified score; identify one or more catalog items that have a similarity to the one or more topics and key phrases; and output an indication.

Abstract: An object detection model and relationship prediction model are jointly trained with parameters that may be updated through a joint backbone. The offset detection model predicts object locations based on keypoint detection, such as a heatmap local peak, enabling disambiguation of objects. The relationship prediction model may predict a relationship between detected objects and be trained with a joint loss with the object detection model. The loss may include terms for object connectedness and model confidence, enabling training to focus first on highly-connected objects and later on lower-confidence items.

Abstract: A system can implement, in a first hyperparameter configuration state, a first set of hyperparameter search operations. The first set of hyperparameter search operations includes selecting a first set of hyperparameters. Each hyperparameter of the first set of hyperparameters having a corresponding configuration. Additionally, the first set of hyperparameter search operations includes obtaining a first set of performance data that includes information indicating a performance of each hyperparameter of the first set of hyperparameters, and assigning a value to each hyperparameter of the first set of hyperparameters based on the corresponding performance data.

Abstract: A method and system for generating engineering programs for one or more engineering objects in a technical installation is provided. The method includes receiving a request to generate an engineering program associated with the technical installation. The method further includes determining a set of programming blocks from a plurality of programming blocks corresponding to a set of industrial domains. The method further includes generating an ontology schema for the set of programming block. The ontology schema includes information about relationships between a set of variables corresponding to each programming block in the set of programming blocks and a set of Key performance indicators associated with the set of programming blocks. The method further includes generating the engineering program associated with the technical installation by modification of the set of programming blocks.

Abstract: This application relates to apparatus and methods for training machine learning models. In some examples, a pool of worker pods are generated that can execute tasks to train a machine learning model. The pool of work pods are assigned tasks by a master that communicates with the worker pods using a work queue. Each worker pod can provide output using a results queue. The embodiments may operate with less reliable memory, such as object stores, which may be less costly than other types of storage mechanisms. To operate in less reliable environments, each worker pod can include a checkpoint mechanism that can recover from interruptions, such as interruptions due to node failure or preemption. For example, the checkpoint mechanism may allow a worker pod to continue processing a task, when the task is interrupted, from a last checkpoint. Processing results are provided to a results queue when a task completes.

Abstract: An object of the invention is to provide compounds as selective JAK1 inhibitor, a process for preparation of the inhibitors, a composition containing the compounds and utility of the compounds.

Abstract: The present invention relates to novel compounds of formula (I), process for preparation of the same and composition comprising these compounds.

Abstract: The present invention relates to novel compounds of formula (I), process for preparation of the same and composition comprising these compounds.

Abstract: The present invention relates to novel compounds of formula (I), process for preparation of the same and composition comprising these compounds.

Abstract: A recommendation system models unknown preferences as samples from a noise distribution to generate recommendations for an online system. Specifically, the recommendation system obtains latent user and item representations from preference information that are representations of users and items in a lower-dimensional latent space. A recommendation for a user and item with an unknown preference can be generated by combining the latent representation for the user with the latent representation for the item. The latent user and item representations are learned to discriminate between observed interactions and unobserved noise samples in the preference information by increasing estimated predictions for known preferences in the ratings matrix, and decreasing estimated predictions for unobserved preferences sampled from the noise distribution.

Abstract: The present invention relates to novel compounds of formula (I), process for preparation of the same and composition comprising these compounds.

Abstract: The present invention relates to novel compounds of formula (I), process for preparation of the same and composition comprising these compounds.

Abstract: Embodiments herein provide a method and a system for rendering a UI of an application installed in an electronic device based on context of usage and persona of a user. Contextual parameters based on application usage and application session are aggregated, which are fed to a context awareness unit to determine a context. The context is determined by an event processor or a machine learning method based on aggregated parameters, user interactions, user navigation, and so on. The embodiments categorize users based on user dominant behavior by machine learning using the determined context and historical user interactions, to develop user persona. Embodiments render UI templates based on the context and user persona.

Abstract: The present invention relates to novel compounds of formula (I), process for preparation of the same and composition comprising these compounds.