Abstract: Existing methodologies apply correlations blindly on the entire event dump leading to many irrelevant correlation signatures and miss out many correlation signatures due to its weak confidence and ignores the events which are observed rarely. These approaches also generate large number of correlation signatures which becomes overwhelming to consume. Embodiments of the present disclosure provide systems and methods for managing event correlations. Events information of an enterprise along with associated timeseries is received and a right correlation scope and a plurality of self-tuned time windows are selected. The correlation scope and the plurality of self-tuned time windows are then used for deriving a plurality of event correlation signatures associated with a set of candidate events. The derived event correlation signatures are then interpreted to obtain a filtered set of event correlation signatures.

Abstract: A universal security implement for cannabis containers includes a security mechanism interface, a rim, and an immobilization implement. A user of the universal security implement for cannabis containers can fully secure a container by situating the universal security implement onto or into the container such that the immobilization implement fully precludes movement relative to the container. Moreover, the universal security implement's user may manipulate a security mechanism, such as a child-resistant cap, such that the security mechanism and the security mechanism interface interact to fully close and secure the contents within the container.

Abstract: A method for making and assembling personal hygiene material containers whereby a cap and core are provided; a lid is disposed at the cap; the cap is then placed with the lid onto the core; situating a leakage prevention insert within the core; and situating a hygiene material elevator within the core. The leakage prevention insert possesses a circumference greater than that of the core such that upon insertion of the leakage prevention insert into the core an overhang is created along the entirety of the leakage prevention insert's circumference, where the overhang creates a complete and tight seal by creating an abutment with the inner wall of the core. The abutment thereby prevents the leakage of hygiene material during the manufacture fill process when the hygiene material is in its liquid form.

Abstract: This disclosure presents an image generation system designed to generate a series of contextually-persistent visual images for a text document. For instance, the image generation system utilizes multiple computer-based models, entity identifiers, and visual entity embeddings to create multiple synthetic images for a given text document. These synthetic images share a consistent theme and style. Additionally, the synthetic images include the same characters, places, and objects. Indeed, the image generation system implements seamless and consistent visual representations of the entities throughout the text document.

Abstract: A method may comprise, for each one of a plurality of destination users, computing a score using a first function based on a probability of a source user performing a source action directed towards the destination user, a second function based on a probability of the destination user performing a destination action in response to the source action, and a third function based on a measure of interaction by the destination user with an online service to result from the destination action being performed by the destination user. The score for inactive users may be boosted using an optimization algorithm with a first constraint comprising a maximum threshold number of the inactive users to display as recommendations to the source user and a second constraint comprising a minimum threshold number of the inactive users for which the source user to perform the source action.

Abstract: Many of the existing approaches for grouping of jobs for processing are manual in nature, and are based on intuition. Other approaches involve modeling batches as node-edge graphs and use existing graph theory solutions such as connected components or cliques to find logical subgraphs. However, this approach isn't efficient due to the fact that there may be various constraints unique to batch systems. The disclosure herein provides a method and system for creating logical groups of jobs meeting objective function and satisfying constraints. In this method, a logical grouping of jobs is performed such that all defined constraints are satisfied. If any job is not satisfying one or more of the defined constraints, smaller components of the jobs are derived such that the smaller components satisfy the constraints. The logical groups are formed by merging the jobs and the smaller components satisfying the constraints.

Abstract: Techniques for tackling delayed user response by modifying training data for machine-learned models are provided. In one technique, a first machine-learned model generates a score based on a set of feature values. A training instance is generated based on the set of feature values. An attribute of the training instance is modified based on the score to generate a modified training instance. The attribute may be an importance weight of the training instance or a label of the training instance. The modified training instance is added to a training data. One or more machine learning techniques are used to train a second machine-learned model based on the training data.

Abstract: In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

Abstract: Data inaccuracy and insufficiency are critical aspects to be analyzed to improve batch predictions, specifically in context of SLA jobs as they are foremost in affecting deliverables. Embodiments of the present disclosure provide a method and system for enhancing batch predictions by localizing jobs contributing to time deviation and generating fix recommendations by fixing data inaccuracy and insufficiency. The term fix recommendation refers to recommending a list of plausible fixes to identified causes that reduce batch prediction errors enhancing accuracy of predictions. The localization is performed by bottom-up traversing of a batch graph representing a batch process, if the batch process has a Service level Agreement (SLA) job, by narrowing down to the SLA job that has end time inaccuracies. The localization enables identifying the origin or real contributors and root cause analysis is performed for the localized jobs to generate effective fix recommendations by fixing data inaccuracy and insufficiency.

Abstract: Techniques for using machine learning to leverage deep segment embeddings are provided. In one technique, a set of training data is processed using one or more machine learning techniques to train a neural network and learn an embedding for each segment of multiple segments. In response to receiving a request, multiple elements are identified, such as a source entity that is associated with the request, a source embedding for the source entity, a particular segment with which the source entity is associated, a segment embedding for the particular segment, and multiple target entities. For each target entity, a target embedding is identified and the target embedding, the source embedding, and the segment embedding are input into the neural network to generate output that is associated with the target entity. Based on the output, data about a subset of the target entities is presented on a computing device.

Abstract: An online connection server is configured to more accurately predict connections for a viewing member of an online connection network. The online connection server may implement a machine-learning model that uses prior interactions by the viewing member to determine those connections that are likely to lead to more substantial interactions with the viewing member. The machine-learning model may be implemented using a reinforcement learning technique, such as a Deep Q network. The online connection server may further implement a state representation module that generates a state from a graph-based embedding of the viewing member profile, where the state is used to train the machine-learning model and determine an optimal candidate to recommend as a connection for the viewing member.

Abstract: Operations for facilitating establishment of connections in an online network are disclosed. A set of connection recommendations for a first entity associated with the online network is accessed. For each connection recommendation in the set of connection recommendations, a ranking value associated with the connection recommendation is accessed, a utility value corresponding to the connection recommendation is determined, and an adjusted the ranking value for the connection recommendation is calculated. The utility value is a two-sided utility value that combines a prediction of a utility of the first entity and a prediction of a utility of a second entity with respect to a key performance indicator. A set of connection recommendations is communicated for presentation in an interactive user interface of a client device associated with the first entity in accordance with the adjusted ranking value of each connection recommendation.

Abstract: Described herein is a technique for generating personalized scores for a cohort of users of an online service, where the scores are for use in ranking connection recommendations, in the context of generating connection recommendations for a user of the online service. The technique involves using a linear programming (LP) problem solver to solve a multi-objective optimization problem formulated to incorporate competing objectives and specific constraints. The technique allows for personalizing recommendations scores, specifically, to ensure that infrequent users are receiving invitations to connect with other users, thereby increasing overall interaction and engagement.

Abstract: An example system includes a memory store of aggregate definitions. Each aggregate definition specifies a key value, a feature, a half-life value, and an aggregate operation metric to apply to a cross of the feature and the half-life value to generate aggregate metrics. The system also includes an aggregation engine that generates aggregate feature records from the input source based on the aggregate definitions and stores the aggregate feature records. An aggregate feature record includes an aggregate of the metric for the feature decayed over time using the half-life. The system also includes a query service that identifies, using the aggregate definitions, responsive aggregate feature records that satisfy parameters of a received request, applies the half-life to the responsive feature records, and provides the responsive feature records to a requester, the requester using the responsive feature records as input for a neural network.

Abstract: The disclosed embodiments provide a system for performing inference. During operation, the system obtains a graph containing nodes representing members of an online system, edges between pairs of nodes, and edge scores representing confidences in a type of relationship between the pairs of nodes. Next, the system performs a set of iterations that propagate a label for the type of relationship from a first subset of edges to remaining edges in the graph, with each iteration updating a probability of the label for an edge between a pair of nodes based on a subset of edge scores for a second subset of edges connected to one or both nodes in the pair and probabilities of the label for the second subset of edges. The system then performs one or more tasks in the online system based on the probability of the label for the edge.

Abstract: A method for making and assembling personal hygiene material containers whereby a cap and core are provided; a lid is disposed at the cap; the cap is then placed with the lid onto the core; situating a leakage prevention insert within the core; and situating a hygiene material elevator within the core. The leakage prevention insert possesses a circumference greater than that of the core such that upon insertion of the leakage prevention insert into the core an overhang is created along the entirety of the leakage prevention insert's circumference, where the overhang creates a complete and tight seal by creating an abutment with the inner wall of the core. The abutment thereby prevents the leakage of hygiene material during the manufacture fill process when the hygiene material is in its liquid form.

Abstract: Techniques for generating and leveraging heterogenous edges in an online connection network are provided. In one technique, a particular user is identified. The identification may be made in response to a computing device of the particular user requesting data from a particular system. For each entity type of multiple entity types: (1) a set of entities of the entity type is identified based on one or more attributes of the particular user; (2) a ranking of the set of entities is generated based on one or more criteria; and (3) a subset of the set of entities is selected and included in a final set of entities. The final set of entities includes entities from different entity types of the multiple entity types. The final set of entities is transmitted over a computer network to be presented concurrently on a computing device of the particular user.

Abstract: An example system includes a memory store of aggregate definitions. Each aggregate definition specifies a key value, an output store, a feature, a half-life value, and an aggregate operation metric to apply to a cross of the feature and the half-life value to generate aggregate metrics. The system also includes an aggregation engine that generates aggregate feature records from the input source based on the aggregate definitions and stores the aggregate feature records in the output store. An aggregate feature record includes an aggregate of the metric for the feature decayed over time using the half-life. The system also includes a query service that identifies, using the aggregate definitions, responsive aggregate feature records that satisfy parameters of a received request, applies the half-life to the responsive feature records, and provides the responsive feature records to a requester, the requester using the responsive feature records as input for a neural network.

Abstract: A real-time messaging platform allows advertiser accounts to pay to insert candidate messages into the message streams requested by account holders. To accommodate multiple advertisers, the messaging platform controls an auction process that determines which candidate messages are selected for inclusion in a requested account holder's message stream. Selection is based on a bid for the candidate message, the message stream that is requested, and a variety of other factors that vary depending upon the implementation. The process for selection of candidate messages generally includes the following steps, though any given step may be omitted or combined into another step in a different implementation: targeting, filtering, prediction, ranking, and selection.

Abstract: A real-time messaging platform allows advertiser accounts to pay to insert candidate messages into the message streams requested by account holders. To accommodate multiple advertisers, the messaging platform controls an auction process that determines which candidate messages are selected for inclusion in a requested account holder's message stream. Selection is based on a bid for the candidate message, the message stream that is requested, and a variety of other factors that vary depending upon the implementation. The process for selection of candidate messages generally includes the following steps, though any given step may be omitted or combined into another step in a different implementation: targeting, filtering, prediction, ranking, and selection.