Automated methods and systems described herein are directed to identifying potential root causes of a problem in a data center. Methods and systems receipt an alert or other notification of a problem occurring in a data center and a time when the problem was noticed. A search window is created based on the time and a stream of log messages generated in the search window is converted into a time dependent metric. An anomaly detection technique is applied to the metric to determine a start time of a problem. Logging events and key phrases in the log messages are identified in the search window and presented as potential root causes of the problem. The potential root cause may then be used by system administrators and/or tenants to diagnose the problem and execute remedial measures to correct the problem.

Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.

Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.

Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.

The present invention provides a method, computer program product, and system of generating predicted reactions of a user. In some embodiments, the method, computer program product, and system include receiving an intelligence data store, receiving a current data object with a current query and at least one knowledge graph, identifying one or more patterns in the at least one knowledge graph, comparing using a deep neural net, the previous queries and associated one or more patterns with the current query and identified one or more patterns of the current data object, classifying the plurality data objects from the intelligence data store based on a closeness of the current query and identified one or more patterns with each of the previous queries and associated one or more patterns in the intelligence data store, and identifying, by the classification engine, potential dispositions based on the classification of the plurality of data objects.

An intelligent computer platform to introduce adversarial training to natural language processing (NLP). An initial training set is modified with synthetic training data to create an adversarial training set. The modification includes use of natural language understanding (NLU) to parse the initial training set into components and identify component categories. One or more paraphrase terms are identified with respect to the components and component categories, and function as replacement terms. The synthetic training data is effectively a merging of the initial training set with the replacement terms. As input is presented, a classifier leverages the adversarial training set to identify the intent of the input and to output a classification label to generate accurate and reflective response data.

Embodiments of the specification provide an image search method, an apparatus, and a device. The method includes: obtaining an input image associated with an image search, wherein the input image includes a plurality of first text blocks; selecting a to-be-processed image from a target database, wherein the to-be-processed image includes a plurality of second text blocks; and generating a first graph structural feature based on the plurality of first text blocks; generating a second graph structural feature based on the plurality of second text blocks; determining that the first graph structural feature and the second graph structural feature satisfy a condition; and in response to determining that the first graph structural feature and the second graph structural feature satisfy the condition, outputting the to-be-processed image as a search result.

A method, an electronic device and computer readable medium for entity-relationship embeddings using automatically generated entity graphs instead of a traditional knowledge graph are provided. The method includes receiving, by a processor, an input text. The method also includes identifying a primary entity, a secondary entity and a context from the input text, wherein the context comprises a relationship between the primary entity and the secondary entity. The method additionally includes generating, by the processor, an entity context graph based on the primary entity, the secondary entity, and the context by: extracting, from the context, one or more text segments comprising a plurality of words describing one or more additional relationships between the primary entity and the secondary entity, and generating a plurality of context triples from the one or more text segments, each of the plurality of context triples defining a respective relationship between primary entity and the secondary entity.

Introduced here are computer programs and associated computer-implemented techniques for finding the correspondence between sets of graphical elements that share a similar structure. In contrast to conventional approaches, this approach can leverage the similar structure to discover how two sets of graphical elements are related to one another without the relationship needing to be explicitly specified. To accomplish this, a graphics editing platform can employ one or more algorithms designed to encode the structure of graphical elements using a directed graph and then compute element-to-element correspondence between different sets of graphical elements that share a similar structure.

A method for extracting information from a table includes steps as follows. Characters of a table are extracted. The characters are merged into n-gram characters. The n-gram characters are merged into words and text lines through a two-stage GNN mode. The two-stage GNN mode comprises sub steps as: spatial features, semantic features, CNN image features are extracted from a target source; a first GNN stage is processed to output graph embedding spatial features from the spatial features; and a second GNN stage is processed to output graph embedding semantic features and graph embedding CNN image features from the semantic features and the CNN image features, respectively. The text lines are merged into cells. The cells are grouped into rows, columns, and key based on one or more adjacency matrices, a row relationship among the cells, a column relationship among the cells, and a key-value relationship among the cells.