The disclosed computer-implemented method for identifying privacy leakage information may include (1) identifying, at the computing device, at least one informative word in a digital text and (2) performing a security action that identifies privacy leakage information, where the security action includes (A) determining, for at least one identified informative word, a type of privacy leakage and a respective confidence score indicating a probability the identified informative word causes the type of privacy leakage, (B) determining, using the respective confidence score, a combined confidence score for each respective element within a level of detail to display, and (C) displaying, on a display device, the combined confidence score for each respective element within the level of detail to display. Various other methods, systems, and computer-readable media are also disclosed.

A method, system and computer program product is disclosed for fuzzy searching. The method, which may be performed by one or more processors, may comprise providing a first prefix tree data structure representing a first data set comprising a first plurality of strings, and providing a second prefix tree data structure representing a second data set comprising a second plurality of strings. The first and second prefix tree data structures may each comprise nodes representing each character and edges connecting prefix nodes to one or more suffix nodes to represent each subsequent character in the string. A search may be performed to identify all matches between the first and second plurality of strings and also approximate matches between the first and second plurality of strings within a maximum distance k, wherein the search comprises traversing the first prefix tree data structure using a depth-first search algorithm to identify matches and approximate matches in the second prefix tree data structure.

Aspects of the invention include generating a vector representation of a root node of the error based on a hierarchical topology of a computing system; generating a respective vector representations of each subject matter expert of a plurality of subject matter experts based at least in part on the hierarchical topology; selecting a subject matter expert based at least in part on the vector representation of root cause of the error; and uploading a diagnostic software to the computing system.

An approach is provided for tracking a single process instance flow. A request is received in a first system of a multi-system environment. Log files are pulled from systems with which the request interacts. Log entries are captured for the request. The log files are combined and flattened into a chronological log. A predictive model is built from an order of entries in the chronological log. Correlation keys in the entries of the chronological log are identified. Logs specifying processing of multiple ongoing requests are aggregated. A process instance of interest to a user is received. Instance specific log files are generated by deflattening the aggregated logs and by using a pattern detection algorithm that uses the predictive model and an alternate identifier algorithm that uses the correlation keys. One of the generated instance specific log files specifies a flow of the process instance of interest.

The invention provides decision tree based systems and methods for estimating the risk of acute coronary syndrome (ACS) in subjects suspect of having ACS. In particular, systems and methods are provided that employ additive decision tree based algorithms to process a subject's initial cardiac troponin I or T (cTnI or cTnT) concentration, a subject's cTnI or cTnT rate of change, and at least one of the following: the subject's age, the subject's gender, the subject's ECG value, the subject's hematology parameter value, to generate an estimate risk of ACS. Such risk stratification allows, for example, patients to be ruled in or rule out with regard to needing urgent treatment.

Examples described herein provide a computer-implemented method that includes receiving, by a processing device, the span of text, the span of text comprising a plurality of elements including at least an entity element and a temporal element. The method further includes organizing, by the processing device, the span of text as a natural language processing (NLP) parse tree. The method further includes traversing, by the processing device, the NLP parse tree by concatenating individual nodes of the span of text to generate the relation type between the entity element and the temporal element. The method further includes associating, by the processing device, the entity element, the relation type, and the temporal element together.

A knowledge management system includes a knowledge entry management configured to manage at least one knowledge entry in recording or managing knowledge, a knowledge entry attribute description management configured to record and manage an attribute description about the at least one knowledge entry. A term used in the attribute description is another knowledge entry or an attribute description of the other knowledge entry and a reference link to the other knowledge entry or the attribute description of the other knowledge entry is available, and a reference term-using document creation configured to create a document in which the knowledge entry is used as a term and that holds a reference link to the knowledge entry.

The disclosure provides a method and an apparatus for extracting content from a document, an electronic device, and a storage medium, which relates to the field of artificial intelligence (AI) technologies such as natural language processing (NLP), deep learning (DL), knowledge graph (KG). The detailed implementation scheme is: obtaining the document; performing anchor search on the document to obtain anchor information corresponding to the document; determining region information of content to be extracted based on the anchor information; and extracting the content to be extracted from the document based on the region information.

A computer-implemented method includes accessing, by a processing unit, an existing layer representing geospatial-temporal data at a selected timestamp. A first overview layer of the existing layer is generated by iteratively aggregating each cluster of cells of the existing layer into a corresponding lower-resolution cell of the first overview layer. The first overview layer therefore has a lower resolution than the existing layer. A query is received related to the geospatial-temporal data in the existing layer, and the query is processed with reference to the first overview layer.

Processor-implemented systems and methods are provided for generating clusters of technical documents. A method includes analyzing degrees of similarity among the technical documents using a hierarchical taxonomy code similarity model and a text clustering model. Clusters of the technical documents are generated based upon the analyzed degrees of similarity from the models.