Abstract: Described are examples for information discovery using domain-specific term matching, including obtaining, for a search session related to a domain, a set of search strings used for searching the domain for information discovery during the search session, identifying, for the search session, a string of the set of search strings as a search string hit and the remaining strings in the set of search strings as search string misses, and correlating, into a set of domain-specific search string misses, the remaining strings in the set of search strings with additional remaining search strings from one or more other search session related to the domain that have the same string identified as the search string hit.

Abstract: The described technology provides detection of a regime shift in streaming data by generating a curve fit of the streaming data representing an attribute over a duration to each of a plurality of probability density functions, scoring conformity of each curve fit to yield a plurality of fit scores, selecting a first duration probability density function among the plurality of probability density functions based on satisfaction of a probability density function fit condition by the fit score corresponding to the first duration probability density function, determining a probability density function change between the selected first duration probability density function and a second duration probability density function, wherein the second duration probability density function is selected for streaming data representing the attribute over a different duration, and indicating detection of the regime shift based on the determined probability density function change satisfying a shift condition.

Abstract: A sample of data, including a risk factor, is selected by a machine learning (ML) model of an extreme value theory (EVT) mechanism. A threshold is determined by the ML model based on the risk factor, an outlier score is generated for the sample, and the outlier score is compared to the threshold. The sample is identified as anomalous based on the generated outlier score being greater than the threshold. A schema comprising results of an investigation into the sample and the risk factor is updated based on the received schema.

Abstract: A subset of data that includes a feature may be selected from a dataset. Parameters from the selected subset of data are determined and an extreme value theory (EVT) algorithm is implemented to determine a probability value for the feature based at least in part on the determined parameters. Based on the determined probability value for the feature, an outlier score is generated for the feature. Based on the outlier score being above a threshold, the subset is identified as anomalous.

Abstract: Methods, systems, apparatuses, and computer-readable storage mediums are descried for identifying a similarity between queries. An intermediate representation generator receives a set of queries from a repository, each query in the set of queries having generated a corresponding set of data stored in a data store. An intermediate representation is generated for each query, where the intermediate representation is characterized by a feature associated with text specified in the query. A similarity determiner determines similarity scores between pairs of intermediate representations. A pair of intermediate representations with a similarity score above a threshold is identified. An indication is generated that sets of data corresponding to queries corresponding to the intermediate representations are overlapping.

Abstract: Described are examples for information discovery using domain-specific term matching, including obtaining, for a search session related to a domain, a set of search strings used for searching the domain for information discovery during the search session, identifying, for the search session, a string of the set of search strings as a search string hit and the remaining strings in the set of search strings as search string misses, and correlating, into a set of domain-specific search string misses, the remaining strings in the set of search strings with additional remaining search strings from one or more other search session related to the domain that have the same string identified as the search string hit.

Abstract: Example aspects include techniques for anomaly detection via sparse judgmental samples. These techniques may include generating, via lexical analysis, a plurality of tokens from a textual representation of a machine learning (ML) model and generating, via a parser, based on the plurality of tokens, an abstract syntax tree (AST) corresponding to the ML model. In addition, the techniques may include identifying a data dependency of the ML model based on an AST node within the AST, the AST node corresponding to a data source and the data dependency indicating the ML model depends on the data source. Further, the techniques may include detecting a potential issue associated with the data source, and transmitting, based on the data dependency, an alert notification in response to the potential issue.

Abstract: The invention is related to a system and method for determining a physiological condition. The system includes a processor configured to receive, from a first sensor, skin temperature measurements during a first period of time, wherein the first sensor is detachably and thermally coupleable to a user's skin, receive from a second sensor, measurements of ambient temperature during the first period of time, adjust the skin temperature measurements received from the first sensor using the ambient temperature measurements received from the second sensor to reduce ambient influence, receive one or more stored temperature measurements measured for at least a second time period, calculate representative temperature information for the first period of time based on the adjust skin temperature measurements from the first period of time and stored temperature measurements and display the representative temperature information.

Abstract: Techniques for implementing blind spots into neural network models are disclosed. In some example embodiments, a computer-implemented method comprises obtaining an image captured within a field of view of an image capture device and comprising an object of a particular type occupying a particular position within the field of view, and determining a confidence value for the object based on the particular position of the object using a neural network model. The confidence value represents a likelihood that the object is an object of interest, and the neural network model is trained to generate lower confidence values for objects of the particular type when the objects of the particular type are occupying the particular position than when the objects of the particular type are not occupying the particular position.

Abstract: Techniques for implementing blind spots into neural network models are disclosed. In some example embodiments, a computer-implemented method comprises obtaining an image captured within a field of view of an image capture device and comprising an object of a particular type occupying a particular position within the field of view, and determining a confidence value for the object based on the particular position of the object using a neural network model. The confidence value represents a likelihood that the object is an object of interest, and the neural network model is trained to generate lower confidence values for objects of the particular type when the objects of the particular type are occupying the particular position than when the objects of the particular type are not occupying the particular position.

Abstract: The invention is related to a system and method for determining a physiological condition. The system includes a processor configured to receive, from a first sensor, skin temperature measurements during a first period of time, wherein the first sensor is detachably and thermally coupleable to a user's skin, receive from a second sensor, measurements of ambient temperature during the first period of time, adjust the skin temperature measurements received from the first sensor using the ambient temperature measurements received from the second sensor to reduce ambient influence, receive one or more stored temperature measurements measured for at least a second time period, calculate representative temperature information for the first period of time based on the adjust skin temperature measurements from the first period of time and stored temperature measurements and display the representative temperature information.