Abstract: Aspects of the present disclosure relate to multi-spectrum visual object recognition. A first image corresponding to visible light and a second image corresponding to invisible light with respect to an object can be obtained. A first contour of the object can be identified based on the first image. A second contour of the object can be identified based on the second image. The first contour of the object and the second contour of the object can be integrated to generate a multi-spectrum contour of the object. The object can be recognized using the multi-spectrum contour of the object.

Abstract: Embodiments for locating performance hot spots include collecting sample data having instruction addresses, the sample data being for a neural network model and determining instructions in the instruction addresses that are performance hot spots. A listing file is used to map the instructions of the sample data that are performance hot spots to locations in a lower-level intermediate representation. A mapping file is used to map the locations of the lower-level intermediate representation that are performance hot spots to operations in one or more higher-level representations, one or more of the operations corresponding to the performance hot spots, the mapping file being generated from compiling the neural network model.

Abstract: A method includes: obtaining, by a database server, security policies associated with a database hosted by the database server; obtaining, by the database server, first database data associated with the database; creating, by the database server, a knowledge distillation model using the security policies and the first database data; obtaining, by the database server, second database data associated with the database; creating, by the database server, a classification model using the second database data and an output of the knowledge distillation model; receiving, by the database server, a client database request to the database; determining, by the database server, a new security policy rule set using the classification model; and applying, by the database server, the new security policy rule set to the client database request.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: receiving an incoming query statement, wherein the incoming query statement comprises a query statement expression that includes an input variable; predicting an input variable value associated to the input variable; selecting an access path for runtime execution of the query statement in dependence on the predicted input variable value; and performing runtime execution of the query statement using the selected access path.

Abstract: In an approach, a processor extracts an audio signal from a video clip. A processor converts the audio signal into a text sequence. A processor selects a first set of keywords from the text sequence, the first set of keywords corresponding to a first audio segment of the audio signal. A processor tags a target video segment of the video clip with the first set of keywords, the target video segment corresponding to the first audio segment.

Abstract: Aspects of the invention include converting an artificial intelligence (AI) model generated in a first framework to a uniform exchange formatted model by engaging a master table to retrieve instructions for converting from the AI model to the uniform exchange formatted model in accordance with the first framework. The uniform exchange formatted model in compiled by engaging the master table to retrieve instructions for compiling the uniform exchange formatted model in accordance with the first framework. Data is received as an input to the compiled uniform exchange formatted model and an output is generated by engaging the master table to retrieve instructions for generating the output in accordance with the first framework.

Abstract: A computer-implemented method to estimate computing cost of database queries. The method includes receiving a query. The method also includes generating one or more access plans for the query. The method further includes dynamically generating a learning model based on each of the generated access plans. The method includes estimating, by the learning model and for each of the one or more access plans, a predicted cost to execute the query. The method includes selecting, based on the predicted cost, a first access path associated with a lowest cost of the predicted costs and executing the query according the selected access path.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: An image processing method and apparatus, and a storage medium are provided. The method includes: obtaining a first image and a second image of a to-be-authenticated object, where the first image is captured by a first camera module, and the second image is captured by at least one second camera module; comparing the first image with image data in a target library for identity authentication, to obtain a first authentication result; and in response to that the first authentication result is authentication failure, performing joint authentication on the first image and the second image, and determining the identity of the to-be-authenticated object according to a second authentication result of the joint authentication.

Abstract: Aspects of the invention include generating a plurality of predictions that each define a plurality of future inputs for a model. A deviation curve is generated by determining a distance between each prediction of the plurality of predictions and a respective known data point of a plurality of known data points. One or more points in the deviation curve are sampled and the sampled points are compared to a low threshold and a high threshold. A judgement is determined for each prediction to determine whether the respective prediction will be accepted or denied as an input to the model. The future inputs for the model are modified based on the judgments.

Abstract: An automated method for determining a complexity of a task. The method includes extracting data from the plurality of historical support tickets to generate training data. The method trains a complexity model to predict a complexity value of a task associated with a support ticket using the training data. The method predicts, using the complexity model, the complexity value of a new task associated with a new support ticket.

Abstract: An approach to optimize performance for large scale inference models. Data in the form of images is received from sensors such as cameras. The data is processed to generate data tags associated with the context of the image and portion the images. Model tags are generated based on data characteristics or user input. The tags and their associated data are added to a time-based queue for delivery to the appropriate inference models. Based on the embedded delivery time and frequency, the portioned images are delivered to the appropriate inference models.

Abstract: Synching multiple streams in a complex enterprise product by collecting and analyzing stream dependency data. Collection and analysis of data for large scale and complex enterprise results in a multi-dimensional relationship diagram that highlights the interconnected dependencies of the streams. This allows enterprise software users to more easily determine and select which stream (or streams) will help the user to perform a given task.

Abstract: The present invention discloses a method for measuring an actual temperature of a flame by using all information of a radiation spectrum and a measurement system thereof. The method includes: conducting more theoretical data processing by using energy level structure correction, wherein all information of the radiation spectrum can be used; and by way of a keyboard input manner or a data transmission input manner, acquiring an energy level structure correction parameter, and finally acquiring a more accurate actual temperature value of a measured flame. The method effectively overcomes a defect that the true temperature of the flame can be obtained by only conducting radiance correction through data processing with great calculations when adpted multi-spectral temperature measurement method.

Abstract: Techniques for multistage process model training are described herein. Another aspect includes determining a first gray level histogram corresponding to a first input image. Another aspect includes determining a second gray level histogram corresponding to a second input image. Another aspect includes determining a set of change values, each change value corresponding to a change in a respective gray level from the first gray level histogram to the second gray level histogram. Another aspect includes comparing each change value of the set of change values to a threshold. Another aspect includes, based on determining that a first change value of the set of change values is higher than the threshold, adding a first gray level corresponding to the first change value to a hot zone of the second input image. Another aspect includes training a model using the hot zone of the second input image.

Abstract: A machine translation system, a ChatOps system, a method for a context-aware language machine identification, and computer program product. One embodiment of the machine translation system may include a density calculator. The density calculator may be adapted to calculate a part of speech (POS) density for a plurality of word tokens in an input text, calculate a knowledge density for the plurality of word tokens, and calculate an information density for the plurality of word tokens using the POS density and the knowledge density. In some embodiments, the machine translation system may further comprise a sememe attacher and a context translator.

Abstract: A computer-implemented method for determining influence of applications on system performance includes collecting, by a processor, for several applications that are executing on a computing system, respective instrumentation data during multiple time-segments. The method further includes determining, for each of the applications, a performance value and a robustness value for each of the time-segments based on the respective instrumentation data. Further, using the performance value and robustness value for each time-segment, multiple health-waveforms are generated, where a health-waveform is generated for each respective application. The method further includes determining, by the processor, an influence-factor of a first application on a second application, the first application and the second application are executing on the computing system.