Abstract: Technologies are described for identifying features that can be used to predict missing attribute values. For example, a set of structured data can be received comprising a plurality of features and one or more labels. The set of structured data can be pre-processed, comprise applying one or more cleaning policies to produce a set of pre-processed features. The set of pre-processed features can be filtered using correlation-based filtering that uses one or more correlation estimation techniques to remove at least some highly correlated features. The correlation-based filtering can produce a set of filtered features. Feature subset selection can be performed comprising applying machine learning algorithms to the set of filtered features to determine relative importance among the set of filtered features. Based on the relative importance, a subset of the set of filtered features can be determined. The subset of the set of filtered features can be output.

Abstract: According to a disclosed embodiment, data analysis is secured with a microservice architecture and data anonymization in a multitenant application. Tenant data is received by a first microservice in a multitenant application. The tenant data is isolated from other tenant data in the first microservice and stored separately from other tenant data in a tenant database. The tenant data is anonymized in the first microservice and thereafter provided to a second microservice. The second microservice stores the anonymized tenant data in an analytics database. The second microservice, upon request, analyzes anonymized tenant data from a plurality of tenants from the analytics database and provides an analytics result to the first microservice.

Abstract: Disclosed herein are system, method, and computer program product embodiments for classifying data objects using machine learning. In an embodiment, an artificial neural network may be trained to identify explained variable values corresponding to data object attributes. For example, the explained variables may be a category and a subcategory with the subcategory having a hierarchical relationship to the category. The artificial neural network may then receive a data record having one or more attribute values. The neural network may then identify a first and second explained variable value corresponding to the one or more attribute values based on the trained neural network model. The first and second explained variable values may then be associated with the data record. For example, if the data record is stored in a database, the record may be updated to include the first and second explained variable values.

Abstract: Technologies are described for identifying features that can be used to predict missing attribute values. For example, a set of structured data can be received comprising a plurality of features and one or more labels. The set of structured data can be pre-processed, comprise applying one or more cleaning policies to produce a set of pre-processed features. The set of pre-processed features can be filtered using correlation-based filtering that uses one or more correlation estimation techniques to remove at least some highly correlated features. The correlation-based filtering can produce a set of filtered features. Feature subset selection can be performed comprising applying machine learning algorithms to the set of filtered features to determine relative importance among the set of filtered features. Based on the relative importance, a subset of the set of filtered features can be determined. The subset of the set of filtered features can be output.

Abstract: According to a disclosed embodiment, data analysis is secured with a microservice architecture and data anonymization in a multitenant application. Tenant data is received by a first microservice in a multitenant application. The tenant data is isolated from other tenant data in the first microservice and stored separately from other tenant data in a tenant database. The tenant data is anonymized in the first microservice and thereafter provided to a second microservice. The second microservice stores the anonymized tenant data in an analytics database. The second microservice, upon request, analyzes anonymized tenant data from a plurality of tenants from the analytics database and provides an analytics result to the first microservice.

Abstract: According to a disclosed embodiment, data analysis is secured with a microservice architecture and data anonymization in a multitenant application. Tenant data is received by a first microservice in a multitenant application. The tenant data is isolated from other tenant data in the first microservice and stored separately from other tenant data in a tenant database. The tenant data is anonymized in the first microservice and thereafter provided to a second microservice. The second microservice stores the anonymized tenant data in an analytics database. The second microservice, upon request, analyzes anonymized tenant data from a plurality of tenants from the analytics database and provides an analytics result to the first microservice.

Abstract: A method for extracting uniform features of at least one object, from a point cloud of an environment, includes acquiring the point cloud associated with the environment having the at least one object, wherein the point cloud is associated with a volume comprising a plurality of points; segmenting the point cloud into at least one sub-volume corresponding to each of the at least one object; applying a non-uniform transform on each of the plurality of points corresponding to each of the at least one sub-volume, to obtain a transform coefficient for each of the plurality of points; and selecting a subset of the plurality of transform coefficients as the extracted uniform features of the at least one object within the environment.

Abstract: Disclosed herein are system, method, and computer program product embodiments for classifying a new record. An embodiment operates by receiving a dataset unique to a user, wherein the dataset includes a plurality of records separate from the new record, and receiving a dataset schema. Thereafter, the dataset is validated based on the dataset schema. Subsequently, a request for creating a machine learning model based on a selected model template and dataset is received. After creating the custom machine learning model, a request for classifying the new record based on the created machine learning model is received. Upon determining the classification of the new record based on the custom machine learning model, the classification for the new record is outputted to the user.

Abstract: Disclosed herein are system, method, and computer program product embodiments for classifying data objects using machine learning. In an embodiment, an artificial neural network may be trained to identify explained variable values corresponding to data object attributes. For example, the explained variables may be a category and a subcategory with the subcategory having a hierarchical relationship to the category. The artificial neural network may then receive a data record having one or more attribute values. The neural network may then identify a first and second explained variable value corresponding to the one or more attribute values based on the trained neural network model. The first and second explained variable values may then be associated with the data record. For example, if the data record is stored in a database, the record may be updated to include the first and second explained variable values.

Abstract: A method for extracting uniform features of at least one object, from a point cloud of an environment, includes acquiring the point cloud associated with the environment having the at least one object, wherein the point cloud is associated with a volume comprising a plurality of points; segmenting the point cloud into at least one sub-volume corresponding to each of the at least one object; applying a non-uniform transform on each of the plurality of points corresponding to each of the at least one sub-volume, to obtain a transform coefficient for each of the plurality of points; and selecting a subset of the plurality of transform coefficients as the extracted uniform features of the at least one object within the environment.

Abstract: A computing device hosts a graphical user interface (GUI) of a computer application, the computer application being run on a backend computing platform accessible to the computing device via a network. The GUI includes multiple models in a Model-View-Controller (MVC) pattern, an eventing mechanism, and a model synchronizer. Each model in the GUI represents one or more application objects of the computer application. The eventing mechanism generates an application object change event when an application object of one of the multiple models in the GUI is changed to a new state. The model synchronizer listens to the generated application object change event, retrieves the new state of the application object, and locally updates other models of the multiple models in the GUI that also represent the application object with the new state of the application object.

Abstract: Various embodiments are provided which relate to the field of image signal processing, specifically relating to the generation of a depth-view image of a scene from a set of input images of a scene taken at different cameras of a multi-view imaging system. A method comprises obtaining a frame of an image of a scene and a frame of a depth map regarding the frame of the image. A minimum depth and a maximum depth of the scene and a number of depth layers for the depth map are determined. Pixels of the image are projected to the depth layers to obtain projected pixels on the depth layers; and cost values for the projected pixels are determined. The cost values are filtered and a filtered cost value is selected from a layer to obtain a depth value of a pixel of an estimated depth map.

Abstract: A method for internationalization of a computer application being designed and developed as cloud application in a platform-as-a-service (PaaS) environment includes disposing a translatable texts table in a data layer of the computer application as a common source of translatable texts for all layers of the computer application. The method further includes disposing a text string translation service in a logic layer of the computer application. to expose the translatable texts table disposed in the data layer to a presentation layer of the computer application.

Abstract: A method of processing an image charge/current signal representative of trapped ions undergoing oscillatory motion. The method includes: identifying a plurality of fundamental frequencies potentially present in the image charge/current signal based on an analysis of peaks in a frequency spectrum corresponding to the image charge/current signal in the frequency domain, wherein each candidate fundamental frequency falls in a frequency range of interest; deriving a basis signal for each candidate fundamental frequency using a calibration signal; and estimating relative abundances of ions corresponding to the candidate fundamental frequencies by mapping the basis signals to the image charge/current signal. At least one candidate fundamental frequency is calculated using a frequency associated with a peak that falls outside the frequency range of interest and that has been determined as representing a second or higher order harmonic of the candidate fundamental frequency.

Abstract: Data is collected through a user interface application. The collected data is associated with a plurality of first events that define impacting events, and a plurality of second events that defined impacted events. The data includes relations, where a relation from the data associates a set of first events from the plurality of first events with a set of second events from the plurality of second events. A relation from the data represents a claimed association between impacting events and impacted events within a given evaluation scenario. The collected data is stored at a data log and is evaluated to determine occurrence of a set of pairs of events. A pair includes an event of the first event type and an event of the second event type. A set of causality measures corresponding to the pairs of events within a relation is computed.

Abstract: Techniques are described for providing a non-blocking application object framework allowing parallelization of operation and function calls throughout an application executing within the framework. In one example, a dependency model associated with an application in a non-blocking application object framework is identified, where the application is associated with a plurality of operations and the dependency model defines at least one dependency between at least two of the operations. At runtime of the non-blocking application object framework, the identified dependency model is interpreted. An optimized execution plan of the application is automatically generated in the non-blocking application object framework based on the interpreted dependency model, wherein at least a first portion of the operations of the application are optimized in a sequential execution order based on dependencies defined in the dependency model.

Abstract: According to a disclosed embodiment, data analysis is secured with a microservice architecture and data anonymization in a multitenant application. Tenant data is received by a first microservice in a multitenant application. The tenant data is isolated from other tenant data in the first microservice and stored separately from other tenant data in a tenant database. The tenant data is anonymized in the first microservice and thereafter provided to a second microservice. The second microservice stores the anonymized tenant data in an analytics database. The second microservice, upon request, analyzes anonymized tenant data from a plurality of tenants from the analytics database and provides an analytics result to the first microservice.

Abstract: A method of processing an image charge/current signal representative of trapped ions undergoing oscillatory motion. The method includes: identifying a plurality of fundamental frequencies potentially present in the image charge/current signal based on an analysis of peaks in a frequency spectrum corresponding to the image charge/current signal in the frequency domain, wherein each candidate fundamental frequency falls in a frequency range of interest; deriving a basis signal for each candidate fundamental frequency using a calibration signal; and estimating relative abundances of ions corresponding to the candidate fundamental frequencies by mapping the basis signals to the image charge/current signal. At least one candidate fundamental frequency is calculated using a frequency associated with a peak that falls outside the frequency range of interest and that has been determined as representing a second or higher order harmonic of the candidate fundamental frequency.

Abstract: Techniques are described for providing a non-blocking application object framework allowing parallelization of operation and function calls throughout an application executing within the framework. In one example, a dependency model associated with an application in a non-blocking application object framework is identified, where the application is associated with a plurality of operations and the dependency model defines at least one dependency between at least two of the operations. At runtime of the non-blocking application object framework, the identified dependency model is interpreted. An optimized execution plan of the application is automatically generated in the non-blocking application object framework based on the interpreted dependency model, wherein at least a first portion of the operations of the application are optimized in a sequential execution order based on dependencies defined in the dependency model.

Abstract: A computing device hosts a graphical user interface (GUI) of a computer application, the computer application being run on a backend computing platform accessible to the computing device via a network. The GUI includes multiple models in a Model-View-Controller (MVC) pattern, an eventing mechanism, and a model synchronizer. Each model in the GUI represents one or more application objects of the computer application. The eventing mechanism generates an application object change event when an application object of one of the multiple models in the GUI is changed to a new state. The model synchronizer listens to the generated application object change event, retrieves the new state of the application object, and locally updates other models of the multiple models in the GUI that also represent the application object with the new state of the application object.