Abstract: Techniques for automated chaos engineering may run an application on an internal production system. The internal production system is probed to determine an initial health status. If the initial health status indicates that the internal production system is in a stable state, a plurality of tests, such as functional tests or load tests, are conducted on the internal production system which create load at production levels. Chaos engineering actions are performed on the internal production system while the plurality of tests are being conducted. The chaos engineering actions cause system faults in the internal production system. The internal production system is probed after performing the chaos engineering actions to determine a later health status of the internal production system. An admin user is notified if the later health status of the internal production system is not a stable state after performing the chaos engineering actions.

Abstract: According to some aspect, a method includes receiving tuner channel data identifying a tuner-based television channel scanned by a television tuner of a display device, retrieving, from a media provider database, a list of tuner-based television channels associated with a location of the display device, identifying the tuner-based television channel from the list using the tuner channel data, retrieving, from the media provider database, program data that identifies a program broadcasted by the tuner-based television channel, generating channel and program data for the tuner-based television channel based on the tuner channel data and the program data, and transmitting the channel and program data to the display device, where the channel and program data is configured to cause the display device to display at least a portion of the program data in a user interface of a media application executable by the display device.

Abstract: A method of processing an image-charge/current signal representative of one or more ions undergoing oscillatory motion within an ion analyser apparatus. The method comprising obtaining a recording of the image-charge/current signal generated by the ion analyser apparatus in the time domain. By a signal processing unit, the method comprises determining a value for the period of a periodic signal component within the recorded signal. Then, the method includes truncating the recorded signal to provide a truncated signal having a duration substantially equal to an integer multiple of said period. A step of reconstructing a time-domain signal is done based on a selected one or more frequency-domain harmonic components of the truncated signal. Next, the method determines a magnitude of the reconstructed time-domain signal and therewith calculating a value representative of the charge of a said ion undergoing oscillatory motion within the ion analyser apparatus.

Abstract: A method of processing an image-charge/current signal representative of one or more ions undergoing oscillatory motion within an ion analyser apparatus, the method comprising obtaining a recording of the image-charge/current signal generated by the ion analyser apparatus in the time domain. By a signal processing unit, the method includes selecting N (where N is an integer>1) separate values (OPn, where n=1 to N; N?M) of the frequency-domain spectrum of the image-charge/current signal each from amongst a plurality of spectral peaks which include a harmonic peak associated with a target ion. By solving a system of equations: OP n = ? m = 1 N ? n ? m × TP m , for ? n = 1 ? to ? N ; N ? M where ?nm are coefficients and TPm are corrected values of the spectrum, the charge of the target ion is determined based on a magnitude of a corrected value(s) (TPm) associated with that ion.

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: Proton conductive membrane includes a proton selective layer of 80-100% carbon with sp2 hybridization having a thickness of 0.3-100 nm, with 0-20% of hydrogen, oxygen, nitrogen and sp3 carbon; wherein the sp2 carbon is in a form of graphene-like material; the proton selective layer having a plurality of pores formed by any of 7, 8, 9 or 10 sp2 carbon cycles or a combination thereof, with the pores having an effective diameter of up to 0.6 nm; an ionomeric polymer layer on the proton selective layer. Total thickness of the proton conductive membrane is less than 50 microns. The ionomeric polymer is PFSA (perfluorinated sulfonic acid), PVP (polyvinylpyrrolidone) or PVA (poly vinyl alcohol) with iodide or bromide counterion dissolved inside. The graphene-like material is CVD graphene or reduced graphene oxide (rGO). A D to G Raman band ratio of the membrane is more than 0.1.

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: Proton conductive membrane includes a proton selective layer of 80-100% carbon with sp2 hybridization having a thickness of 0.3-100 nm, with 0-20% of hydrogen, oxygen, nitrogen and sp3 carbon; wherein the sp2 carbon is in a form of graphene-like material; the proton selective layer having a plurality of pores formed by any of 7, 8, 9 or 10 sp2 carbon cycles or a combination thereof, with the pores having an effective diameter of up to 0.6 nm; an ionomeric polymer layer on the proton selective layer. Total thickness of the proton conductive membrane is less than 50 microns. The ionomeric polymer is PFSA (perfluorinated sulfonic acid), PVP (polyvinylpyrrolidone) or PVA (poly vinyl alcohol) with iodide or bromide counterion dissolved inside. The graphene-like material is CVD graphene or reduced graphene oxide (rGO). A D to G Raman band ratio of the membrane is more than 0.1.

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.