Abstract: A system and method for generating product analytics insights for machine learning platforms is disclosed. The system and method can receive a log file including data derived while executing a software program. Software packages and the package sub-modules logged in the log file may be categorized into a plurality of package categories. A notebook in which the software code is written, and identified in the log file, may be categorized into a notebook category. A consumable insight may be generated based on: the categorized software packages and package sub-modules, and the categorized notebook. A natural language summary may be generated describing the consumable insight. The natural language summary may be transmitted for display on a graphical user interface (GUI) in response to a user query.

Abstract: Systems and methods include intercepting traffic at a mobile device via a connector application executing on the mobile device, the traffic originating from one or more applications on the mobile device and destined for one or more resources located in one of a public cloud, a private cloud, and an enterprise network; detecting one or more Virtual Private Network (VPN) profiles associated with the traffic, wherein the one or more VPN profiles are assigned to the traffic by the operating system of the mobile device; and forwarding the traffic to a cloud-based system via one or more tunnels based on the one or more VPN profiles detected in the traffic.

Abstract: The present application relates to managing memory of a KLL sketch for a streaming set. A computer system allocates an initial amount of memory from a computer memory to a buffer of a KLL sketch data structure based on a sparse sketch size independent of a data type of the streaming set. The system allocates an incremental amount of memory greater than or equal to the initial amount of memory from the computer memory to the buffer in response to a size of the streaming set exceeding a supported stream size of the KLL sketch data structure. The system determines a number of levels of the KLL sketch data structure based on a total allocated amount of memory and a size of the data type of the streaming set. The system adds new elements from the streaming set to the KLL sketch data structure based on the number of levels.

Abstract: The present application relates to managing memory of a KLL sketch for a streaming set. A computer system allocates an initial amount of memory from a computer memory to a buffer of a KLL sketch data structure based on a sparse sketch size independent of a data type of the streaming set. The system allocates an incremental amount of memory greater than or equal to the initial amount of memory from the computer memory to the buffer in response to a size of the streaming set exceeding a supported stream size of the KLL sketch data structure. The system determines a number of levels of the KLL sketch data structure based on a total allocated amount of memory and a size of the data type of the streaming set. The system adds new elements from the streaming set to the KLL sketch data structure based on the number of levels.

Abstract: The present disclosure provides an intermittent tape (100) disposed of around a pair of conductors. The intermittent tape (100) has a top dielectric layer (102), a bottom dielectric layer (106) and a conductive layer (104). The conductive layer (104) is sandwiched between the top dielectric layer (102) and the bottom dielectric layer (106). The conductive layer (104) includes conductive segments (108) and non-conductive segments (110). The non-conductive segments (110) are defined by an absence of the conductive segments (108). The conductive segments (108) and the non-conductive segments (110) are arranged alternatingly. A width of the non-conductive segments (110) between the conductive segments (108) is constant.

Abstract: A method for dynamic storage object configuration in a datacenter is provided. Embodiments include determining a number of fault domains in a storage cluster that have sufficient storage capacity for creating a storage object. Embodiments include applying a dynamic fault tolerance policy to the number of fault domains that have sufficient capacity for creating the storage object in order to determine a number of host failures to tolerate for the storage object, the dynamic fault tolerance policy specifying a manner of determining, for any respective storage object, a respective number of host failures to tolerate for storing the respective storage object in a respective storage cluster based on at least a respective number of fault domains of the respective storage cluster. Embodiments include implementing the storage object on the storage cluster based on the number of host failures to tolerate for the storage object.

Abstract: The disclosure generally relates to methods and systems for enabling human robot interaction by cognition sharing which includes gesture and audio. Conventional techniques that use the gestures and the speech, require extra hardware setup and are limited to navigation in structured outdoor driving environments. The present disclosure herein provides methods and systems that solves the technical problem of enabling the human robot interaction with a two-step approach by transferring the cognitive load from the human to the robot. An accurate shared perspective associated with the task is determined in the first step by computing relative frame transformations based on understanding of navigational gestures of the subject. Then, the shared perspective transformed to the robot in the field view of the robot. The transformed shared perspective is then given to a language grounding technique in the second step, to accurately determine a final goal associated with the task.

Abstract: The disclosure herein generally relates to the field of autonomous navigation, and, more particularly, to a diverse trajectory proposal for autonomous navigation. The embodiment discloses a hierarchical network based diverse trajectory proposal for autonomous navigation. The hierarchical 2-stage neural network architecture maps the perceived surroundings to diverse trajectories in the form of trajectory waypoints, that an autonomous navigation system can choose to navigate/traverse. The first stage of the disclosed hierarchical 2-stage Neural Network architecture is a Trajectory Proposal Network which generates a set of diverse traversable regions in an environment which can be occupied by the autonomous navigation system in the future. The second stage is a Trajectory Sampling network which predicts a fine-grained trajectory/trajectory waypoint over the diverse traversable regions proposed by Trajectory Proposal Network.

Abstract: A method for dynamic storage object configuration in a datacenter is provided. Embodiments include determining a number of fault domains in a storage cluster that have sufficient storage capacity for creating a storage object. Embodiments include applying a dynamic fault tolerance policy to the number of fault domains that have sufficient capacity for creating the storage object in order to determine a number of host failures to tolerate for the storage object, the dynamic fault tolerance policy specifying a manner of determining, for any respective storage object, a respective number of host failures to tolerate for storing the respective storage object in a respective storage cluster based on at least a respective number of fault domains of the respective storage cluster. Embodiments include implementing the storage object on the storage cluster based on the number of host failures to tolerate for the storage object.

Abstract: The disclosure generally relates to methods and systems for enabling human robot interaction by cognition sharing which includes gesture and audio. Conventional techniques that use the gestures and the speech, require extra hardware setup and are limited to navigation in structured outdoor driving environments. The present disclosure herein provides methods and systems that solves the technical problem of enabling the human robot interaction with a two-step approach by transferring the cognitive load from the human to the robot. An accurate shared perspective associated with the task is determined in the first step by computing relative frame transformations based on understanding of navigational gestures of the subject. Then, the shared perspective transformed to the robot in the field view of the robot. The transformed shared perspective is then given to a language grounding technique in the second step, to accurately determine a final goal associated with the task.

Abstract: Embodiments herein achieve a method for managing display of content on one or more secondary device by a primary device. The method includes causing, by the primary device, to display a user interface comprising the content and a graphical representation of the at least one secondary device connected to the primary device. Further, the method includes detecting by the primary device an input performed to select at least one portion of the content to be displayed on the at least one secondary device. Further, the method includes transmitting by the primary device the at least one selected portion of the content to the secondary device. One or more selected portion is dynamically modified based on information about the at least one secondary device.

Abstract: The disclosure provides an approach for recommending and installing a patch onto a first computing device, comprising: parsing a machine-readable document for a property variable and a possible value of the property variable, wherein the combination of the property variable and the possible value represents a criticality of installing the patch; transmitting a request to the first computing device for a first value of the first computing device, the first value representing a state of the first computing device corresponding to the property variable; receiving the first value; comparing the possible value to the first value; based on the comparing, performing a first determination of the criticality of installing the patch onto the first computing device; and based on the criticality of installing the patch, installing the patch onto the first computing device.

Abstract: The disclosure herein generally relates to the field of autonomous navigation, and, more particularly, to a diverse trajectory proposal for autonomous navigation. The embodiment discloses a hierarchical network based diverse trajectory proposal for autonomous navigation. The hierarchical 2-stage neural network architecture maps the perceived surroundings to diverse trajectories in the form of trajectory waypoints, that an autonomous navigation system can choose to navigate/traverse. The first stage of the disclosed hierarchical 2-stage Neural Network architecture is a Trajectory Proposal Network which generates a set of diverse traversable regions in an environment which can be occupied by the autonomous navigation system in the future. The second stage is a Trajectory Sampling network which predicts a fine-grained trajectory/trajectory waypoint over the diverse traversable regions proposed by Trajectory Proposal Network.

Abstract: The disclosure provides an approach for recommending and installing a patch onto a first computing device, comprising: parsing a machine-readable document for a property variable and a possible value of the property variable, wherein the combination of the property variable and the possible value represents a criticality of installing the patch; transmitting a request to the first computing device for a first value of the first computing device, the first value representing a state of the first computing device corresponding to the property variable; receiving the first value; comparing the possible value to the first value; based on the comparing, performing a first determination of the criticality of installing the patch onto the first computing device; and based on the criticality of installing the patch, installing the patch onto the first computing device.

Abstract: Embodiments herein achieve a method for managing display of content on one or more secondary device by a primary device. The method includes causing, by the primary device, to display a user interface comprising the content and a graphical representation of the at least one secondary device connected to the primary device. Further, the method includes detecting by the primary device an input performed to select at least one portion of the content to be displayed on the at least one secondary device. Further, the method includes transmitting by the primary device the at least one selected portion of the content to the secondary device. One or more selected portion is dynamically modified based on information about the at least one secondary device.

Abstract: The present disclosure relates to a method and system for optimizing a test suite comprising plurality of test cases. In an embodiment, the pluralities of test cases are received from data sources. The method identifies one or more redundant and non-redundant test cases among the plurality of test cases in the test suite and computes similarity percentage between each of the non-redundant test cases by comparing test description and steps of each non-redundant test case with test description and steps of other non-redundant test cases. The non-redundant test cases are grouped into clusters based on the similarity percentage. Further, the method identifies test functions in each of the clusters for determining reusable test functions. Upon identifying the reusable test functions, test cases in each of the clusters are reconstructed by replacing each of the test cases with the reusable test function references thereby optimizing the test suite.

Abstract: The present disclosure relates to a method and system for optimizing a test suite comprising plurality of test cases. In an embodiment, the pluralities of test cases are received from data sources. The method identifies one or more redundant and non-redundant test cases among the plurality of test cases in the test suite and computes similarity percentage between each of the non-redundant test cases by comparing test description and steps of each non-redundant test case with test description and steps of other non-redundant test cases. The non-redundant test cases are grouped into clusters based on the similarity percentage. Further, the method identifies test functions in each of the clusters for determining reusable test functions. Upon identifying the reusable test functions, test cases in each of the clusters are reconstructed by replacing each of the test cases with the reusable test function references thereby optimizing the test suite.