Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.

Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: Exemplary methods, apparatuses, and systems include a client virtual machine processing a system call for a device driver to instruct a physical device to perform a function and transmitting the system call to an appliance virtual machine to execute the system call. The client virtual machine determines, in response to the system call, that an established connection with the appliance virtual machine has switched from a first protocol to a second protocol, the first and second protocols including a high-performance transmission protocol and Transmission Control Protocol and Internet Protocol (TCP/IP). The client virtual machine transmits the system call to the appliance virtual machine according to the second protocol. For example, the established connection may switch to the second protocol in response to the client virtual machine migrating to the first host device from a second host device.

Abstract: System trains machine learning model to determine content data, metadata, and context data for support ticket communications, in response to receiving support ticket communications. Machine learning model receives communication associated with support ticket, and determines content data, metadata, and context data for communication. System converts content data, metadata, and context data for communication into first impulse for first channel and second impulse for second channel. System determines first channel value based on first type of conversion of first impulse and any impulses for first channel that are converted from data that is determined for support ticket event. System determines second channel value based on second type of conversion of second impulse and any impulses for second channel that are converted from data that is determined for support ticket event. System uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.

Abstract: A hardware accelerator that is efficient at performing computations related to tensors. The hardware accelerator may store a complementary dense process tensor that is combined from a plurality of sparse process tensors. The plurality of sparse process tensors have non-overlapping locations of active values. The hardware accelerator may perform elementwise operations between the complementary dense process tensor and an activation tensor to generate a product tensor. The hardware accelerator may re-arrange the product tensor based on a permutation logic to separate the products into groups. Each group corresponds to one of the sparse process tensors. Each group may be accumulated separately to generate a plurality of output values. The output values may be selected in an activation selection. The activation selection may be a dense activation or a sparse activation such as k winner activation that set non-winners to zeros.

Abstract: A hardware accelerator that is efficient at performing computations related to tensors. The hardware accelerator may store a complementary dense process tensor that is combined from a plurality of sparse process tensors. The plurality of sparse process tensors have non-overlapping locations of active values. The hardware accelerator may perform elementwise operations between the complementary dense process tensor and an activation tensor to generate a product tensor. The hardware accelerator may re-arrange the product tensor based on a permutation logic to separate the products into groups. Each group corresponds to one of the sparse process tensors. Each group may be accumulated separately to generate a plurality of output values. The output values may be selected in an activation selection. The activation selection may be a dense activation or a sparse activation such as k winner activation that set non-winners to zeros.

Abstract: A hardware accelerator that is efficient at performing computations related to tensors. The hardware accelerator may store a complementary dense process tensor that is combined from a plurality of sparse process tensors. The plurality of sparse process tensors have non-overlapping locations of active values. The hardware accelerator may perform elementwise operations between the complementary dense process tensor and an activation tensor to generate a product tensor. The hardware accelerator may re-arrange the product tensor based on a permutation logic to separate the products into groups. Each group corresponds to one of the sparse process tensors. Each group may be accumulated separately to generate a plurality of output values. The output values may be selected in an activation selection. The activation selection may be a dense activation or a sparse activation such as k winner activation that set non-winners to zeros.

Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.

Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.

Abstract: Systems and methods are described for determining customer sentiment using natural language processing in technical support communications. Communication content exchanged between a customer device and an agent device may be filtered to remove technical support syntax. Using natural language processing techniques, the processor may assign baseline values to features within the filtered communication content. To assign the baseline values, features from the filtered communication content may be identified, where the features pertain to expressed sentiments, and a trained first model may be applied to identify polarities and strengths related to the identified features. A score value may then be assigned to each identified feature, the score values being based on the polarities and strengths. A subset of the score values may then be weighted based on metadata and/or context, and the score values may be combined using a second model to determine an overall sentiment of the filtered communication content.

Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: System trains machine learning model to determine content data, metadata, and context data for support ticket communications, in response to receiving support ticket communications. Machine learning model receives communication associated with support ticket, and determines content data, metadata, and context data for communication. System converts content data, metadata, and context data for communication into first impulse for first channel and second impulse for second channel. System determines first channel value based on first type of conversion of first impulse and any impulses for first channel that are converted from data that is determined for support ticket event. System determines second channel value based on second type of conversion of second impulse and any impulses for second channel that are converted from data that is determined for support ticket event. System uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.

Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.

Abstract: Platform-agnostic predictive models based on database management system instructions are described. A system identifies a representation of data transformations associated with a first predictive model that executes on a first computing platform. The system parses the representation of data transformations. The system generates database management system instructions that correspond to the parsed representation of data transformations. The system sends the database management system instructions to a second predictive model that executes on a second computing platform, thereby enabling the second predictive model to execute at least some of the database management system instructions to generate a prediction. The first computing platform and the second computing platform are different types of computing platforms.

Abstract: In a computer-implemented method to facilitate administration of a virtualization infrastructure, operational conditions of members of the virtualization infrastructure are monitored by a social network monitoring agent, wherein the members of the virtualization infrastructure are mapped to a social network such that at least a portion of the members of the virtualization infrastructure are also members of the social network. A group of members of the virtualization infrastructure is automatically created within the social network based at least in part on the operational conditions.

Abstract: In a computer-implemented method to facilitate administration of a virtualization infrastructure, posted messages of members of a virtualization infrastructure are displayed, wherein the posted messages comprise tags identifying operational conditions of the members of the virtualization infrastructure. Responsive to a selection of a particular tag, the members of the virtualization infrastructure that posted a message comprising the particular tag are displayed.

Abstract: Image data representing a desktop image for a client device that is accessing the desktop remotely is compressed according to a method that preserves image fidelity in selected non-text regions. The method, which is carried out in a remote server, includes the steps of generating image data for the remote desktop image and analyzing different regions of the remote desktop image, identifying those regions of the remote desktop image that are text regions, selecting non-text regions of the remote desktop image for lossless compression based on a spatial relationship between the non-text regions and the text regions, compressing the image data using a lossless compression protocol for a portion of the image data corresponding to the selected non-text regions, and transmitting the compressed image data to the client device.

Abstract: Exemplary methods, apparatuses, and systems include a client virtual machine processing a system call for a device driver to instruct a physical device to perform a function and transmitting the system call to an appliance virtual machine to execute the system call. The client virtual machine determines, in response to the system call, that an established connection with the appliance virtual machine has switched from a first protocol to a second protocol, the first and second protocols including a high-performance transmission protocol and Transmission Control Protocol and Internet Protocol (TCP/IP). The client virtual machine transmits the system call to the appliance virtual machine according to the second protocol. For example, the established connection may switch to the second protocol in response to the client virtual machine migrating to the first host device from a second host device.

Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.