Abstract: A lateral movement identification tool analyzes communications sent and received from a local host to identify potential instances of lateral movement. When the host-based lateral movement identification tool identifies a host to host connection, the tool processes one or more artificial intelligence algorithms to analyze information from local network resources including a directory service, a local network system such as a network basic input/output system, a domain name system, and event logs. The lateral movement identification tool correlates the aggregated information with identified host to host messaging and sends alerts when lateral movement is suspected. Alerts may be either presented locally or provided to a central console based on configuration information.

Abstract: A multidrop network system includes N network devices. The N network devices includes M transmission-permissible devices including a master device and at least one slave device, wherein M is not greater than N. Each transmission-permissible device has at least one identification code as its identification in the multidrop network system, and the M transmission-permissible devices have at least N identification codes. The M transmission-permissible devices obtain transmission opportunities in turn according to their respective identification codes in each round of data transmission. A Kth device among the M transmission-permissible devices has multiple identification codes, and thus obtains multiple transmission opportunities in one round of data transmission.

Abstract: Embodiments of a method are disclosed. The method includes performing distributed deep learning training on a batch of training data. The method also includes determining training times representing an amount of time between a beginning batch time and an end batch time. Further, the method includes modifying a communication aspect of the communication straggler to reduce a future network communication time for the communication straggler to send a future result of the distributed deep learning training on a new batch of training data in response to the centralized parameter server determining that the learner is the communication straggler.

Abstract: Aspects of the present disclosure involve systems and methods for a service activation system in a telecommunications network that utilizes one or more generic container files for building the configuration file to instantiate the service on the network. A request for service from a network may be received from an order entry system that includes specific information about the requested service. A collection of generic configuration files may be selected based on the information included in the service order and arranged to build a configuration file to be executed on the network. The service activation system may also include a component or group of components to verify a received service order and alter the service order with default information or data where applicable. The configuration file may also be executed on the network through one or more drivers communicating with the affected devices to configure the one or more network devices.

Abstract: Systems and methods are disclosed for communicating with and controlling load control systems of respective user environments from locations that are remote from the user environments.

Abstract: A system configured to synchronize the displays of multiple infusion pumps is provided. In some embodiments, the system includes a plurality of infusion pumps in communication with a server. An individual infusion pump synchronizes its internal clock by communicating with the server. Based on the synchronized internal clock, the infusion pump determines the current time, calculates a parameter based on the current time, and causes screen content corresponding to the calculated parameter to be displayed.

Abstract: A method and a device for determining deployment information of a network are disclosed. The method for determining deployment information of a network includes: receiving, by a first network entity, a first message sent by a second network entity, where the first message carries first deployment information, and the first deployment information is deployment information of a network component; and determining, by the first network entity, second deployment information based on the first deployment information, where the second deployment information is deployment information of a network, and the network includes at least one network component. The foregoing solution can improve accuracy of determining deployment information of a network.

Abstract: A system includes a memory; and a processor configured to train a first machine learning model based on the first dataset labeling; provide the second dataset to the trained first machine learning model to generate an updated second dataset including an updated second dataset labeling, determine a first difference between the updated second dataset labeling and the second dataset labeling; train a second machine learning model based on the updated second dataset labeling if the first difference is greater than a first threshold value; provide the first dataset to the trained second machine learning model to generate an updated first dataset including an updated first dataset labeling, determine a second difference between the updated first dataset labeling and the first dataset labeling; and train the first machine learning model based on the updated first dataset labeling if the second difference is greater than a second threshold value.

Abstract: A system and method of serverless function scaling is described. The method may include receiving one or more types of serverless function invocations. The method also includes invoking one or more serverless functions and receiving metrics from the one or more serverless functions. The method also includes generating scaling heuristics for scaling of a number of serverless functions in view of the received metrics.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: System, methods, and devices provide prioritized scaling for persistent content sharing platforms. Systems include a processing device comprising one or more processors configured to generate a first display configured to represent a private computing environment associated with a user, and configured to generate a second display configured to represent a shared computing environment associated with the user. Systems further include an encoder configured to generate a packetized data stream representing the second display. Systems also include a multiview module comprising a decoder configured to generate a third display configured to represent a unified view of a plurality of active shared computing environments associated with a plurality of users, the decoder being further configured to decode at least one packetized data stream representing at least one of the plurality of active shared computing environments.

Abstract: An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.

Abstract: Methods, computer readable media, devices, and systems provide for distributed machine learning. In one aspect, a method of training a model is disclosed. The method includes receiving, by a client device, from one or more servers, an intermediate model, training, by the client device, the intermediate model based on private data, and transmitting, by the client device, to the one or more servers, the trained intermediate model.

Abstract: A first server can generate user profiles and receive requests from user devices for enrollment in a first server-managed system that includes user groups. The first server can provide a unique key to a user device during an enrolment process based on a user group the user device is assigned to. The first server can include an enrollment notification for the user device in a first notification transmitted to a messaging service. The messaging service can transmit a second notification to the user device, and the user device can request a user profile from a second server based on second server access information included in the second notification. The second server can use the unique key to access user profile information which it transmits to the user device based on the request. The user device can access the user profile from the profile information using the unique key.

Abstract: A communication system is a communication system connected to an upper network and a lower network, and includes at least one intermediate communication device including a plurality of upper ports for inputting and outputting optical signals to and from the upper network, and a plurality of lower ports for inputting and outputting optical signals to and from the lower network, the intermediate communication device being configured to relay communication between the upper network and the lower network, a switching device that is connected to the upper and lower networks and the plurality of upper and lower ports, switches a connection between the upper network and the plurality of upper ports, and switches a connection between the lower network and a plurality of lower ports, and a control device that outputs, to the switching device, an instruction to switch the connection by the switching device according to a communication status between the upper network and the lower network.

Abstract: A communication network may be provided having a plurality of network nodes and which may be configured to enable instantiation of different network slices which represent virtual networks with different feature sets, e.g., providing different network functions and/or having different network characteristics. The communication network may be configured to allow a network slice to be used for data communication between i) a user equipment (UE) connected to the network and configured to execute an application, and ii) a content or application server (CAS) configured to provide an application service via the network. Network functions may be provided which allow influencing the UE's slice usage based on requirements of the application service, even if the CAS is located outside the connectivity provider's network, e.g., outside a mobile operator's domain.

Abstract: Methods, systems, and apparatuses are described for dynamic management of file system objects (e.g., a file or a directory). Such management includes syncing, routing, and storing capabilities. A file system object may be tagged with one or more tags based on at least one of file system object content or file system object location. Dynamic rules that control routing of file system objects to one or more locations based on the one or more tags are accessed and searched for an applicable rule. The file system object may be routed and/or stored based on the applicable rule. The rules may specify allowable locations as well as locations that are not allowed for a given file system object. The location may be a cloud-based location, a location that is on the premises of the enterprise, a location provided by and/or serviced by a competing entity, etc.

Abstract: In general, techniques are described for slice assurance within a mobile network. In some examples, a method includes obtaining, by a slice assurance function (SAF) executed by a device, key performance indicator (KPI) values for a first slice of a plurality of slices implemented by a plurality of base stations serving a tracking area of a mobile network; determining, by the SAF, based in part on the KPI values for the first slice, a service level agreement (SLA) for the first slice has not been met; re-allocating, by the SAF in response to the determining, slice resources associated with any of the plurality of slices to compute a new slice configuration parameter for the first slice; and reconfiguring, by the SAF, at least one of the plurality of base stations to implement the new slice configuration parameter for the first slice.

Abstract: A communication device includes a parameter setter and a communication controller for cyclic communication to transmit data including a first data piece and a second data piece different from the first data piece and having lower priority for real-time transmission than the first data piece to slave stations. The parameter setter presets a number of divisions n to divide the second data piece before the cyclic communication is started. The communication controller transmits the first data piece to a first slave station in one cycle, divides the second data piece by the number of divisions n, and transmits the divided second data piece to a second slave station in n cycles, where n is the number of divisions. The communication controller transmits, before transmitting the second data piece, data indicating the number of divisions n set by the parameter setter to the second slave station.

Abstract: Disclosed are a method for realizing an nGraph framework supporting an FPGA backend device, and a related apparatus. The method includes: integrating an OpenCL standard API library into an nGraph framework; creating, in the nGraph framework, an FPGA backend device creation module for registering an FPGA rear-end device, initializing an OpenCL environment and acquiring the FPGA backend device; creating, in the nGraph framework, an FPGA buffer space processing module for opening up an FPGA buffer space and for reading and writing an FPGA cache; creating, in the nGraph framework, an OP kernel implementation module for creating an OP kernel and compiling the OP kernel; and creating, in the nGraph framework, an FPGA compiling execution module for registering, scheduling and executing the OP kernel.