Abstract: A message broker customization system and method with user administered policy functions. The system uses user defined and controlled policy functions to programmatically customize the behaviors of a message broker's processing of messages and further allowing for that customization to take place on a per channel basis. This provides for greater functionality and flexibility to the users of a message system, especially when the users of the message system do not have administrative control over the message broker.

Abstract: A method for processing a data package to be sent using an application, by an originator of the data package to at least one recipient, the data package having an associated mode of communication, the method comprising the steps of analysing the data package to determine content of at least one field of the data package. The method also comprises the step of generating a characteristic of the data package, wherein the characteristic is based on the mode of communication and the content of the at least one field of the data package; and an incorrect action by the originator of the data package based on the characteristic is determined.

Abstract: Aspects of the present disclosure relate to interactive search training. A training canvas comprises results associated with a search query. The training canvas may be used as part of a training session that occurs during normal use of a search platform. When the search platform is first used, the results may be provided based on an existing model. An irrelevant result may be removed from the training canvas, such that a replacement result is added in its place. Additionally, results may be reordered, thereby indicating a ranking with which results should be displayed. Such interactions with the training canvas may be used to generate training data, such that a new model is trained accordingly. Thus, interactions with the training canvas yield high-quality training data that is usable to generate a model having equal or greater performance than a model that was trained using an equivalent amount of implicit training data.

Abstract: A method and system for providing a communication stream for associating messages are described. A communication stream receives various message types including messages, posts, events, tasks, and comments, and presents them to users. Private messages relating to one or more messages may be sent between and among one or more users.

Abstract: A method, device, and system for ensuring a service level agreement (SLA) of an application, where the method includes: obtaining, by an application function (AF) entity, information about a first network slice instance (NSI) that is in network slice instances between a specified location and a target network and whose SLA support capability meets a subscribed SLA requirement of the application, and sending a notification message including the information about the first NSI, where the notification message includes the information about the first NSI, to establish a new session in the first NSI for a terminal.

Abstract: A method includes obtaining, by a management device, sub-interface information that includes a correspondence between an identifier of a first sub-interface of a first device and a first network slice identifier, a correspondence between an identifier of a second sub-interface of a second device and a second network slice identifier, and information indicating that the first sub-interface is directly connected to the second sub-interface, and determining, by the management device, a network slice topology based on the obtained sub-interface information.

Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Abstract: Provided is a behavior analysis system including: a generation unit that generates a behavior data group including a plurality of behavior data on an analysis target basis; a conversion unit that converts the behavior data group of each of a plurality of analysis targets by converting a parameter which depends on the analysis target out of parameters included in the behavior data group into a parameter which does not depend on the analysis target; and an analysis unit that performs analysis by using the converted behavior data group of the plurality of analysis targets.

Abstract: A configuration method includes: receiving, by a first device of a network, a first control message having configuration elements for activating a dynamic routing protocol in the network; configuring by the first device setup parameters for establishing sessions according to the protocol used by the first device on the basis of configuration elements included in the first message; if the configuration elements in the message include a management instruction for handling sessions according to the protocol in the network, executing by the first device the at least one management instruction; and if the configuration elements in the message include a setting for directing propagation of the configuration elements in the network, dispatching by the first device in accordance with the propagation setting at least one second control message to at least one second device of the network, which includes all or some of the configuration elements.

Abstract: Automatic network device identification and presentation of network information via augmented reality is disclosed. Real time imagery of a scene within a field of view of a camera is received. It is determined that the imagery depicts a first object. It is determined that the first object comprises a first network-attached device attached to a network. Information that corresponds to the first network-attached device is obtained, and the information is presented in conjunction with a real time view of the scene.

Abstract: A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services inclu

Abstract: A Quality of Experience (QoE) optimization system and method are provided. An electronic device inputs key performance indicators (KPIs) and system control parameters collected from a core network, a base station and a user equipment (UE) into a QoE optimization model. The QoE optimization model then optimizes the system control parameters based on the KPIs and a user QoE fed back from the UE to output optimized system control parameters. Furthermore, a strategy emulator controls at least one of a base station emulator and a UE emulator, so as to emulate the QoE optimization model using the at least one of the base station emulator and the UE emulator. Non-real-time optimization adjustments to the QoE optimization model are made based on the result of the emulation performed by the at least one of the base station emulator and the UE emulator.

Abstract: An electronic device stores a shared playback queue for the shared playback session, the shared playback queue comprising one or more media content items, including a first media content item requested by a first user. While the shared playback queue includes the first media content item, the device receives a second request, from a second user, to add a second media content item to the shared playback queue. The device determines an order of the shared playback queue based at least in part on the musical preferences indicated in a profile of a third user of the plurality of users participating in the shared playback session. The device provides, for playback in the shared playback session, the first media content item and the second media content item based on the order of the shared playback queue.

Abstract: A peripheral device includes one or more processors and a memory storing program instructions that when executed implement an extension manager of a virtualized computing service. The extension manager establishes a secure network channel for communications between the peripheral device, which is located at a premise external to a provider network, and a data center of the provider network. The extension manager assigns a network address of the substrate network of the service to a hardware server at the external premise. The substrate address is also assigned to an extension traffic intermediary at the data center. In response to a command directed to the virtualized computing service, one or more compute instance configuration operations are performed at the hardware server.

Abstract: Network management apparatus and methods are described. A network management apparatus comprises network data receiving means for receiving network data that is representative of the current condition of a communications network, the network data comprising a plurality of values indicative of the performance of network elements; network data transformation means for transforming the received network data into a network state vector that is indicative of a current state of the network; and network state prediction means for predicting a future network state vector of the network from the current network state vector, the network state prediction means comprising a self-learning prediction module having a memory for storing at least one internal state.

Abstract: The disclosure herein describes a system for generating customizable summaries of multi-message conversations in email threads and other multi-message and multi-participant messaging applications. A summary icon is displayed if the number of messages in a thread exceeds a threshold number. Message data is cleaned and analyzed by ML models to generate feature vectors representing the messages content. Multiple loglinear regression models compute similarity between message sentences. Clustering algorithm(s) to generate a multi-message conversation thread summary based on the values. If the user selects the summary icon, the multi-message conversation thread summary is displayed. The summary includes summary sentences and/or annotations describing content of the messages in the thread, participants contributing to the messages in the thread, links back to the original messages in the thread and/or a count of the number of messages contributed by each participant.

Abstract: An object recommendation method, computer device, and medium are provided, relating to the field of artificial intelligence and, particularly, content recommendation. A method includes: obtaining a first user profile of a user, the first user profile being determined based on behavior data of the user over a first historical period of time; using a matching model to determine a recommended object based on the first user profile; recommending the recommended object to the user; obtaining a second user profile of the user, the second user profile being determined based on behavior data of the user over a second historical period of time, and the behavior data of the user over the second historical period of time includes behavior data of the user after the recommended object is recommended to the user; and updating the matching model based on the first user profile, the second user profile, and the recommended object.

Abstract: A system receives data storage workflow activities that include computer-executable instructions for carrying out data storage workflow in a network data storage system. Once the workflow is received, the system deploys the workflow to one or more workflow engines that can execute the various data storage activities related to the workflow. Prior to executing a data storage activity, the system can determine which workflow engine to use based on an allocation scheme.

Abstract: An edge computing telecommunications network is provided for efficiently generating and updating computing models for use at distributed devices connected to different edge compute sites of the network. A network orchestration system may track devices connected to the network and the edge compute sites to which they are connected. The devices may comprise limited computing power and may include sensors or other data collection mechanisms. Raw data may be provided from connected devices to one or more edge compute sites. Edge compute sites may be instructed, e.g., by the network orchestration system, whether to replicate the raw data, modify the data to make it ready for consumption by a computing model, replicate the modified data, refine the computing model, replicate the refined computing model, and/or share some or all of the raw data, modified data, and/or refined computing model with other edge computing sites and/or connected devices.

Abstract: Systems, computer program products, and methods are described herein for real-time imitation network generation using artificial intelligence. The present invention is configured to electronically receive, from a computing device of a user, a real dataset; initiate one or more machine learning algorithms on the real dataset; determine, using the one or more machine learning algorithms, one or more data distribution parameters associated with the real dataset; electronically receive, from the computing device of the user, a first shift parameter; skew the one or more data distribution parameters using the first shift parameter to generate one or more skewed data distribution parameters; and generate, using the one or more machine learning algorithms, an imitation dataset using the one or more skewed data distribution parameters.