Abstract: In general, this disclosure describes a multi-zone secure AI exchange. The multi-zone secure AI exchange may be implemented in a multi-cloud, multi-data center environment, where each zone may be in a different cloud or data center. The multi-zone secure AI exchange may include a data repository, a data exchange, and shared services. The data repository may be configured to store algorithms and datasets, each having a respective owning user. The data exchange may receive datasets and algorithms from the data repository, and may perform the algorithms to produce output data. Each of the data repository, data exchange, and shared services may have a different level of security. The data repository may implement the highest level of security, allowing the owner user, and only the owning user, to control how their data and algorithms move in and out of the data repository, or are changed while in the data repository.

Abstract: Performance of devices can be evaluated and enhanced in software-defined systems. For example, a computing device can receive, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system. The computing device can compare, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system. The computing device can determine, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system. The computing device can output, by the server, an indication of the adjustment to the client system.

Abstract: Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

Abstract: A discovery application on a computing system is provided. The discovery application receives a user input, which is for discovery of resources associated with a cloud operating system of a cloud computing system. The user input includes an authentication credential and account information associated with the cloud operating system. Based on the received input, the discovery application executes a discovery pattern comprising operations for the discovery of resources. The cloud operating system includes a group of services to access such resources. At least one of the operations corresponds to an API call to an API endpoint associated with a service of the group of services. The discovery application receives a response to the API call from the cloud operating system. The response includes a payload of information associated with the resources. The discovery application updates, based on the received response, one or more configuration items in a configuration management database.

Abstract: Some embodiments use text and/or image processing methods to determine whether a user of an electronic messaging platform is subject to an online threat such as cyberbullying, sexual grooming, and identity theft, among others. In some embodiments, a text content of electronic messages is automatically harvested and aggregated into conversations. Conversation data are then analyzed to extract various threat indicators. A result of a text analysis may be combined with a result of an analysis of an image transmitted as part of the respective conversation. When a threat is detected, some embodiments automatically send a notification to a third party (e.g., parent, teacher, etc.

Abstract: Systems and techniques for determining data assessment scores indicative of potential security vulnerabilities and enacting measures to protect sensitive information by controlling permissions and access to sensitive information and systems are provided herein. A data assessment model receives inputs of user access data, user role type, baseline permissions for the user role type, and produces a score. The score is useful for identifying potential security vulnerabilities and enacting proactive security measures to lock down potential vulnerabilities by blocking or changing permissions to reduce risk scores.

Abstract: Methods, systems, and devices for data processing are described. According to the techniques described herein, an application server may be used for real-time provisioning for features for a user. The application server may validate requests from the user for feature activation or deactivation and after validation, may communicate with a provisioning server, which may be non-local to the user or the application server, to indicate validation and activation or deactivation of a feature for the user.

Abstract: There are provided systems and methods for an artificial intelligence (AI) orchestration layer to facilitate migrations between different AI platforms. A service provider may provide AI portability functions through an orchestration layer that connects different AI services and platforms. The orchestration layer may be used to monitor user interactions with a first AI platform that request AI predictive services and outputs. Using these monitored interactions, the service provider may build and train a simulated AI model that attempts to mirror or replicate the AI model trained for the user on the first AI platform. Thereafter, when the user begins use of a second AI platform that includes the same or similar functionalities to the first AI platform, the service provider may utilize the orchestration layer to assist in training an AI model on the second AI platform based on the previously trained AI model on the first AI platform.

Abstract: A system and method are disclosed for training a machine learning model using information pertaining to job titles. Training data for the machine learning model is generated. Generating the training data includes generating first training input including information identifying the job titles associated with organizations, and generating a first target output for the first training input. The first target output identifies an indication of job families. A job family identifies a category of personnel positions that are categorized based on one or more characteristics that are shared between the personnel positions of the category. The training data to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output is provided.

Abstract: Methods, systems, and computer readable medium are provided for receiving an event message in a plurality of event messages, the event message comprising a sequence number and associated data, identifying the event message as an out-of-order event message based on the sequence number, assigning a priority level to the out-of-order event message based on a plurality of priority rules, and placing the out-of-order event message in a primary queue of messages based on the priority level assigned to the event message.

Abstract: A method is disclosed for enabling a network location to provide an ordering process for data relevant to connected network devices' activities. The method includes assembling the data, utilizing the activity data, and associating the data, such that information is derived to enable a desired expansion of at least one designated activity. Another method is disclosed for managing an object assignment broadcast operations for a network location based on a network device's previous activities. This second method includes tracing a network device's conduct to determine that a network device prefers a particular class of content. The method also includes tagging a network device's profile with the respective observation and deciding by a network location as to the classification factor for a network device to be targeted for an object assignment broadcast.

Abstract: The present disclosure is directed to methods and apparatus for tuning implementations of machine learning models for resource-constrained devices. In various embodiments, computer-readable instructions that utilize a trained machine learning model during execution of the computer-readable instructions on a resource-constrained device may be evaluated. Based on the evaluating, it may be determined that an amount of a first computing resource required by the resource-constrained device to execute the computer-readable instructions fails to satisfy a constraint related to the first computing resource. Based on the constraint, one or more candidate alterations to the computer-readable instructions may be identified.

Abstract: Techniques for machine learning inferencing endpoint discovery in a distributed computing system are discloses herein. In one example, a method includes searching a database containing machine learning endpoint records having data representing values of execution latency or prediction accuracy corresponding inferencing endpoints deployed in the distributed computing system. The method also includes generating a list of inferencing endpoints matching the individual target values and determining whether a count of the inferencing endpoints in the generated list exceeds a preset threshold. In response to determining that the identified count does not exceed the preset threshold, the method includes instantiating one or more additional inferencing endpoints in the distributed computing system based on the individual target values in the received query.

Abstract: A system and method are disclosed associated with at least one physical data store instance adapted to contain electronic records; and a shared cluster platform, coupled to the data store, including: a computer processor, and a computer memory, coupled to the computer processor, storing instructions that, when executed by the computer processor cause the shared cluster platform to: receive a request for a first tenant; select a physical data store instance in response to the request; generate a first container for the first tenant, wherein the first container maps the first tenant to the selected physical data store instance; generate a unique first key element for the first tenant; and transmit a first endpoint of the first container as a proxy for the selected physical data store instance, wherein the transmission fulfills the request. Numerous other embodiments are provided.

Abstract: A new approach is proposed that contemplates systems and methods to support utilizing security device plugins for external device control and monitoring in a secured environment. A plugin that implements one or more functionalities to communicate with and to control operations of an external device is provided to a network security device/appliance. The plugin is then loaded to the network security appliance and integrated with a software running on the network security device, wherein the software obtains the functionalities offered by the plugin. A communication link is established between the plugin of the network security device and the external device following a communication protocol. The network security device is then configured to issue/receive one or more commands to/from the external device following the communication protocol to monitor and collect information from and/or control or be controlled by the external device remotely.

Abstract: Examples described herein generally relate to deploying, on multiple separate devices, an instance of an application having a baseline version of a model for executing in a real-world context, receiving, from each instance of the application, interaction data from the application based on using the model to perform inferences during execution of the instance of the application, and training, based on the interaction data, the model to generate a trained version of the model for performing subsequent inferences during execution of the application.

Abstract: In one embodiment, a device filters data usage metrics regarding a plurality of network nodes by one or more data characteristics, to form filtered metrics. The device applies an anomaly detector to the filtered metrics. The device distinguishes, based on an output of the anomaly detector, abnormal, unusual, and normal data usage among the filtered metrics. The device provides display data to a user interface indicative of the abnormal, unusual, and normal data usage among the filtered metrics.

Abstract: A method for enabling cloud-based management services using an on-site management cloud engine includes establishing a single secure communication channel between a management cloud engine (MCE) located in a customer premises and a cloud management services platform located in a cloud computing infrastructure, receiving, by the MCE from a unified management service (UMS) manager located in the cloud management services platform, a management instruction message directed to at least one network function located in the customer premises via the single secure communication channel, converting instruction data contained in the management instruction message to a legacy protocol recognized by the at least one network function, and providing the converted instruction data to the at least one network function.

Abstract: One or more systems, computer-implemented methods and/or non-transitory computer-readable mediums are provided to facilitate a process to employ machine learning and selectable response options to respond to a communication from an identity. A system can comprise a processor, and a memory that stores computer executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise determining a communication associated with a user identity, and, employing a machine learning model generated based on machine learning applied to one or more historical communications determined to have communicated respective intents, predicting an intent of the communication, and generating a set of one or more ranked response options corresponding to the intent, wherein the one or more response options of the set are ranked based on respective confidence levels individually determined for the one or more response options relative to the predicted intent.

Abstract: Provided is a distributed machine learning device which can efficiently converge a plurality of parameters. The distributed machine learning device includes: block configuration determination unit that determines a plurality of configurations of blocks each being a subset of a plurality of parameters used for a prediction model, and acquires a change amount of each subset of parameters belonging to the block, based on a result of executing processing of optimizing the plurality of parameters, and changes an element of the subset of the parameters in the block, based on the acquired change amount.