Abstract: Embodiments provide for intelligent report configuration. Some embodiments aggregate historical configuration data based at least in part on a plurality of historical reports, receive a signal indicative of a configuration request for a report having a hierarchical data structure, identify at least one configuration node of a report configuration workflow for configuring the report. The report configuration workflow may be defined by a hierarchical arrangement of a plurality of configuration nodes. Some embodiments generate, based at least in part on the historical configuration data and using one or more models, one or more data objects for the at least one configuration node. The one or more data objects may be indicative of at least a portion of the configuration data for one or more portions of the report. Some embodiments cause display of the one or more data objects on a configuration user interface rendered on a user device.

Abstract: Described are techniques for disintegrating an entity into smaller entities. A graph (“first graph”) for the entity of records to be disintegrated is constructed, where each vertex of the first graph represents a record in the entity of records to be disintegrated. The edges in the first graph connecting records in the entity of records represent matching links between the records, where each edge is associated with a weight corresponding to a similarity score. Furthermore, two or more additional graphs representing two or more sub-entities of the entity of records to be disintegrated are constructed. Such graphs are constructed based on selecting edges with a maximum weight out of the edges connected between each pair of records in the first graph or based on the number of connections each record has with other records in the first graph exceeding a threshold value.

Abstract: Techniques are disclosed for migrating one or more services from an edge device to a cloud computing environment. In one example, a migration service receives a request to migrate a first set of services from the edge device to the cloud computing environment. The migration service identifies a hardware profile of a computing device (or devices) of the cloud computing environment that matches the edge device, and then configures the computing device to execute a second set of services that corresponds to the first set of services. The migration service establishes a communication channel between the edge device and the computing device, and then executes a set of migration operations such that the second set of services is configured to execute as the first set of services. The computing device may operate in a virtual bootstrap environment or dedicated region of the cloud computing environment.

Abstract: The method can include optionally training a transportation modality classification model; determining a transportation modality of a trip; and optionally triggering an action based on the transportation modality. However, the method can additionally or alternatively include any other suitable elements. The method functions to facilitate a classification of a transportation modality for trips based on location data (e.g., collected at a mobile device). Additionally or alternatively, the method can function to facilitate content provisions based on a trip classification.

Abstract: Provisioning of day-1 configuration templates is automated. Users are provided access to a Configuration Template User Interface (UI) for creating configuration templates. Input is received on the Configuration Template UI to create the configuration templates. A trigger is received by a configuration manager to initiate configuration of network devices or functions. The trigger includes one or more Template Identifiers (IDs) corresponding to the one or more configuration templates. Configuration files are generated based on the trigger and one or more Template IDs corresponding to the one or more configuration templates. Configuration files are sent from the configuration manager to the one or more network devices or functions to configure the one or more network devices or functions.

Abstract: Techniques are disclosed for framing images and/or video streams. Such techniques may include performing face detection on an image, determining a gaze direction within image content associated with a detected face, and defining a cropping window for the image based on the detected face and the determined gaze direction of the detected face. Thereafter, the image may be cropped according to the cropping window.

Abstract: A system and method of open-world semi-supervised satellite object detection involves a machine learning engine configured with a training component and an inference component. The method of object detection detects objects in satellite imagery represents a solution for difficult challenges of arbitrary orientations, wide variation in object sizes, large number of densely packed objects, and highly complex background. A transformer network detects unknown objects in the satellite image. The transformer network includes a rotation-aware pyramidal pseudo-labeling operation that captures scale-specific pyramidal features at oriented box regions for pseudo-labeling unknown objects in the satellite image. A semi-supervised learning pipeline learns a new set of object classes to be detected. A prediction head outputs the satellite image annotated with a predicted object class for an unknown object.

Abstract: Techniques are described for protecting services containing web applications from malicious attacks. A data flow that includes a stack trace and a process is received from a web application. The stack trace is chained with the process to generate a data flow graph. A data lake is then accessed to determine whether the data flow graph is in the data lake. If the data flow graph is not in the data lake, then the data flow can be considered safe. If the data flow graph is not already in the data lake, then a determination is made as to whether the data flow includes a command that accepts user input. If it is determined that the data flow does include a command that accepts user input, then the data flow can be considered to be malicious and the process is terminated to protect the web application from a malicious attack.

Abstract: An example operation may include one or more of detecting, by a blockchain (BC) node, a contract update on a blockchain, determining, by the BC node, at least one charge affected by the contract update, executing, by the BC node, a smart contract to re-generate an invoice based on the at least one charge, and recording the invoice on a ledger of the blockchain.

Abstract: Provided are systems, methods, and programming for facilitating real-time determination of a process completion likelihood. In some embodiments, data including an update to a system, the update occurring at a first time, wherein updates to the system are permitted until an expiration time may be obtained, a set of fixed descriptors of the system may be retrieved and/or received, and a set of status updates describing the system at prior times may be obtained. Each status update of the set of one or more status updates includes at least (i) an update to the first system and (ii) a time that the respective status update occurred. Based on the data, the fixed descriptors, and the status updates, using a trained machine learning model, a failure/success score indicating a likelihood that, at the expiration time, the system satisfies a threshold condition may be computed and stored in memory.

Abstract: Techniques and architecture are described for detecting a compromised mailbox as an email account compromise (EAC) involved in lateral phishing, lateral scam, lateral BEC, outbound scam, lateral and inbound fraudulent money transfer requests. For example, the techniques and architecture provide a method that comprises scanning, by a pre-filter, electronic mail messages (emails) within an organization, wherein the emails originate within the organization. The pre-filter analyzes the emails with respect to known fraudulent email practices and determines that an email is a questionable email. A retrospective behavior engine analyzes the questionable email with respect to one or more historical traits to provide a feature set. Based at least in part on the feature set, the verdict correlation engine determines that the questionable email belongs in a class of emails from multiple classes of emails. Based at least in part on the class, the verdict correlation engine performs a responsive action.

Abstract: An example system may include a first endpoint and a second endpoint executing a remote collector to monitor the first endpoint. The remote collector may include a buffer limit configuration unit to receive a request to install a monitoring agent on the first endpoint. The request may include an operating system type. Further, the buffer limit configuration unit may determine a first predefined buffer limit corresponding to the operating system type. Furthermore, the remote collector may include an installation unit to install the monitoring agent with configuration data on the first endpoint. The configuration data may specify a configuration for the monitoring agent to monitor an operating system executing in the first endpoint and the first predefined buffer limit as a buffer limit for the monitoring agent. Furthermore, the installation unit may enable the monitoring agent to monitor the operating system based on the configuration data with the buffer limit.

Abstract: An example system may include a first endpoint executing a remote collector and a second endpoint in communication with the first endpoint. The remote collector may monitor the second endpoint. The remote collector may include an agent installation unit to install a monitoring agent with configuration data on the second endpoint. The configuration data may specify a configuration for the monitoring agent to monitor a first program executing in the second endpoint. Further, the second endpoint may include a buffer limit configuration unit to execute the monitoring agent in a test mode to determine a first number of metrics to be collected in one cycle based on the configuration data. Furthermore, the buffer limit configuration unit may configure a buffer limit of the monitoring agent based on the first number of metrics and, upon configuring the buffer limit, enable the monitoring agent to monitor the first program.

Abstract: A system and method for performing tactical segmentation including a supply chain network having a tactical segmentation planner, an inventory system, a transportation network, supply chain entities and a computer.

Abstract: A cellular repeater system is provided. The cellular repeater system includes an outdoor antenna communicatively coupled to a repeater. The outdoor antenna is configured to relay communications from the repeater to a cellular base station. The outdoor antenna includes a plurality of log-periodic antennas rotated relative to one another such that each of the plurality of log-periodic antennas is pointed in a different direction. The cellular repeater system includes a switching device configured to selectively couple one or more of the plurality of log-periodic antennas to a RF transceiver of the repeater in order to configure the outdoor antenna in a plurality of antenna modes. Each of the plurality of antenna modes have a distinct radiation pattern.

Abstract: A method and system for implementing a multi-lender, multi-borrower loan facilitation platform is provided. The system includes a memory element and processing circuitry. The processing circuitry receives a loan request from a borrower device of a borrower. Look-alike historical borrowers for the borrower are filtered based on historical data of past loans disbursed to historical borrowers and borrower profiles of the historical borrowers stored in the memory element. The processing circuitry determines an aggregate value for each lender of multiple lenders in the multi-lender, multi-borrower loan facilitation platform based on a rate component, an inventory utilization component, and a quality component of each lender. The processing circuitry ranks lenders for various interest rate bins that are selected based on the look-alike historical borrowers based on the aggregate value. The processing circuitry matches the loan request to a highest ranking lender for an interest rate bin selected by the borrower.

Abstract: A neural network architecture search may be conducted by a controller to generate a neural network. The controller may perform the search by generating a directed acyclic graph across nodes in a search space, the nodes representing compute operations for a neural network. As the search is performed, the controller may retrieve resource availability information to modify the likelihood of a generated neural network architecture including previously unused nodes.

Abstract: A configuration manager (CM) User Interface (UI) enables a user to initiate a configuration push by a configuration manager. A CM UI is displayed and accessed by a user. A network function device to configure is identified. A configuration file for configuring the network function device is provided to the CM using the CM UI. The user, form the CM UI, initiates a configuration push of the configuration file by the CM to the network function device to begin configuring the network function device.

Abstract: The disclosure relates to systems and methods of generating behavior classifications that predict a behavior of an entity by training and executing a base machine learning (ML) model, a plurality of segmented ML models, and a merged ML model. Training data may be historical entity data, which may be grouped into different segments that describe the entity. The base ML model may be trained to predict entity behavior across a plurality of segments. Each segmented ML model may be trained to the generate a segmented behavior class that predicts entity behavior based on a respective segment. A system may provide the base class and the plurality of segmented classes as input to a merged model that was trained based on weights for each of the base ML model and the plurality of segmented ML models to generate a behavior classification representing a prediction of the entity behavior.

Abstract: A system and method are provided to configure a network for onboarding of devices. A hierarchical parameter data file describing configuration parameters of the plurality of devices, such as a Yet Another Next Generation (YANG) model, is received. A parameter database file including a plurality of configuration parameters is populated, with at least one of the configuration parameters being derived from content of the hierarchical parameter data file, and a parameter library is generated based on the parameter database file. A class definition file is generated based on at least one of the database file and the hierarchical parameter data file. Then, for a network function, a corresponding configuration file is generated based on the parameter library and the class definition file, and is provided to the network function for implementation.