Abstract: As described herein, a system, method, and computer program are provided for batching a collection of elements in local memory for processing. A single collection of elements of data is stored in a local memory across a plurality of sub-collections. The single collection of elements is processed on a sub-collection basis, where each sub-collection is removed from the local memory upon completion of the processing of the elements in the sub-collection.

Abstract: As described herein, a system, method, and computer program are provided for using malware to protect data. A dormant malware embedded in data detects that a preconfigured condition has been satisfied for self-activation. Responsive to detecting that the preconfigured condition has been satisfied, the dormant malware performs self-activation.

Abstract: As described herein, a system, method, and computer program provide a test environment as a service. The service provides a user interface presenting a plurality of configurable parameters for creating a test environment. The service receives, via the user interface, user input defining configurations for one or more configurable parameters of the plurality of configurable parameters. The service creates the test environment in accordance with the configurations.

Abstract: According to one aspect of the present invention there is provided a system, method, and computer program product for recovering from a network failure in a communication network using network function virtualization (NFV-based network), the method including: selecting a first network component of the NFV-based network, detecting at least one probable failure of the first network component, selecting a second network component to be used for replacing the instance of the VNF in the first network component prior to a failure of the first network component, and securing at least one resource of the selected second network component for the other instance of the VNF and maintaining, in the selected second network component, an updated copy of data associated with the instance of the VNF in the first network component.

Abstract: As described herein, a system, method, and computer program are provided for using a dynamic threshold mechanism that is utilizing a set of machine learning models to auto-complete user input fields. User access to a form having a plurality of user input fields is detected. One or more of the plurality of user input fields are auto-completed over a sequence of stages, utilizing at least one machine learning model.

Abstract: As described herein, a system, method, and computer program are provided for a multi-stage multi-class classification. A dataset having an imbalanced distribution of data across a plurality of classes is identified. The plurality of classes are grouped into a plurality of clusters, based on a defined criteria. A plurality of machine learning models are trained, each machine learning model of the plurality of machine learning models trained using a subset of the data in the dataset corresponding to a particular cluster of the plurality of clusters. The plurality of machine learning models are used, in stages, to predict a classification for a given input.

Abstract: As described herein, a system, method, and computer program are provided for smart user alert messaging. In use, raw information associated with usage of a communication service by a customer is identified. Additionally, the raw information is processed, using artificial intelligence, to infer one or more aspects of a status of the customer. Further, one or more alerts is generated for the customer, based on the raw information and the one or more aspects of the status of the customer.

Abstract: According to one aspect of the present invention there is provided a system, method, and computer program product for recovering from a network failure in a communication network using network function virtualization (NFV-based network), the method including: selecting a first network component of the NFV-based network, detecting at least one probable failure of the first network component, selecting a second network component to be used for replacing the instance of the VNF in the first network component prior to a failure of the first network component, and securing at least one resource of the selected second network component for the other instance of the VNF and maintaining, in the selected second network component, an updated copy of data associated with the instance of the VNF in the first network component.

Abstract: As described herein, a system, method, and computer program are provided for a TOSCA modeling optimization for 5G network orchestration. A template catalog storing one or more network service instance templates is accessed, by a TOSCA-based orchestrator in a 5G network. A network service instance is deployed to the 5G network from the one or more network service instance templates, by the TOSCA-based orchestrator.

Abstract: As described herein, a system, method, and computer program are provided for processing a dataset. In one embodiment, an AI-model to inference the dataset is selected. A plurality of inferencing engines each using the AI-model are initiated, where a number of the inferencing engines that are initiated is calculated according to a predetermined time constraint. The dataset is divided between the plurality of inferencing engines.

Abstract: As described herein, a system, method, and computer program are provided for scraping POM data from webpages. A plug-in tool of a web browser receives input defining one or more parameters for extracting page object model (POM) data from a webpage. The plug-in tool automatically extracts the POM data from the webpage based on the one or more parameters defined in the input.

Abstract: As described herein, a system, method, and computer program are provided for code customization. A customization of existing code or metadata defining a software application is received, to form a customized version of the software application. The customization is tagged with a label that differentiates the customization from the existing code or metadata. During runtime or compilation time, a conditional logical selection between the existing code or metadata and the customization is made, using the label.

Abstract: As described herein, a system, method, and computer program provide explainability of entity data segmentation based on Boolean friction points. A dataset is processed, using a machine learning model, to calculate a plurality of Shapley values for the dataset, wherein the dataset includes friction points and explanatory variables. The dataset is clustered to generate a plurality of segments, based on the Shapley values. For each segment of the plurality of segments, a global explanation is generated for the segment using a predefined list of Boolean friction columns and the Shapley values.

Abstract: As described herein, a system, method, and computer program are provided for selectively amending a large customer agreement. A written contract for a customer representing an agreement to provision a plurality of telecommunication services at a plurality of sites of the customer is accessed. A user interface is provided presenting, as a plurality of selectable elements, a plurality of items in the written contract. An update to the written contract is received via the user interface. The update is processed as a selective amendment to the written contract to form an amended version of the written contract.

Abstract: As described herein, a system, method, and computer program are provided for processing a dataset. In one embodiment, an AI-model to inference the dataset is selected. A plurality of inferencing engines each using the AI-model are initiated, where a number of the inferencing engines that are initiated is calculated according to a predetermined time constraint. The dataset is divided between the plurality of inferencing engines.

Abstract: As described herein, a system, method, and computer program are provided for processing a dataset. In one embodiment, an AI-model to inference the dataset is selected. A plurality of inferencing engines each using the AI-model are initiated, where a number of the inferencing engines that are initiated is calculated according to a predetermined time constraint. The dataset is divided between the plurality of inferencing engines.

Abstract: As described herein, a system, method, and computer program are provided for direct API call authentication during an end-to-end test flow. During use of an end-to-end testing framework, a network log is stored that includes an authentication token issued by an identity provider. The authentication token is then used for making a direct API call during a test flow of the end-to-end testing framework.

Abstract: As described herein, a system, method, and computer program are provided for autoscaling data lake connections. One or more metrics defining a load on one or more data sources included in a data lake are collected. A plurality of processes configured to connect to the data lake to retrieve data therefrom are automatically scaled, as a function of the one or more metrics.

Abstract: As described herein, a system, method, and computer program are provided for managing quality of 5G network slice services. A latency issue in a 5G network is detected. Information associated with the 5G network is collected. The information is analyzed to determine a network slice provisioned in the 5G network having a quality requirement that is not met as a result of the latency issue. The network slice is reengineered, using the information associated with the 5G network. The reengineered network slice is deployed in the 5G network.

Abstract: As described herein, a system, method, and computer program are provided for data validation during transfer of data from source to target. A transfer of data from a source to a target is detected. The data transferred from the source is compared to data at the target resulting from the transfer. The transfer is validated based on a result of the comparison. A result of the validation is output.