Abstract: A system may provide blockchain-based storage node control for unified proof-of-storage interaction for off-chain data chunk storage. The system may include verification circuitry that may provide a challenge to a storage node to audit the storage status of a data chunk. The verification circuitry may obtain identities for the storage node and data chunk based on blockchain records. The verification circuitry may obtain a challenge answer from the storage node in response. The verification circuitry may analyze the challenge answer in view of a challenge key to confirm whether the storage node has possession of the data chunk. When the storage node has possession of the data chunk chain circuitry may add a verification record to the blockchain.

Abstract: An autonomous system and method of comprehensively testing serverless applications. The system is configured to automatically generate test scripts and test data based on deployed and modified function code and configuration files. The system is configured to work in multi-cloud environments and is able to accommodate any cloud provider. Implementation of such a system can eliminate manual errors. Furthermore, the proposed embodiments are configured to integrate with already existing devOps pipelines for rapid test execution, and can continue automated testing in real-time as modifications to the app are made.

Abstract: In some implementations, a system may receive, from an analysis system, a data stream associated with monitoring an environment. The system may process, using a first model, the data stream to detect an object associated with a service provided by an application. The system may analyze a monitoring configuration of the analysis system. The system may determine, based on processing the data stream, service data associated with providing the service in association with the object. The system may configure, based on the monitoring configuration, an edge device to access the data stream and host a second model to process the service data according to the data stream. The system may receive, from the edge device, a service output associated with providing the service in association with the object. The system may perform an action associated with the service output and the object.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated optimization of product management systems. In embodiments, automated optimization of component tools of the product management system is provided by automated evaluation and optimization of associated rules and metadata. In embodiments, metadata and rules may be associated to each other by an assessment engine. A recommendation engine may then identify non-compliant metadata, may determine a condition of the rule, and/or may generate recommendations for the rules based on the non-compliant metadata. Automated optimization of the product management system may include automated creation and mapping of decomposition relationships between commercial products and technical products. In embodiments, input data may be parsed into a set of object with unique attribute fields, which may then be validated.

Abstract: Complex computer system architectures are described for analyzing data elements of a knowledge graph, and predicting new surprising or unforeseen facts from relational learning applied to the knowledge graph. This discovery process takes advantage of the knowledge graph structure to improve the computing capabilities of a device executing a discovery calculation by applying both training and inference analysis techniques on the knowledge graph within an embedding space, and generating a scoring strategy for predicting surprising facts that may be discoverable from the knowledge graph.

Abstract: A system of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.

Abstract: A method and system for training a federated learning model are disclosed. The method may include receiving the global federated learning model from the server via the client. The client may control remote computing resources. The method may further include identifying a spare computing instance from the remote computing resources and determine a threshold training load for training the global federated learning model based on a training load assigned to the client. The method may further include, in response to a processing capacity of the spare computing instance being sufficient to process the threshold training load, offloading the threshold training load to the spare computing instance and training the global federated learning model on the spare computing instance with the training dataset stored in a data source maintained by the client.

Abstract: A system for providing temporary, transportable cellular communications networks is disclosed. The system includes at least one mobile base station and a control system. The mobile base station can include an aerial vehicular base station with a frame and propellers mounted on the frame that enable flight of the aerial vehicular base station while the control system is configured to control the flight path and functioning of the aerial vehicular base station. The aerial vehicular base station includes hardware and software components for providing cellular network coverage for short ranges. The control system determines the safe flight path for the aerial vehicular base station to reach a service location to provide cellular network coverage. The aerial vehicular base station identifies the closest base stations and available spectrum at the service location to provide communication services for the user equipment at the service location.

Abstract: A device may configure a plurality of time dimensions of a forecast model to forecast transactions. The device may identify, using the forecast model and transaction information associated with forecasting a transaction, historical transaction data that includes timing information associated with executions of a set of previous transactions that are associated with the transaction. The device may generate, using the forecast model and the plurality of time dimensions, a plurality of clusters associated with the set of previous transactions. The device may determine, using the forecast model, similarity scores for the plurality of clusters that correspond to individual probabilities that the set of previous transactions occurred according to the plurality of time dimensions. The device may select, based on the similarity scores, a time dimension for a forecast of the transaction. The device may generate the forecast of the transaction based on the time dimension.

Abstract: In some examples, machine learning and rule-based identification, anonymization, and de-anonymization of sensitive structured and unstructured data may include receiving input data that is to be masked, and determining, for the input data, at least one type '1 of entity extraction from a plurality of types of entity extractions to be performed on the input data. The at least one determined type of entity extraction may be performed on the input data, and at least one entity may be extracted from the input data. At least one replacement strategy may be determined from a plurality of replacement strategies for the at least one extracted entity. Further, the at least one determined replacement strategy may be applied to the at least one extracted entity to generate masked data.

Abstract: A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.

Abstract: Systems and methods for providing dynamic responses to a user query are disclosed herein. A system receives a message request from a computing device, and generates an intent associated with the message request based on a pre-processing of the message request. Further, the system concurrently transmits a query to a database for a prestored response to the message request based on the generated intent, and the generated intent to a knowledge source searcher. The knowledge source searcher asynchronously queries one or more knowledge sources for a predicted response to the message request. Furthermore, the system receives the prestored response from the database and the predicted response from the knowledge source searcher, determines a response to the message request, from at least one of the prestored response and the predicted response, based on a scoring model, and transmits the determined response to the computing device.

Abstract: A new product development system may provide, to one or more client devices and server devices, one or more tools to enable development of a new product. The new product development system may receive current project data identifying a current project for developing the new product, historical project data identifying historical projects for developing historical products, and industry data associated with the new product. The new product development system may process the current project data, the historical project data, and the industry data, with a machine learning model, to identify one or more variables for a predictive model and may process the one or more variables, with the predictive model, to predict a predicted success rate of the current project. The new product development system may perform one or more actions based on the predicted success rate of the current project.

Abstract: Embodiments of the present disclosure leverage near field communication (NFC) technology to provide dynamic and interactive monitoring of an environment. NFC devices may be used to check items in and out of a storage facility, obtain readings from instruments or other machinery present in the environment (e.g., to perform tests on the items, etc.), track movement of users and items within the environment, and to prompt users with information about the environment, such as information about the instruments or machinery currency being used to perform operations with respect to one or more items checked out to the user. Additionally, the NFC device may be used to configure the instruments with appropriate settings for the particular item(s) for which the instrument is currently being used or for other purposes.

Abstract: Methods, systems, and computer-readable storage media for receiving data representative of two or more AAGs, providing an identifier for each element of each of the two or more AAGs, each identifier being unique within a respective AAG, at least one identifier being non-unique between the two or more AAGs, determining an attribute value for each element of each of the two or more AAGs, storing attribute value to element mappings in an attribute dictionary, providing a differenced AAG based on the attribute value to element mappings in the attribute dictionary, determining a set of remedial actions at least partially based on the differenced AAG, and executing one or more remedial actions in the set of remedial actions to reduce a cyber security risk to the enterprise network.

Abstract: An Artificial Intelligence (AI)-based data product provisioning wherein a data product responsive to an informational requirement of a user query is identified or automatically built is disclosed. An enhanced user query generated from the received user query is used to search a plurality of data sources. Mapped search results are obtained and features of the mapped search results are used to determine if the responsive data product exists within the plurality of data sources. Else, a logical data product (LDP) including data entities required to build a responsive physical data product (PDP) is generated. The code to build the PDP is created from the LDP and executed on a target platform where the PDP is built. One or more of the PDP and an output of the PDP can be provided to the user as a reply to the user query.

Abstract: A method and system for generating structured semantic annotations from unstructured documents are disclosed. The method may include obtaining a natural language processing model trained to extract unstructured data from an unstructured document and output structured data based on a generic document ontology. The method may further include inputting a plurality of unstructured documents into the natural language processing model to obtain generic structured annotations of the unstructured documents. The method may further include aligning the generic structured annotations to a target ontology specifying a use case to generate target structured annotations for the use case. The target structured annotations may include entities and relations between the entities. The method may further include generating a knowledge representation for the use case based on the target structured annotations and generic structured annotations.

Abstract: Systems and methods for optimizing single shot detector (SSD) for object detection are disclosed herein. A system receives an image of a plurality of objects. Further, the system determines a plurality of feature layers and a plurality of feature cell sizes corresponding to the received image, based on an aspect ratio of the received image. Furthermore, the system determines aspect ratio of anchor boxes from trained model file, based on aspect ratio of anchor boxes, position and number of anchor boxes to be tiled in each feature cell of the plurality of feature layers. The size of the one or more anchor boxes corresponds to an anchor box aspect ratio. Additionally, the system assigns the one or more anchor boxes as a horizontal tile or a vertical tile in each feature cell, when the anchor box aspect ratio is less than a first pre-defined threshold value and greater than a second pre-defined threshold value, respectively.

Abstract: Methods and systems for dynamic dashboard administration are presented. Embodiments include displaying a dashboard through a graphical user interface (‘GUI’); receiving user instructions to manipulate one or more elements of the dynamic dashboard; capturing the manipulation of the one or more GUI elements; parsing, according to a gesture taxonomy and gesture ontology, the captured manipulation into one or more gesture triples; storing the gesture triples in an enterprise knowledge graph of a semantic graph database; identifying, in dependence upon the gesture triples, dashboard insights; updating the dashboard in accordance with the dashboard insights.

Abstract: The present disclosure relates to automated chatbot generation for different domains from available human-to-human chat logs. The systems and methods may be configured to cluster user utterances as well as agent utterances from the human chat logs. A data miner mines intents and entities from the user utterance clustering and mines actions from agent utterances. The intents, entities and actions mined are used to generate a set of stories or flows which are further used by a machine learning engine to train the chatbot. The stories or flows are also generated automatically by mapping the intents with the actions.