Abstract: An approach is provided in which the approach receives a request to upload a file at a server system that includes metadata encoded with a non-invertible key. The metadata includes contact information corresponding to an owner of the file. The approach establishes both a photon channel and a classical channel between the server system and a client system, which are both secured using one or more shared secret keys. The approach interfaces with the client system over the photon channel and the classical channel to decode the contact information at the server system, and sends an upload request from the server system to the owner of the file using the contact information. The approach authorizes the upload request at the server system in response to receiving an upload approval from the owner.

Abstract: In an approach for predictive forecasting of food allocation, a first data is received from one or more sensors. The amount and condition of available food is determined from the first data. The number and location of people is determined from the first data. The received data is modified to create a second data. One or more food requirements for the people are predicted based on the number and location of people and the second data. An optimal food allocation for the people is predicted based on the amount and condition of food available and the one or more food requirements. The optimal food allocation is reported.

Abstract: Computer-implemented methods, computer program products, and computer systems for detecting and resolving fraudulent calls. The computer-implemented methods include one or more processors configured for receiving voice call data corresponding to an incoming telephone call placed to a user device, wherein the voice call data comprises caller voice data. Further, the computer-implemented methods may include one or more processors configured for converting the caller voice data to caller text data comprising one or more text phrases. Further embodiments of the computer-implemented method include processors configured for determining that the one or more text phrases satisfies a first condition, and responsive to determining that the one or more text phrases satisfies the first condition, transmitting a user alert to the user device.

Abstract: Emergency monitoring application mobility management is provided. A portion of battery power on a mobile device is reserved for an emergency monitoring application between defined boundaries of a certain area based on analysis of a profile identifying battery consumption of each respective application running on the mobile device. A dedicated logical communication channel is established with the mobile device that the emergency monitoring application connects to in a detected emergency situation between the defined boundaries of the certain area. The dedicated logical communication channel is utilized to shut down a number of applications running on the mobile device during the detected emergency situation so that the reserved portion of battery power is retained to run the emergency monitoring application on the mobile device between the defined boundaries of the certain area during the detected emergency situation.

Abstract: Selective compression of packet payload data in a 5G network includes receiving a congestion signal by user equipment (UE) connected to a broadband cellular network based on network traffic congestion in hardware of the network and indicating the network traffic congestion, determining current processor utilization of the UE, based on the received congestion signal, the determined current processor utilization, and known times for data compression and decompression, determining whether to automatically enable selective packet payload data compression on the UE, and based on determining to automatically enable selective packet payload data compression, compressing payload data of data packet(s) generated by the UE, and forwarding the data packet(s) with the compressed payload data for transmission on the broadband cellular network.

Abstract: Described herein are platforms, systems, media, and methods for assessing the resiliency of reasoning of an expert system comprising a natural language interface and a graph database.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining prediction data that are indicative of media use behaviors of a user over a period of time. Prediction on a pattern of media use behaviors of the user including media content, hours spent on the media content, and the period of time is made. Base on the predicted media use pattern corresponding to a current stage in the period of time, a media use control rule for the user on a controlled device is generated.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: transmitting, during a media streaming session, streaming media to a user equipment (UE) device of a user, the transmitting streaming media including simultaneously transmitting a first media stream and a second media stream to the UE device; subjecting the first media stream to processing by natural language processing to provide a topic extracted from the first media stream; subjecting the second media stream to processing by natural language processing to provide an extracted topic extracted from the second media stream; identifying a match between the topic and the extracted topic; and providing one or more output in response to the identifying the match between the topic and the extracted topic.

Abstract: A method, computer program product, and a system where a processor(s) obtains data from one or more sources, wherein a portion of the one or more sources comprise training data for a first artificial intelligence decision-making system. The processor(s) ingest data from each source into a corpus and ingest metadata corresponding to the data, into a volume accessible to a second artificial intelligence decision-making system. The processor(s) search public sources and obtain information describing quality and non-objective influence of the data from each source. The processor(s) provide as inputs to the second artificial intelligence decision-making system, the information and the metadata, to classify the quality and the non-objective influence of each source. The processor(s) obtains outputs comprising, for each source, a first rating classifying the quality of the source of the one or more sources.

Abstract: By analyzing execution of a set of transactions by an application, a set of actual code execution paths of the application are determined. From the set of actual code execution paths, a set of predicted execution paths of the application are predicted using an execution prediction model. The set of predicted execution paths includes the set of actual code execution paths. By determining that paths in the set of predicted execution paths have above a threshold similarity to each other, a cluster of predicted execution paths is identified. The cluster of predicted execution paths is recommended, using a recommendation model, for implementation as a single execution path in a revised version of the application.

Abstract: A neural network system for generating a value estimation is provided. A computing device analyzes one or more digital media streams. A computing device identifies one or more parameters of the object in one or more digital media streams. A computing device collects information of an object in one or more digital media streams. A computing device generates a precited value estimation of the object via invocation of additional multi-level input collection in a distributed cognitive system. A computing device communicates a report associated with the predicted value estimation of the object to a user of a computing device.

Abstract: An embodiment for determining a drop-off location of a passenger is provided. The embodiment may include receiving a pick-up location and drop-off location from one or more passengers. The embodiment may also include identifying the one or more passengers to be picked up from a passenger profile. The embodiment may further include identifying one or more factors associated with each passenger. The embodiment may also include in response to determining the drop-off location is not appropriate, notifying the one or more passengers of an alternative drop-off location. The embodiment may further include in response to determining the drop-off location is appropriate, dropping the one or more passengers off at the drop-off location. The embodiment may also include in response to determining the one or more passengers are not responsive to the notification, dropping each passenger who did not respond off at the alternative drop-off location.

Abstract: In an approach for improving operational efficiencies in IoT agricultural ecosystems, a processor receives a request from a user for a production plan to modify one or more values of a set of values of an area of interest. A processor maps the area of interest using a set of data originating from a LIDAR instrument to create a digital elevation model of the area of interest. A processor creates a knowledge set to train a decision engine. A processor calculates one or more modifications to be made to the one or more values of the set of values of the area of interest. A processor generates a map of applicable land options that the user may interact with to select the one or more modifications to be made to the one or more values of the set of values. A processor plots a finalized rendering of the production plan.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: generating a plurality of deep transfer learning networks including a source deep transfer learning network for a source domain and a target deep transfer learning network for a target domain. Transfer layers of the source deep transfer learning network are encoded to a chromosome, diversified, and integrated with the target deep transfer learning network and the target deep transfer learning network passing a predefined fitness threshold condition is produced.

Abstract: Systems and methods for automated resolution of over-specification and under-specification in a knowledge graph are disclosed. In embodiments, a method includes: determining, by a computing device, that a size of an object cluster of a knowledge graph meets a threshold value indicating under-specification of a knowledge base of the knowledge graph; determining, by the computing device, sub-classes for objects of the knowledge graph; re-initializing, by the computing device, the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph; and generating, by the computing device, an output based on information determined from the refined knowledge graph.

Abstract: Embodiments for implementing intelligent audio composition guidance for a video by a processor. One or more acoustic characteristics used in a plurality of video segments may be identified, from a corpus, as having similar acoustic, linguistic, and visual characteristics of a selected video segment.

Abstract: Utilization data of a computer by a user during a session is gathered. A domain in which the user is utilizing the computer during the session is identified as reflected by the utilization data. Competence data on how competent the user is with the domain is gathered. The competence data relates to how the user interacts with the computer during the session. A competence level of the user for the domain is determined based on the competence data.

Abstract: A computer-implemented method for determining whether an electric vehicle (EV) requires a current charge. The method analyzes a set of EV data, wherein the set of EV data comprises a battery level, a destination, a current position, and a predicted arrival time to the destination. The method further constructs a charging regulation model for the EV, based on the analyzed set of EV data. The method further computes a risk score pertaining to charging the EV, based on the constructed charging regulation model for the EV, and determines whether the EV requires a current charge based on the computed risk score. The method further engages one or more wireless charging points on a roadway, if the computed risk score is below a threshold value.

Abstract: Computer-implemented methods, computer program products, and computer systems for detecting and resolving fraudulent calls. The computer-implemented methods include one or more processors configured for receiving voice call data corresponding to an incoming telephone call placed to a user device, wherein the voice call data comprises caller voice data. Further, the computer-implemented methods may include one or more processors configured for converting the caller voice data to caller text data comprising one or more text phrases. Further embodiments of the computer-implemented method include processors configured for determining that the one or more text phrases satisfies a first condition, and responsive to determining that the one or more text phrases satisfies the first condition, transmitting a user alert to the user device.

Abstract: An embodiment includes analyzing a first digital image using a first trained neural network that classifies the first image as having a first context based on a plurality of characteristics of the first image. The embodiment generates a data structure that associates first and second elements depicted in the first image based on an affinity detected between the first and second elements in the first context. The embodiment executes a querying process that searches for information requested by a user, and detects that a search result from the querying process conveys a second context that is different from the first context. The embodiment identifies a third element in the data structure having an affinity with the first element in the second context. The embodiment renders a machine-generated image that is generated using a second trained neural network and depicts the first element and the third element in the second context.