Abstract: One or more communication capabilities and a plurality of versions of communication terms of a cognitive interface are identified. A probability of a particular user reaction for each communication term version is determined. A desired outcome of an interaction between a user and the cognitive interface is determined. A first communication term version is selected from the plurality of communication term versions based on the determined probabilities of the communication term versions and the desired outcome. An interaction between the user and the cognitive interface is created using the selected first communication term version. The interaction is sent to a communication device associated with the user.

Abstract: A cognitive proximate recommendation method, system, and non-transitory computer readable medium, include first extracting a requested feature and a requested value of the requested feature for a requested item and returning a return item from a plurality of return items stored in the database and returning a ranked list of the plurality of return items, where the ranked list further includes inter-relationships that are defined by defining a primary value of each of the requested items and the return feature corresponding to the requested feature or anchor value for each of the requested items and the return feature corresponding to the requested feature.

Abstract: A method includes: receiving, by a computing device, a digital voice stream; receiving, by the computing device, converted text that represents the digital voice stream; identifying, by the computing device, an erroneously converted portion of the converted text; selecting, by the computing device, the erroneously converted portion for explainability processing; parsing, by the computing device, the erroneously converted portion into parts based on a predetermined parsing level; collecting, by the computing device, supplementary input data related to the erroneously converted portion; and determining, by the computing device and based on the supplemental input data, a reason why the erroneously converted portion was erroneously converted.

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: A computer-implemented method according to one embodiment includes applying a predetermined augmentation to the sample set of training data to create an augmented sample set, training a model with the augmented sample set, determining a performance of the trained model, and assigning a weight to the predetermined augmentation for the training data set based on the determined performance. A determination is made as to whether to apply the predetermined augmentation to a larger training data set before the training data set is applied to the model, based on the weight assigned to the predetermined augmentation.

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: 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: A method, system, and computer program product for monitoring a telecom network is provided. The method includes retrieving images associated with a transmission location of a telecom network. A communication channel is initialized for enabling transmission of a real time video stream comprising the images with respect to virtual network functions. The virtual network functions are enabled and a triggering signal of the virtual network functions is located. Image collector software and hardware components are activated for collecting the images for local cache storage and location and activation information is collected. Attributes of objects within the images are collected and detecting resulting obstruction characteristics associated with obstruction objects located within a coverage area of the telecom network are detected.

Abstract: Automatic task assignment in a multiparticipant message exchange includes receiving, by a computer, data snapshots from a collaborative message exchange between one or more participants. Based on the data snapshots, the computer identifies tasks requiring completion, a task leader among participants, and a task criteria. Based on a semantic match between the task criteria and a database of historical task completion, the computer identifies a candidate pool for completing the tasks and determines a likelihood of each candidate completing the tasks. A relevancy score is assigned based on the likelihood and used to generate a list of ranked candidates for completing the tasks. The computer presents the list to the task leader, receives a selection from the task leader including at least one candidate for completing the tasks, automatically notifies the selected candidate of the tasks to be completed, and updates the database of historical task completion.

Abstract: A method, computer program product and computer system to automatically generate augment reality-based guides for maintenance procedures is provided. A processor retrieves non-structured text instructions to perform a maintenance procedure on a device within a data center. A processor extracts at least one imperative statement from the non-structured text instructions. A processor identifies a named entity in the extracted at least one imperative statement from the non-structured text instructions. A processor generates a mapping of the named entity to be used by an augmented reality device, where the mapping indicates the position of the named entity on the device within a data center. A processor provides to the augmented reality device the extracted at least one imperative statement and the mapping of the named entity, where the augmented reality device displays the extracted imperative statement and the mapping of the named entity to a user of the augmented reality device.

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: In an approach for in-band selective data snapshot using fifth generation (5G) radio link control (RLC) channels for edged cloud application backups, a processor creates one or more RLC channels to transfer network data packets in a telecommunication network using a network slicing technology. A processor allocates the one or more RLC channels to one or more applications in a user device for dedicated packet routing requirements. A processor maps the corresponding RLC channels and applications with corresponding identifiers, respectively. A processor monitors the one or more applications for backups. A processor, in response to receiving an application's backup request, creates a copy of a datastore associated with the application based on changes monitored in the one or more applications.

Abstract: Imposing and/or readjusting the CPU clocks for services of containers from various cloud-based cognitive systems in a multi-cloud cognitive computing environment for performing a particular job. The particular job having an emergency performance requirement. During processing of the job, a need to tune the execution of instructions is identified through a pre-existing emergency identification process that includes collecting clock data for each container supporting the performance of the particular job.

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: A computer-implemented method, system and computer program product for performing real-time monitoring of machine learning models. Real-time model state data and metadata (e.g., operating dataset) of the machine learning models located within an orchestration plane of a network are collected by agents located within the machine learning models. The portion of the collected real-time model state data and metadata that is to be provided to the user by the service orchestrator (configured to monitor the machine learning models in the service orchestration plane via the use of agents in the machine learning models) is selected and marked. The marked collected real-time model state data and metadata are then provided to the user by the service orchestrator. In this manner, real-time monitoring of the machine learning models in the orchestration plane, such as the service orchestration plane, of a broadband cellular network (e.g., fifth generation broadband cellular network) is achieved.

Abstract: A system, method, and computer program product for implementing dynamic telecom network agent filtering is provided. The method includes retrieving peripheral data associated with user equipment (UE) enabled with respect to a telecom network associated with a telecom network hardware device. The peripheral data is mapped with a pre-defined filter selection model and a filtering agent model is selected. Likewise, filtering agents associated with the UE are selected and environmental characteristics associated with the telecom network are retrieved. A location of the UE is tagged and the filtering agents are pushed to the UE. Network commands associated with execution of the filtering agents are generated and the filtering agents are executed with respect to the UE. In response, noise and distortion occurring during operation of the UE with respect to the telecom network is reduced.

Abstract: A computer-implemented method for altering an appearance of an electrochromic coating of a vehicle is provided. The method includes determining that a vehicle is a boundary area of a testing zone based on a determined location of the vehicle. The method also includes changing a base appearance of the electrochromic coating of the vehicle to an altered appearance based on the determination that the vehicle is in the boundary area.

Abstract: Performing container scaling and migration for container-based microservices is provided. A first set of features is extracted from each respective microservice of a plurality of different microservices. A number of containers required at a future point in time for each respective microservice of the plurality of different microservices is predicted using a trained forecasting model and the first set of features extracted from each respective microservice. A scaling label and a scaling value are assigned to each respective microservice of the plurality of different microservices based on a predicted change in a current number of containers corresponding to each respective microservice according to the number of containers required at the future point in time for each respective microservice.

Abstract: Pairing a user response and associated context with a neural network associated with a virtual assistant computer during a dynamic text conversation with an end user. The virtual assistant computer receives a detected user generated text input; determines context of the detected user generated text input; compares the context of the detected user generated text input by comparing a confidence score representing context of the user generated input to a classification associated with each of a plurality of existing nodes of a neural network. For confidence scores below a threshold relative to the classification associated with each of the existing nodes of the neural network, the virtual assistant computer creates a new node within the neural network and assigns the context of the user generated text to the new node.