Abstract: In an approach to RLC channel management for low memory 5G devices, responsive to detecting a memory overload in an RLC layer of a 5G user equipment, whether slices of a plurality of slices are merger candidates is determined. Responsive to determining that the slices are merger candidates, whether any merger candidates can share a transportation logical entity is determined, where merger candidates can share the transportation logical entity if performance and quality parameters are within predetermined limits. The merger candidates that can share the transportation logical entity are marked as allowed candidates. Responsive to determining that at least one allowed candidate has a workload that is below a predetermined threshold, the allowed candidates are merged into merged flows.

Abstract: Masking a route by determining a first location of a first user, receiving destination data for the first user, generating a plurality of map segments according to the first location and the destination data, presenting a first map segment to a second user, determining a second location of the first user, and presenting a second map segment to the second user according to the second location.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining medical health user data from respective ones of a plurality of UE devices by a virtual network function (VNF) layer which VNF layer has assigned logical channels to respective ones of the UE devices for wireless transmission of the user data, wherein the VNF layer maintains user to logical channel association data associating user identifying data to logical channels assigned to users identified by the user identifying data; examining, by a service orchestration layer that runs on top of the VNF layer, medical health data of the user medical health data, wherein the service orchestration layer is configured so that the service orchestration layer is restricted from accessing the user identifying data of the user to logical channel association data; and performing processing in dependence on the examining.

Abstract: An approach is provided in which the approach establishes a connection between a first data site and a second data site. The first data site executes a first aggregation model that includes multiple first machine learning models and the second data site executes a second aggregation model that includes multiple second machine learning models. The approach detects, at the first data site, a change of data in a selected one of multiple first training data buckets corresponding to a selected one of the multiple first machine learning models. The approach sends the change of data from the first data site to the second data site, and replicates at the second data site, one of multiple second data buckets corresponding to a selected one of the multiple second machine learning models based on the change of data.

Abstract: A computer executes a discovery protocol, where the discovery protocol identifies each of the machine learning models and groups the machine learning models into one or more virtual groups based on criteria, and where the auto discovery program is injected to each of the machine learning models. The computer identifies an input to a machine learning model, where the input comprises a plurality of features that processed by the machine learning model. Based on determining a distance of the input is above an acceptable threshold the computer identifies an alternative machine learning model from the machine learning models, and transfers the input to the alternative machine learning model.

Abstract: A system, method, and computer program product for implementing data replication generation is provided. The method includes utilizing hardware and software resources within a hybrid cloud environment. A non-volatile memory host system and an associated target system are enabled for operational functionality and the non-volatile memory host system is connected to an I/O queueing component. In response, a plurality of queue structures is generated with respect to a host driver component and a connection between the non-volatile memory host system and the associated target system is detected. In response, a special purpose cache structure is generated and the plurality of queue structures and the special purpose cache structure are enabled such that remote data mirroring functionality is enabled.

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 method, computer program product, and computer system for transmitting an ACK in response to receipt of a data packet in RLC AM mode operation in a 5G communication protocol stack. A first RLC entity receives from a receiving UE corresponding to the first RLC entity: (i) an ACK to be sent to a transmitting UE in response to the receiving UE having received a data packet from the transmitting UE and (ii) a first RLC channel extracted by the receiving UE from a header of the data packet. In response to a first communication having specified that the first RLC channel is congested, the first RLC entity selects a second, different RLC channel operating in the RLC AM mode and not being congested. The first RLC entity sends the ACK to the transmitting UE via the second RLC channel instead of via the first RLC channel.

Abstract: In an approach, a processor identifies that a vehicle has been exposed to a first user, the first user associated with an infectious disease. A processor monitors, via a wireless ad hoc network, presence of a mobile device at a location of the vehicle. A processor identifies the presence of the mobile device at the location of the vehicle. A processor sends a notification to the mobile device, the notification specifying that the vehicle has been exposed to the infectious disease.

Abstract: A computer-implemented method for selectively excluding data from compression prior to uplink to a telecommunication system. In an embodiment, the method analyzing a request by an application, executing within a device, to wirelessly uplink data to a telecommunication node. The method further includes establishing a set of logical channels assigned to the application. The method further includes transferring the data among one or more logical channels, of the set of logical channels, based on a status flag related to data compression corresponding to the data. The method further includes determining that the data is transferred via a compression-disabled logical channel, of the set of logical channels. The method further includes responding to determining that the data is transferred via the compression-disabled logical channel by bypassing a data compression function prior to preparing the data for uplink. The method further includes uplinking the data to the telecommunication node.

Abstract: In an approach, a processor identifies that a vehicle has been exposed to a first user, the first user associated with an infectious disease. A processor monitors, via a wireless ad hoc network, presence of a mobile device at a location of the vehicle. A processor identifies the presence of the mobile device at the location of the vehicle. A processor sends a notification to the mobile device, the notification specifying that the vehicle has been exposed to the infectious disease.

Abstract: An approach for managing memory usage in cloud and traditional environments using usage analytics is disclosed. The approach may be implemented in a computer infrastructure including a combination of hardware and software. The approach includes determining that space is available within one or more tables which have schema definitions with string fields having a predefined length. The approach further includes creating a virtual table and mapping the available space to the virtual table for population by one or more records.

Abstract: A computer-implemented method according to one embodiment includes receiving a training data set to be applied to a model; selecting a subset of the training data set as a sample set; for each of a plurality of predetermined augmentations, applying the predetermined augmentation to the sample set to create an augmented sample set, training the 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; and selecting one or more of the plurality of predetermined augmentations to be applied to the training data set before the training data set is applied to the model, based on the weight assigned to each of the plurality of predetermined augmentations.

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: 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: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can allocate a dedicated traffic channel to a plurality of connected devices. Embodiments of the present invention can dynamically determine an appropriate visualization for one or more connected devices of the plurality of connected devices based on received information. Embodiments of the present invention render the dynamically determined appropriate visualization for the one or more connected devices of the plurality of connected devices using the dedicated traffic channel.

Abstract: A method, apparatus and computer program for detecting natural language (NL) bias by a conversational system is described. Embodiments of the invention determine an NL bias in a set of training questions used to train a machine learning model used by the conversational system to select a user intent. Other embodiments of the invention determine an NL bias in a user question received by the conversation system as compared to the set of training questions. The NL bias causes the machine learning to preferentially associate user queries to a user intent. In respective embodiments, the system takes a corrective action to adjust the NL bias of the training questions or the user question.

Abstract: An approach is provided in which the approach detects a set of personal information data corresponding to a set of users in a set of training data. The approach transforms the set of training data into a set of semantically equivalent training data by replacing the set of personal information with a set of semantic equivalent data. The approach then trains a machine learning model using the set of semantically equivalent training data.

Abstract: A computer-implemented method includes establishing, by a computer device, an activity to be performed by a robot; determining, by the computer device, a required knowledge that is required for the robot to perform the activity; comparing, by the computer device, the required knowledge to a current knowledge of the robot to establish an additional learning that is needed for the robot to perform the activity; requesting, by the computer device, the additional learning; directing, by the computer device, retrieval of the additional learning to the robot if the additional learning is available for retrieval; and requesting, by the computer device, that the additional learning be created if the additional learning is not available for retrieval.

Abstract: A processor may identify that a user is interacting with a device, where the interacting is identified from the user touching the device. The processor may receive tactile data associated with the user from one or more tactile sensors. The processor may extract, utilizing an AI model, one or more features of the tactile data. The processor may classify, utilizing the AI model, the tactile data as having a tactile data characteristic. The processor may output the classification to an interaction management module.