Abstract: Identifying an indistinct entity within an image can include generating by an image filter multiple gradients, each of which corresponds to one of a plurality of pixels of an image captured by an imager. The image can be searched for a likely repeating pattern. Responsive to detecting, based on the multiple gradients, a likely repeating pattern within the image, data structures can be generated, the data structures comprising a set of probabilistically weighted feature vectors corresponding to the likely repeating pattern. A machine learning model can classify each of the set of probabilistically weighted feature vectors. An identity of the likely repeating pattern can be output, the identity based on the machine learning model classifications of the probabilistically weighted feature vectors.

Abstract: Cooperative session orchestration includes devising a crypt for pre-distribution of tokens, distributing the tokens to member nodes of the network, based on a request from a delegate node of the network for brokerage of a session between the delegate node and a supplier node of the network, creating and sending, for each of a plurality of potential supplier nodes of the network, a respective individual puzzle, receiving, from each of one or more potential supplier nodes of the plurality of potential supplier nodes, a respective result obtained by the potential supplier node from solving the individual puzzle using the token distributed to the potential supplier, identifying, based on the receiving, candidate supplier node(s) of the one or more potential supplier nodes as a potential supplier for the session with the delegate node, and identifying to the delegate node the candidate supplier node(s) for the session with the delegate node.

Abstract: Prioritizing a work request pertaining to a physical asset can include generating a data structure that encodes the work request as a multidimensional representation indicating at least one classification, each at least one classification including at least one sub-category. In response to identifying multiple work requests encoded as multidimensional representations with respect to the physical asset, each multidimensional representation can be reduced to a one-dimensional (1-D) representation that preserves a variance factor of each sub-category of each multidimensional representation. Each 1-D structure can be input to a machine learning model trained to prioritize each of the work requests. The work requests can be prioritized in accordance with the machine learning model based on the 1-D structures. The priorities of each of the work requests can be output.

Abstract: An approach for assessing terms and conditions (TnC)s of a legal agreement is provided. A block of text, which contains a TnC of the set of TnC, in the legal agreement is identified using a cognitive system. A standardized significance value is assigned to the TnC by the cognitive system by applying the TnC to a TnC model containing corresponding industry TnCs from industry standard documents. A block currency that indicates a significance of an impact of the TnC to a contract party is computed for the TnC by the cognitive system. Based on the standardized significance value and the block currency, a personal impact of the TnC to the contract party is assessed by the cognitive system. The contract party is alerted to the TnC and the personal impact corresponding to the TnC.

Abstract: Provisioning business functions is provided. A runtime binary activation code is sent to a nodal edge server that has a needed runtime binary for a set of edge devices to perform a business function. A secure shell protocol connection with root operating system access is established to the nodal edge server that has the needed runtime binary to execute the runtime binary activation code.

Abstract: Setting a primary server within a business density cluster when the business density location changes. A new primary edge provisioning server replaces a current primary edge provisioning server when the business density cluster is determined not to include the current primary edge provisioning server. An edge provisioning framework handles the associations and dissociations of the edge network(s) to build the new primary server within the business density cluster. The former primary server is respawned as a new secondary node to the new primary server.

Abstract: Edge service deployment with network slice invocation is provided, which includes obtaining one or more service-related parameters for network slice invocation to support an edge service instance, and requesting a network slice from a network based on the obtained one or more service-related parameters. The requesting from the network is via a collaboration platform. Further, the edge service deployment and network slice invocation includes receiving from the network, based on requesting of the network slice, network slice invocation codes, and initiating activating of the edge service instance over the network slice of the network using the network slice invocation codes.

Abstract: Runtime binary migration is provided. A slice of a 5G network is provisioned based on time period and bandwidth requirements in accordance with a service level agreement corresponding to a customer requesting performance of a business function transaction. A runtime binary for invoking the slice of the 5G network is migrated to a nodal edge server for a set of edge devices associated with the nodal edge server to perform the business function transaction using the slice of the 5G network.

Abstract: Edge service deployment with network slice invocation is provided, which includes obtaining one or more service-related parameters for network slice invocation to support an edge service instance, and requesting a network slice from a network based on the obtained one or more service-related parameters. The requesting from the network is via a collaboration platform. Further, the edge service deployment and network slice invocation includes receiving from the network, based on requesting of the network slice, network slice invocation codes, and initiating activating of the edge service instance over the network slice of the network using the network slice invocation codes.

Abstract: A computer-implemented method according to one embodiment includes establishing, for each hop in pathways of a network, a relation between a desired reliability and channel error. Based on the established relations, a maximum number of copies of packets to send from a source in the network to a target in the network is determined. An overhead associated with sending packets from the source to the target in the network is determined for each of the hops, and a first sum of such overheads is also determined. An overhead associated with sending an acknowledgement packet to the source is determined for each of the hops. A second sum is a sum of the overheads associated with sending the acknowledgement packet to the source. In response to a determination that a sum of the first sum and the second sum is less than a predetermined threshold value, the pathways are used.

Abstract: A computer identifies, within a hierarchical database, data elements associated with a selected function associated with the database, comprising. The computer identifies at least one function associated with a hierarchical database containing data elements. The computer, in response to identifying the function, identifies within a list of indica, at least one reference indicia corresponding to the at least one function. The computer identifies within a monolithic application relevant code elements associated with the reference indicia. The computer generates an activity log associated with execution of the relevant code elements. The computer identifies, within the activity log, a group of data elements associated with the execution of the relevant code elements. The computer generates a group data element clusters using a Machine Learning algorithm. The computer identifies at least one of the group of data element clusters as relevant to the at least one function.

Abstract: Generating a model for evaluating organizational skills to determine cost of entering a new market includes training a machine learning model to define business capabilities, processes and required skills of an organization based on a current business strategy, training the machine learning model to define a plurality of skill classes of the required skills of the organization using the cognitive computing processor device, training the machine learning model to define skill profiles of the available talent of the organization based on the plurality of skill classes, determining skill gaps of the available talent of the organization by analyzing the required skills of the organization and the skill profiles, assessing skills required for a new business strategy for the organization, and determining a cost of the organization executing the new business strategy based on the skill profiles, the at least one skill gap and the new business strategy skills.

Abstract: In an approach to improve service chain node adequacy, embodiments utilize fifth-generation technology standard (5G) for broadband cellular networks architecture for creating relevant network slices for dedicated business processes, to ensure that each node within the network slice has stabilized operations. Embodiments employ an optimization technique, maximize a model of network adequacy to ensure there is a predetermined amount of adequacy to handle a request, and introduce the model for pre-processor without impacting existing functionality. Embodiments apply one or more constraints to one or more systems to balance the one or more queues; and implement a rapid packet delivery to a subsequent entity comprising decongested queues.

Abstract: A process for service chain instance pool sizing receives service requests for processing in a 5G network by a service function chain, of the 5G network, providing network function(s) to process the service requests. The method determines an average number of service requests to be processed, and determines a number of service chain instances to exist in a pool of service chain instances, of the service function chain, as a composite of (i) a number of instances to process service requests having arrived and making an initial relay through the service function chain and (ii) a number of instances to process service requests that were re-introduced after having previously arrived. The method additionally indicates the number of service chain instances to exist in the pool to a component responsible for controlling the number of service chain instances in the pool.

Abstract: Object overlay for vehicle occupants includes determining that visibility of an object in a view of a vehicle occupant through a transparent surface of the vehicle is degraded, the object being in an environment in which the vehicle travels, selecting from an image repository a reference image of the object, extracting, from the reference image, an image portion comprising an image of a least a portion of the object, transforming the extracted image portion to correspond to the view of the vehicle occupant, the transforming producing a transformed image portion, and displaying the transformed image portion on the transparent surface and interposed in a line-of-sight of the vehicle occupant to the object in the environment such that the transformed image portion overlays at least a portion of the vehicle occupant's view through the transparent surface to the object in the environment.

Abstract: A process for service chain instance pool sizing includes receiving service requests, communicated in a 5G network, into a buffer of a request handler of the 5G network. The service requests are for processing by a service function chain. The request handler is configured to relay received service requests to a pool of service chain instances of the service function chain. The process determines an average number of service requests to be processed by the service function chain, determines a number of service chain instances to exist in the pool as a composite of (i) service chain instances to process service requests making an initial relay through the service function chain and (ii) service chain instances to process service requests that were re-introduced into the buffer after having previously arrived, and indicates this composite number to a broker component that controls the number of service chain instances in the pool.

Abstract: A computer-implemented method according to one embodiment includes defining a network resilience construct of an internet-based network. The network resilience construct includes rankings for nodes of the internet-based network. The nodes of the internet-based network are manipulated to determine an Activated Core Affluence (ACA) of the internet-based network, and the ACA defines a likelihood, for each node, of retaining the ranking for the node upon a first group of the nodes losing connection with the internet-based network. The method further includes determining, based on the ACA, a resilient portion of the network that includes an activated core of the nodes. The nodes of the activated core retain connection with one or more other nodes of the internet-based network upon the first group of the nodes losing connection.

Abstract: The continual retraining of Cognitive Bots (“CogBots”) is of CogBots allows for the adaptation and evolution to ever changing environments. In this retraining process a CogBot response model continually searches the response space for potential responses in which it may shift. An approach for optimizing such retraining of CogBots may be presented herein. The approach may include receiving a prompt at a CogBot retraining framework. The approach may include analyzing the prompt and determining potential responses to the prompt. The approach may include generating a dialogue benchmark for each of the potential responses. The approach may further include generating a decision shift score for the prompt. Further, the approach may additionally include updating the CogBot retraining framework based on the generated decision shift score.

Abstract: Methods, computer program products, and systems are presented.

Abstract: A computer-implemented method for providing a framework to identify questions and answers dynamically from a dataset based on previous learning and an evaluation score of a user. The method includes creating a library of potential questions and answers from the dataset based on the previous learning and evaluation score of the user, and generating a set of personalized questions, for the user, related to the dataset by utilizing sentence-based machine translation (SBMT) and natural language processing (NLP) tools. The method further includes identifying a plurality of answers for the set of personalized questions for the user, based on collective information available in the dataset, and providing, to the user, the plurality of answers for the set of personalized questions for verification and evaluation.