Abstract: An example operation may include one or more of generating a proposal to perform a ledger operation at a first node, informing one or more second nodes of the proposal, receiving a decision on consensus among the first node and the one or more second nodes for the proposal, and performing the ledger operation at the first node when there is consensus, wherein the ledger operation changes a state database of a ledger of the first node and wherein the state database corresponds to a blockchain stored in the ledger.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: Automatically generating audit logs is provided. Audit log statement insertion points are identified in components of an application based on a static code analysis identifying start and end operations on sensitive data in the components of the application. The application is instrumented with audit log statements at the audit log statement insertion points in the components of the application. Audit logs of monitored sensitive data activity events in the application are generated using the audit log statements at the audit log statement insertion points in the components of the application.

Abstract: A method, computer system, and a computer program product for text bias identification and correction are provided. A first text corpus may be received. A designation of a second text corpus may be received. Words of the first text corpus may be embedded as a first word embedding in an embedding model. The first word embedding may be compared to a second word embedding in the embedding model to identify a first biased text in the first text corpus. The second word embedding may be from the second text corpus. A first replacement text portion may be generated as a substitute for the first biased text. The first replacement text portion may include a first unbiased text. The first biased text and the first replacement text portion may be presented.

Abstract: An example operation may include one or more of retrieving a snapshot of key values of a world state database, retrieving a hash of the snapshot from a blockchain associated with the world state database, determining whether the snapshot is valid based on the hash of the snapshot retrieved from the blockchain, and in response to determining the snapshot is valid, updating key values of a world state database based on the snapshot of key values.

Abstract: A method, system, and non-transitory compute readable medium for a fraud detection including a calendar reading device configured to read calendar data of a calendar for a user, a purchase detection device configured to detect an attempted purchase using a credit-card of the user, and a risk detection device configured to detect fraud by calculating a risk based on the calendar data and data related to the attempted purchase.

Abstract: A method, system, and/or computer program product controls a self-driving vehicle (SDV) based on a purchase transaction at a point of sale (POS) device. One or more processors detects a purchase transaction at a device for a product. In response to detecting the purchase transaction at the POS device, the processors(s) transmits instructions to the SDV to transport a passenger associated with the purchase transaction from a location of the POS device to a predetermined location.

Abstract: A method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. One or more processors detects that the aerial drone has entered a predetermined airspace, and also determines a physical size of the aerial drone. In response to detecting that the aerial drone has entered the predetermined airspace, one or more processors directs the aerial drone to alter a velocity of the aerial drone based on its physical size.

Abstract: A method provides a network-agnostic identity broker for retrieving identity records across heterogeneous identity networks. An identity broker receives a client request from a client to retrieve and evaluate user identity information for confirming an identity of a particular entity. The identity broker utilizes a group membership of the client to select a set of policies for handling the client request, and selects an identity network from multiple heterogeneous identity networks as a selected identity network to which the client request is to be sent. The identity broker sends the client request to the selected identity network, and then receives a response from the selected identity network. The identity broker evaluates the response according to the set of policies, such that the evaluated response conforms with the set of policies, and transmits the evaluated response to the client.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: Embodiments of the present invention provide systems and methods for thwarting attempts at the unauthorized access to the restricted resources within the target server in a multi-node system. Real-time detection of the user ID and thread ID associated with attempts to access the restricted resources within the target server in a multi-node system is achieved by analyzing causality, message queue, and event-driven patterns.

Abstract: A method trains a neural network to recognize whether a resource is authorized to be returned to a requester. One or more processors train a neural network to traverse a policy enforcement hypergraph in order to identify a security policy to be used for a resource request and to authorize a use of a requested resource by a requester. The policy enforcement hypergraph is derived from a policy enforcement graph that expresses a set of security profiles for resources and requesters. The processor(s) receive a resource request for a requested resource from a requester, where the resource request includes a description of the requester. A system/user inputs a description of the received resource request and a description of the policy enforcement hypergraph into the trained neural network in order to selectively return the requested resource to the requester.

Abstract: A method alters a computer resource while in a particular geolocation. One or more processors detect that a geolocation of a computer resource has changed from an initial geolocation to a new geolocation, where the computer resource is a virtual machine (VM). In response to detecting that the geolocation of the VM has changed from the initial geolocation to the new geolocation, the processor(s) reduce a functionality of the VM by reducing a quantity of processors and storage space available to the VM at the new geolocation as compared to the initial geolocation.

Abstract: An example operation may include one or more of receiving, by a blockchain node or peer of a blockchain network, attribute data for a user profile, creating blockchain transactions to store attribute hashes and metadata to a shared ledger, receiving a user profile query from an identity consumer, creating blockchain transactions to retrieve attribute hashes and metadata corresponding to the query, reconstructing the user profile from the metadata, responding to the query by providing attribute data to the identity consumer, and creating and storing hashes of the attribute data and metadata to the shared ledger.

Abstract: An explainable artificially intelligent (XAI) application contains an ordered sequence of artificially intelligent software modules. When an input dataset is submitted to the application, each module generates an output dataset and an explanation that represents, as a set of Boolean expressions, reasoning by which each output element was chosen. If any pair of explanations are determined to be semantically inconsistent, and if this determination is confirmed by further determining that an apparent inconsistency was not a correct response to an unexpected characteristic of the input dataset, nonzero inconsistency scores are assigned to inconsistent elements of the pair of explanations.

Abstract: A method trains a neural network to recognize whether a resource is authorized to be returned to a requester. One or more processors train a neural network to traverse a policy enforcement hypergraph in order to identify a security policy to be used for a resource request and to authorize a use of a requested resource by a requester. The policy enforcement hypergraph is derived from a policy enforcement graph that expresses a set of security profiles for resources and requesters. The processor(s) receive a resource request for a requested resource from a requester, where the resource request includes a description of the requester. A system/user inputs a description of the received resource request and a description of the policy enforcement hypergraph into the trained neural network in order to selectively return the requested resource to the requester.

Abstract: A method for utilizing data analytics to identify unauthorized consumption of content comprises obtaining social network activity data from one or more social networks. The obtained social network activity data is analyzed to identify one or more users involved with content associated with a content provider. A determination is made as to whether or not the one or more users are authorized to access the content based at least in part on the analysis.

Abstract: A method routes an identity query to a particular identity network. An identity broker determines that a candidate identity network is associated with a confidence score that satisfies predetermined criteria. Responsive to determining that the candidate identity network is associated with the confidence score that satisfies the predetermined criteria, the identity broker onboards the candidate identity network into a set of identity networks services, and then routes an identity query for an identity to the candidate identity network that satisfies the predetermined criteria.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.