Techniques are disclosed for scheduling artificial intelligence model partitions for execution in an information processing system. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A reversed computation graph corresponding to a computation graph generated based on the intermediate representation is obtained. Nodes in the reversed computation graph represent functions related to the artificial intelligence model, and one or more directed edges in the reversed computation graph represent one or more dependencies between the functions. The reversed computation graph is partitioned into sequential partitions, such that the partitions are executed sequentially and functions corresponding to nodes in each partition are executed in parallel.

A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

Sub-systems of air handling units in infrastructures face unresolved problem of conflict in the rules that activate in a contradictory manner at the same time resulting in sub-optimal performance of the subsystems. The present disclosure provides a system and method for optimizing performance parameters of air handling units in infrastructures. Rule sets having conflicting conditions are identified after verification of rules which are specific to air handling units. Further, frequency of the rule sets having conflicting conditions is determined to generate a ranked list of the rule sets having conflicting conditions. Another ranking procedure is implemented for the rules comprised in the ranked list of the rule sets having conflicting conditions. The system dynamically optimizes one or more parameters specific to the performance criteria based on the ranking of rules.

The present invention is a combined commodity mining method based on knowledge graph rule embedding, comprising: expressing rules, commodities, attributes, and attribute values as embeddings; splicing and inputting the embeddings of the rules and the embeddings of the attributes into a first neural network to obtain a importance scores of the attributes; splicing and inputting the rules and attributes into a second neural network to obtain the embeddings of the attribute values that the rules should take under the attributes; calculating a similarity between the value of two inputted commodities under the attribute and the embedding of the attribute value calculated by a model; after calculating scores of all attribute-attribute value pairs, summing up to obtain scores of these two commodities under this rule; then making the cross entropy loss with the real scores of these two commodities, and iteratively training based on an optimization algorithm having gradient descent; after the model is trained, parsing the embeddings of the rules in a similar way to obtain rules that can be understood by human beings.

A computer-implementable method employs radio signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one radio communication protocol. The inferred function is used to map metadata of a detected radio signal to a cognitive profile of a transmitter of the detected radio signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected radio signal. A response to the transmitter is based upon the mapping.

Establishing event-specific trust through data-centric mediation by: generating a mediated covenant of association as an instance of trust among a plurality of entities at an association layer of a multi-layer computer security system; constructing a security model enforceable by the multi-layer computer security system that expresses node-node semantic relationships as links among nodes of the model representing protectable computing resources; and producing an event-specific security model via informatic convolution of elements of the covenant with elements of the security model, so that the event-specific security model is operable to constrain a computing action among computing resources represented by the plurality of entities.

Synthesizing a control object for a computing event, the control object for securing a computing resource based on a set of access and privilege information provided through a set of mediated associations that are represented by an enchained set of certificates, portions of which are encrypted including entity-specific paths to entity-specific predecessor certificates and partial decryption keys therefor, wherein the control object is applied to secure the computing resource for performing a computing action indicated by a process-type entity identified in the certificate for the control object.

Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.

A transposable identity enchainment system for diffuse identity management processing entities for each of users, data, and processes equivalently and having a recombinant access mediation system that mediates association among entities, an associational process management system that creates entity-defining indices, and a multi-dimensional enchainment system that enchains aspects of entity identities via mediated association certificates including at least one root certificate for at least one of the entities.

Diffusing a root identity of an entity among association and event covenants in a multi-dimensional computing security system involves generating a first generation of diffusion of identities of entities participating in mediated association and generating a second generation of diffusion of identities of the entities through recombinant mediated association of the entities and at least one other entity. The second generation of diffusion of identities facilitates securely constraining a computing system action associated with one of the entities.