Maximum expressivity can be received representing a ratio between maximum and minimum input weights to a neuron of a neural network implementing a weighted real-valued logic gate. Operator arity can be received associated with the neuron. Logical constraints associated with the weighted real-valued logic gate can be determined in terms of weights associated with inputs to the neuron, a threshold-of-truth, and a neuron threshold for activation. The threshold-of-truth can be determined as a parameter used in an activation function of the neuron, based on solving an activation optimization formulated based on the logical constraints, the activation optimization maximizing a product of expressivity representing a distribution width of input weights to the neuron and gradient quality for the neuron given the operator arity and the maximum expressivity. The neural network of logical neurons can be trained using the activation function at the neuron, the activation function using the determined threshold-of-truth.

Generally discussed herein are devices, systems, and methods for improving phishing webpage content detection. A method can include identifying first webpage content comprises phishing content, determining, using a reinforcement learning (RL) agent, at least one action, generating, based on the determined at least one action and the identified first webpage content, altered first webpage content, identifying that the altered first webpage content is benign, generating, based on the determined at least one action and second webpage content, altered second webpage content, and training, based on the altered second webpage content and a corresponding label of phishing, a phishing detector.

A method to detect anomalous behavior in a computing system begins by training a graph neural network (GNN) in an unsupervised manner by applying contrastive representation learning on sets of positive samples and negative samples derived from one or more heterogeneous graphs using meta-path sampling. Following training, a temporal graph derived from system-generated events is received. The GNN is used to embed the temporal graph into a vector representation in a vector space. The trained GNN is also used to embed a set of attack pattern graphs into corresponding vector representations in the vector space. For anomaly detection, the representation corresponding to the temporal graph is compared to the representations corresponding to the attack pattern graphs. In one embodiment, the comparison is implemented using a fuzzy pattern matching algorithm. If a fuzzy match is found, an indication that the temporal graph is associated with a potential attack on the computing system is then output.

Systems and methods related to resource distribution for a fleet of machines are disclosed. A system may include a fleet of machines each having an associated resource capacity and a resource requirement to perform a task. The system may further include a controller having a resource requirement circuit to determine an aggregated amount of the resource requirement and an aggregated amount of the resource capacity. A resource distribution circuit may adaptively improve, in response to an aggregated amount of the resource capacity, an aggregated resource delivery of the resource.

An uncertainty prediction apparatus includes an artificial neural network model trained based on deep learning, sampling models modeled by at least two weights obtained through sampling during a training process for the artificial neural network model, and an output generation unit configured to generate a result value reflecting an uncertainty degree by aggregating values output from the artificial neural network model and the sampling models after the same data is input to the artificial neural network model and the sampling models.

Techniques regarding discovering configuration information for one or more computer applications are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a configuration component that can discover configuration information associated with a containerized computer application. The configuration information can be characterized by a set of environment attributes extracted by querying a source code of the containerized computer application.

A method and system for artificial intelligence-based radiofrequency ablation parameter optimization and information synthesis are provided. The method is applied to a radiofrequency ablation controller including a processor and an artificial intelligence module. The processor of the radiofrequency ablation controller preprocesses sample data and sends the preprocessed sample data to the artificial intelligence module. The artificial intelligence module establishes an artificial neural network model according to the preprocessed sample data and a radiofrequency ablation control parameter for the sample data. The processor preprocesses signals collected by sensors on a plasma wand. The artificial intelligence module imports preprocessed sensor data into the artificial neural network model for analysis and fusion, to obtain the radiofrequency ablation control parameter.

Disclosed is a water circulation intelligent sensing and monitoring system based on differentiable reasoning, including a processor module, wherein a data terminal of the processor module is connected to a feature knowledge base module, an intelligent sensing module and an intelligent control module, respectively; the intelligent sensing module is connected to the feature knowledge base module through a conversion module; the feature knowledge base module includes a differentiable reasoning unit, a feature knowledge base unit and a feature knowledge graph unit; and a data terminal of the feature knowledge graph unit is connected to the differentiable reasoning unit, the feature knowledge base unit and the conversion module. The system aims to solve the technical problems of low precision, low efficiency, long time consumption and complicated operation in an existing water environment monitoring and control method, and provides a water circulation intelligent sensing and monitoring system based on differentiable reasoning.

An automated workflow process for integration of services with a data estate of an external entity. A plurality of application data models are obtained that are associated with of a plurality of applications on a cloud-based platform. A data model is obtained that corresponds to a collection of data to be processed. Mappings are determined between the data model and the plurality of application data models. A candidate application of the plurality of applications is identified for processing the collection of data based on the mappings. A communication suggesting the candidate application for processing the collection of data is sent to the external entity over one or more networks.

An automated workflow process for integration of services with a data estate of an external entity. A plurality of application data models are obtained that are associated with of a plurality of applications on a cloud-based platform. A data model is obtained that corresponds to a collection of data to be processed. Mappings are determined between the data model and the plurality of application data models. A candidate application of the plurality of applications is identified for processing the collection of data based on the mappings. A communication suggesting the candidate application for processing the collection of data is sent to the external entity over one or more networks.