A system for automated malicious software detection includes a computing device, the computing device configured to receive a software component, identify at least an element of software component metadata corresponding to the software component, determine a malicious quantifier as a function of the software component metadata, wherein determining the malicious quantifier further comprises obtaining a source repository, the source repository including at least an element of source metadata, and determining the malicious quantifier as a function of the at least an element of software component metadata and the at least an element of source repository metadata using a malicious machine-learning model, and transmit a notification as a function of the malicious quantifier and a predictive threshold.

An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.

An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to classify telemetry data into a set of categories. The processor implements a communication synchronization scheme to receive a first set of telemetry data associated with a first user and a second set of telemetry data associated with a second user. The processor applies the machine learning algorithm to each of the first and second sets of telemetry data, to classify the data. The processor transmits, to a server, training data that includes at least the classified data or a set of parameters derived from the classified data. The server uses the training data to refine a reinforcement learning algorithm that is configured to generate a recommendation of computational resources to provision to a new user.

Provided is multi-layer neural network technique that includes: calculating, from an input and using a first one or more layers of a plurality of layers of a neural network, a first intermediate output; reducing a size of one or more dimensions of the first intermediate output; calculating, from the first intermediate output and using a second one or more layers of the neural network, a second intermediate output (the second one or more layers including one or more ultra-low precision layers); reducing a size of one or more dimensions of the second intermediate output; combining a plurality of reduced intermediate outputs (including the reduced first intermediate output and the reduced second intermediate output) to derive a combined intermediate output; and calculating, using the combined intermediate output and one or more higher-precision layers of the plurality of layers, a neural network output.

A reinforcement learning (RL) based adaptive state observation model usable for implementing a brain machine interface (BMI) is proposed for decoding a brain signal to determine a movement action and controlling a machine to perform the movement action. In the model, the brain signal is processed by a neural network (NN) for applying a nonlinear mapping defined by NN weights to the brain signal to thereby yield a transformed brain signal. The NN learns the nonlinear mapping by RL, allowing the weights to be adaptively and continuously updated to follow nonlinearity and non-stationarity of the brain signal. The transformed brain signal is processed by a Kalman filter (KF) to yield a control signal for controlling the machine to perform the movement action, thereby utilizing the KF to provide smooth generation of the control signal while blocking adverse influence of nonlinearity and non-stationarity of the brain signal to the KF.

The present disclosure relates to an operating device and method of an ESS. The ESS operating method may include forecasting electricity information during a predetermined period using a deep learning model generated based on data about an electricity price and an electricity demand, deriving an ESS operating policy by a reinforcement learning model based on the forecasted electricity information and state information of an energy storage device included in the ESS, and controlling the ESS based on the derived ESS operating policy.

The present disclosure relates to an operating device and method of an ESS. The ESS operating method may include forecasting electricity information during a predetermined period using a deep learning model generated based on data about an electricity price and an electricity demand, deriving an ESS operating policy by a reinforcement learning model based on the forecasted electricity information and state information of an energy storage device included in the ESS, and controlling the ESS based on the derived ESS operating policy.

The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Provided are an autonomous driving control apparatus and method based on a Road-GNN. By using road graph-based data, a network can more accurately and efficiently understand road shape information, and driving performance is improved.