An anomaly detection method of objects in a digital image is provided, wherein the image of the object is encoded and decoded by an autoencoder, then a pixel-wise difference is calculated between the input image of the object, and the reconstructed image of the object. Pixels whose pixel-wise difference is above a threshold are considered as dissimilar pixels, and the presence of clusters of dissimilar pixels is tested. A cluster of dissimilar pixel is considered as representing an anomaly.

Provided is a method for calculating an evaluation score of clustering quality, based on the number of clusters into which a plurality of data is clustered. The calculating the evaluation score includes: calculating a degree of internal compactness that is a sum of values, each being defined by normalizing a first index value by a first value that is based on a number of data within each cluster, the first index value indicating a degree of dispersion of data within each cluster; calculating a degree of external separation defined by normalizing a sum of a second index value for each cluster by a second value that is based on the number of clusters, the second index value indicating an index of a distance between the clusters; and calculating the evaluation score according to a predetermined formula having, as variables, the degree of internal compactness and the degree of external separation.

Systems and methods for flexible, multi-constraint risk segmentation.

Example systems may include a head-mounted device configured to present an artificial reality view to a user, a control device including a plurality of electromyography (EMG) sensors, and at least one physical processor programmed to receive EMG data based on signals detected by the EMG sensors, detect EMG signals corresponding to user gestures within the EMG data, classify the EMG signals to identify gesture types, and provide control signals based on the gesture types, wherein the control signal triggers the head-mounted device to modify the artificial reality view. Various other methods, systems, and computer-readable media are also disclosed.

A cyber defense system using models that are trained on a normal behavior of email activity and user activity associated with an email system. A cyber-threat module may reference the models that are trained on the normal behavior of email activity and user activity. A determination is made of a threat risk parameter that factors in the likelihood that a chain of one or more unusual behaviors of the email activity and user activity under analysis fall outside of a derived normal benign behavior. An autonomous response module can be used, rather than a human taking an action, to cause one or more autonomous rapid actions to be taken to contain the cyber-threat when the threat risk parameter from the cyber-threat module is equal to or above an actionable threshold.

Techniques for adaptive thresholding are provided. A first data point in a data stream is received, and a first plurality of data points from the data stream is identified, where the first plurality of data points corresponds to a timestamp associated with the first data point. At least a first cluster is generated for the first plurality of data points, and a predicted value for the first data point is generated based at least in part on data points in the first cluster. A deviation is computed between the predicted value for the first data point and an actual value for the first data point. Upon determining that the deviation exceeds a first predefined threshold, the first data point is labeled as anomalous, and reallocation of computing resources is facilitated based on labeling the first data point as anomalous.

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes processor circuitry to execute computer readable instructions to: execute a machine learning model to generate a first code recommendation for programming code, the first code recommendation being associated with security of the programming code; cause output of the first code recommendation via a user interface; update the machine learning model based on feedback obtained via the user interface; determine a performance of the programming code; generate a second code recommendation, the second code recommendation being associated with the performance of the programming code; and cause output of the second code recommendation via the user interface.

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes processor circuitry to execute computer readable instructions to: execute a machine learning model to generate a first code recommendation for programming code, the first code recommendation being associated with security of the programming code; cause output of the first code recommendation via a user interface; update the machine learning model based on feedback obtained via the user interface; determine a performance of the programming code; generate a second code recommendation, the second code recommendation being associated with the performance of the programming code; and cause output of the second code recommendation via the user interface.

An example system includes a processor to receive a data set and similarity scores. The processor is to execute an eigen response analysis on eigenvectors calculated for a similarity matrix generated based on the similarity scores for the data set. The processor is to output an estimated number of clusters in the data set based on the eigen response analysis.

An example system includes a processor to receive a data set and similarity scores. The processor is to execute an eigen response analysis on eigenvectors calculated for a similarity matrix generated based on the similarity scores for the data set. The processor is to output an estimated number of clusters in the data set based on the eigen response analysis.