Abstract: Some embodiments include a method for detecting and interrupting a cache-based side-channel attack. The method includes: (1) at least calibrating one or more chiplets of a network by calculating a threshold; (2) determining one or more device heartbeat vectors of the one or more chiplets, the one or more device heartbeat vectors being derived at least part from one or more measurements of activity of one of more dedicated security processors associated with the one or more chiplets; (3) determining that a particular chiplet of the one or more chiplets is being attacked with a cache-based side-channel attack, the determining being based at least in part on a computed disparity exceeding the threshold; and (4) employing countermeasures against the cache-based side-channel attack of the particular chiplet, the countermeasures including revoking one or more access rights of the particular chiplet on the network.

Abstract: A computer-implemented method includes training at least a generative adversarial network, the method operable on one or more processors. The method includes at least (1) applying pattern extraction to a set of training data to extract one or more feature embeddings representing one or more features of the training data, (2) attenuating the one or more feature embeddings to create one or more attenuated feature embeddings, (3) providing the one or more attenuated embeddings to a generator of the generative adversarial network as a condition to at least partly control the generator in generating synthetic data, the providing being performed automatically and dynamically during training of the generator, and (4) with the generator, generating synthetic data based at least in part on the attenuated embeddings.

Abstract: In some embodiments, an edge device is configured to execute machine learning procedures with a sparse dataset. The edge device includes at least (1) one or more sensor interfaces, (2) one or more microcontrollers (MCUs), and one or more memories in communication with the one or more microcontrollers. The one or more memories contain one or more executable instructions that cause the one or more microcontrollers to perform operations that include at least: (a) receiving one or more batches of real-time sensor data via the one or more sensor interfaces, the one or more batches defining the sparse dataset, and creating one or more batches of augmented data with the one or more batches of real-time sensor data and one or more batches of generated synthetic data. In some embodiments the edge device is a resource-constrained edge device.

Abstract: A computer-implemented method includes training at least a generative adversarial network, the method operable on one or more processors. The method includes at least (1) applying pattern extraction to a set of training data to extract one or more feature embeddings representing one or more features of the training data, (2) attenuating the one or more feature embeddings to create one or more attenuated feature embeddings, (3) providing the one or more attenuated embeddings to a generator of the generative adversarial network as a condition to at least partly control the generator in generating synthetic data, the providing being performed automatically and dynamically during training of the generator, and (4) with the generator, generating synthetic data based at least in part on the attenuated embeddings.

Abstract: The present invention relates to a method and apparatus for compression and decompression of a numerical file. The compression method comprises: read a numerical file, convert each numerical element into a 32-bit floating point number; combine all the numbers to form a binary numerical file; group the binary numerical file into a n-bit sequence pattern; generate a Huffman tree based on frequency of occurrences of a plurality of unique bit patterns present in the binary numerical file; generate codewords and replace unique bit patterns with codewords so that a compressed binary numerical file is generated. A method for decompression comprises: read a compressed binary numerical file having codewords; fetch a part or entire compressed binary numerical file using an address dictionary; replace the codewords with unique bit patterns using a Huffman tree such that a decompressed binary numerical file being generated.