Abstract: A method for determining relationships of entities associated with interactions may include receiving interaction data associated with interactions between first and second entities. The interaction data may include first entity identification data, second entity identification data, and relative timing data. A node may be generated for each second entity. A set of edges may be generated for each first entity to include an edge connecting the node associated with second entity identification data of each interaction to the node associated with second entity identification data of a next interaction based on the relative timing data. Sample data associated with a portion of the nodes/edges may be generated. A vector for each node of the sample may be generated. A distance between each vector and other vectors may be determined. A relationship between each second entity may be determined based on the distance. Systems and products are also disclosed.

Abstract: A method for determining relationships of entities associated with interactions may include receiving interaction data associated with interactions between first and second entities. The interaction data may include first entity identification data, second entity identification data, and relative timing data. A node may be generated for each second entity. A set of edges may be generated for each first entity to include an edge connecting the node associated with second entity identification data of each interaction to the node associated with second entity identification data of a next interaction based on the relative timing data. Sample data associated with a portion of the nodes/edges may be generated. A vector for each node of the sample may be generated. A distance between each vector and other vectors may be determined. A relationship between each second entity may be determined based on the distance. Systems and products are also disclosed.

Abstract: An image classification method based on reliable weighted optimal transport (RWOT) includes: preprocessing data in a source domain, so that a deep neural network fits a sample image in the source domain to obtain a sample label; performing image labeling to add a pseudo label to a data sample in a target domain; performing node pairing to pair associated images in the source domain and the target domain; and performing automatic analysis by using a feature extractor and an adaptive discriminator, to perform image classification. The present disclosure proposes a subspace reliability method for dynamically measuring a difference between the source domain and the target domain based on spatial prototypical information and an intra-domain structure. This method can be used as a preprocessing step of an existing domain adaptation technology, and greatly improves efficiency.

Abstract: The invention provides an echo cancellation system and method of a multichannel sound mixing. The echo cancellation system comprises a voice assistant module and at least one signal generating device for respectively outputting first audio data and second audio data adapted to the configuration of a loudspeaker; a copying module for copying the first audio data and the second audio data to obtain corresponding third audio data and fourth audio data; a first sound mixing module for mixing and converting the first audio data and the second audio data to obtain two-channel first sound mixing data; a second sound mixing module for mixing the third audio data and the fourth audio data to obtain second sound mixing data; an echo cancellation module for echo cancellation according to the first sound mixing data; and a playing module for receiving and playing the second sound mixing data.

Abstract: A method for determining relationships of entities associated with interactions may include receiving interaction data associated with interactions between first and second entities. The interaction data may include first entity identification data, second entity identification data, and relative timing data. A node may be generated for each second entity. A set of edges may be generated for each first entity to include an edge connecting the node associated with second entity identification data of each interaction to the node associated with second entity identification data of a next interaction based on the relative timing data. Sample data associated with a portion of the nodes/edges may be generated. A vector for each node of the sample may be generated. A distance between each vector and other vectors may be determined. A relationship between each second entity may be determined based on the distance. Systems and products are also disclosed.