Abstract: Using labelled training content, a content classification model is trained. Using the trained content classification model, a label describing a first content is determined. The first content is classified into a category in a set of categories using the label. Responsive to the first content being classified into a category of inappropriate content, the first content is removed from a storage location.

Abstract: A method for contrastive learning by selecting dropout ratios and locations based on reinforcement learning includes receiving training data having a positive sample corresponding to a target and negative samples not corresponding to the target. A dropout policy for a neural network is produced based on the training data, where the dropout policy identifies at least one connection between neurons in the neural network to dropout. The training data is encoded, based on the dropout policy, to form embeddings, where the embeddings include multiple positive sample embeddings corresponding to the positive sample and multiple negative sample embedding corresponding to the negative samples.

Abstract: In an approach for road section recognition using multi-modal cognitive mechanism, a processor receives an audio signal from a road test. A processor processes the audio signal to generate an acoustic spectrum density distribution map to identify a respective at least one road section switching point in a first mode. A processor processes a spectrogram of the audio signal to identify the respective at least one road section switching point in a second mode. A processor uses a machine learning model to predict an expected sound at each frame of the audio signal, to calculate a similarity between the expected sound and an actual sound, and to identify the respective at least one road switching point when the similarity is lower than a pre-set similarity threshold in a third mode. A processor combines results of the three modes to obtain a final set of road section switching points.

Abstract: In order to perform a semantic search based on a graph database, sets of nodes are selected from a plurality of nodes in a graph database. A set of nodes semantically matches a keyword in a natural language query. At least one target node is identified in the sets of nodes. A path is selected from candidate paths based on similarities between the candidate paths and a plurality of paths in the graph database. A graph query for retrieving information from the graph database is generated based on the selected path and the query target.

Abstract: Embodiments of the present disclosure present and approach for automatic generation of short names for a named entity. According to the approach, a standard text segment is obtained, which indicates a full name of a named entity. At least one feature representation of the standard text segment is extracted. A plurality of variant text segments are generated based on the at least one feature representation using a generative learning network. The plurality of variant text segments indicate a plurality of short names for the named entity, the generative learning network characterizing a generation of variants for an input text segment. The plurality of variant text segments are stored in association with the standard text segment into a data repository.