Abstract: An identity authentication method is disclosed in embodiments of the present application. When a requester and an authentication access controller perform identity authentication using an authentication mechanism of a pre-shared key, the identity information of entities is transmitted in the form of ciphertext, thereby preventing the identity information of the entities from being exposed during the transmission, so that attackers cannot obtain private or sensitive information. The mutual or unilateral identity authentication between the authentication access controller and the requester is achieved while ensuring the confidentiality of the entity identity and related information, thereby laying a foundation for ensuring that the user accessing the network is legitimate and/or the network accessed by the user is legitimate.

Abstract: In an example embodiment, a model is trained to specifically identify reversal points in data and then to rank these reversal points in order of importance. A reversal point shall be defined as a point in which a particular metric, specifically a first order derivative, crosses over from positive to negative or vice-versa. Users are more likely to be interested in abnormal and significant changes in data, and thus the machine-learned model is trained to evaluate a reversal point based on two dimensions: abnormality and significance.

Abstract: A parallel decoder for decoding compressed video picture data including inter-coded picture item data with motion vector data. A decoding module decodes picture data stored in a temporary storage. The decoding module includes an inter-prediction module that uses inter-prediction item data to decode an inter-coded picture item by referring to already decoded reference picture item data. The structure of inter-prediction item data in the temporary storage is a function of the positions of corresponding reference picture items. The decoding order of stored inter-prediction item data by the inter-prediction module is prioritized as a function of a decoding order of reference picture item data.

Abstract: A multi-core decoder for decoding compressed video picture data decodes compressed video picture data. Multi-core processing resources parse compressed video picture data, and decode structures of picture data stored in a temporary storage. A control module adapts the resources of the cores by allocating at least one core to parse picture data serially, and allocating other cores to decode picture data in parallel. The multi-core processing resources are allocated between parsing and decoding picture data as a function of a workload parameter related to the relative workloads of the parsing and decoding operations.

Abstract: A parallel decoder for decoding compressed video picture data including inter-coded picture item data with motion vector data. A decoding module decodes picture data stored in a temporary storage. The decoding module includes an inter-prediction module that uses inter-prediction item data to decode an inter-coded picture item by referring to already decoded reference picture item data. The structure of inter-prediction item data in the temporary storage is a function of the positions of corresponding reference picture items. The decoding order of stored inter-prediction item data by the inter-prediction module is prioritized as a function of a decoding order of reference picture item data.