Described is a system for providing quick and efficient identification of a desired version of content from an editing history of the content. The system receives a search index identifying versions of content from an editing history of the content. The system sorts the search index according to sort criteria and receives a selection from the sorted search index of a first version of the content and a second version of the content. The system identifies and displays one or more content differences between the first and second versions of the content.

A data generating method includes obtaining first sample data, determining a type of the first sample data and a corresponding data expansion method, expanding the first sample data according to the determined data expansion method to generate second sample data, and dividing the first sample data and the second sample data into a training set and a verification set according to a preset rule. A data model is trained according to the training set, and the data model is verified according to the verification set after training.

A data generating method includes obtaining first sample data, determining a type of the first sample data and a corresponding data expansion method, expanding the first sample data according to the determined data expansion method to generate second sample data, and dividing the first sample data and the second sample data into a training set and a verification set according to a preset rule. A data model is trained according to the training set, and the data model is verified according to the verification set after training.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.

A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.

The present disclosure relates to a method for representing an ordered group of symbols with a hypervector. The method comprises sequentially applying on at least part of the input hypervector associated with a current symbol a predefined number of circular shift operations associated with the current symbol, resulting in a shifted hypervector. A rotate operation may be applied on the shifted hypervector, resulting in an output hypervector. If the current symbol is not the last symbol of the ordered group of symbols the output hypervector may be provided as the input hypervector associated with a subsequent symbol of the current symbol; otherwise, the output hypervector of the last symbol of the ordered group of symbols may be provided as a hypervector that represents the ordered group of symbols.

A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to receive a read command form a host device, collect environment data of the memory device, decode data associated with the read command, determine a bit error rate (BER) of the decoded data, compare the BER to a threshold, and determine whether the data associated with the read command is to be relocated. The environment data includes temperature, number of program/erase cycles, amount of grown defects, number of past relocations and time since last data relocation. The controller is further configured to dynamically adjust the threshold based on the collected environment data and an amount of time that has passed since a last relocation of the read command data.

A computing system includes an anonymizer server. The anonymizer server is communicatively coupled to a data repository configured to store a personal identification information (PII) data. The anonymizer server is configured to perform operations including receiving an anonymized data request, and creating an anonymized data repository based on the anonymized data request. The anonymizer server is also configured to perform operations including anonymizing the PII data to create an anonymized data by applying a cluster-based process, and storing the anonymized data in the anonymized data repository.

A computing system includes an anonymizer server. The anonymizer server is communicatively coupled to a data repository configured to store a personal identification information (PII) data. The anonymizer server is configured to perform operations including receiving an anonymized data request, and creating an anonymized data repository based on the anonymized data request. The anonymizer server is also configured to perform operations including anonymizing the PII data to create an anonymized data by applying a cluster-based process, and storing the anonymized data in the anonymized data repository.

A method for determining a model compression rate comprises determining a near-zero importance value subset from an importance value set associated with a machine learning model, a corresponding importance value in the importance value set indicating an importance degree of a corresponding input of a processing layer of the machine learning model, importance values in the near-zero importance value subset being closer to zero than other importance values in the importance value set; determining a target importance value from the near-zero importance value subset, the target importance value corresponding to a turning point of a magnitude of the importance values in the near-zero importance value subset; determining a proportion of importance values less than the target importance value in the importance value set in the importance value set; and determining the compression rate for the machine learning model based on the determined proportion.