Embodiments of the present invention relate to a method, system, and computer program product for multiplexing data operation. In some embodiments, a method is disclosed. A query for at least one table comprising a plurality of data records is received. The query indicating a plurality of data operations to be performed on the plurality of data records. The plurality of data operations are combined into a target data operation. An intermediate result of the query is generated by performing the target data operation on the plurality of data records. A final result of the query is determined based on the intermediate result. In other embodiments, a system and a computer program product are disclosed.

A cloud security system and method implements cloud activity threat detection using analysis of cloud usage user behavior. In particular, the cloud security system and method implements threat detection for users, cloud service providers, or tenants (enterprises) of the cloud security system who are new or unknown to the cloud security system and therefore lacking sufficient cloud activity data to generate an accurate behavior model for effective threat detection. In accordance with embodiments of the present invention, the cloud security system and method performs user behavior analysis to generate generalized user behavior models for user groups, where each user group includes users with similar cloud usage behavior. The user behavior models of the user groups are assigned to users with sparse cloud activity data. In this manner, the cloud security system and method of the present invention ensures effective threat detection by using accurate and reliable user behavior models.

A cloud security system and method implements cloud activity threat detection using analysis of cloud usage user behavior. In particular, the cloud security system and method implements threat detection for users, cloud service providers, or tenants (enterprises) of the cloud security system who are new or unknown to the cloud security system and therefore lacking sufficient cloud activity data to generate an accurate behavior model for effective threat detection. In accordance with embodiments of the present invention, the cloud security system and method performs user behavior analysis to generate generalized user behavior models for user groups, where each user group includes users with similar cloud usage behavior. The user behavior models of the user groups are assigned to users with sparse cloud activity data. In this manner, the cloud security system and method of the present invention ensures effective threat detection by using accurate and reliable user behavior models.

Even if an existing learning dataset is limited, a new learning dataset with sufficient variation is generated. Therefore, for each of a plurality of learning data subsets, new input signals are generated from input signals of a plurality of pieces of learning data, and a plurality of pieces of new learning data that are respectively combinations of the new input signals and output signals of the corresponding learning data subset are generated. The input signals of the plurality of pieces of the learning data included in the corresponding learning data subset are divided into a first signal group and a second signal group, and the new input signals are generated by a learning device that is generated by performing learning by the first signal group set as an input signal set and the second signal group set as an output signal set.

The present application relates to a device for selecting top values from a set of raw values, comprising: an output queue, a loop queue, a top value storage module and a control module. The control module is configured to, at a higher priority, merge the intermediate sequence stored in the loop queue with the at most N top values stored in a storage area of the top value storage module, and sort the merged values to generate a merged sequence, until a predetermined number of storage areas in the top value storage module are traversed; wherein the control module is further configured to, when there is no intermediate sequence being stored in the loop queue, merge the output sequence with the at most N top values stored in a storage area of the top value storage module, and sort the merged values to generate a merged sequence; wherein the control module is further configured to provide a first subsequence in the merged sequence which is closer to a top most value of the merged sequence to the top value storage module to update the top value storage module, and provide a second subsequence in the merged sequence which is farther away from the top most value of the merged sequence to the loop queue to generate or update the intermediate sequence.

Embodiments of the present disclosure provide an AI capability research and development platform and a data processing method. The AI capability research and development platform includes: a data management module, a tool management module, a process management module and a model management module, where the data management module is configured to perform data processing on received data, including at least one of the following: analyzing data type of the data, converting the data according to preset data format and storing the data; the tool management module is configured to store at least one tool, each tool being used to execute a preset processing flow; the process management module is configured to perform model training according to the tool provided by the tool management module and the data provided by the data management module; the model management module is configured to store a model obtained by the model training.

Embodiments of the present disclosure provide an AI capability research and development platform and a data processing method. The AI capability research and development platform includes: a data management module, a tool management module, a process management module and a model management module, where the data management module is configured to perform data processing on received data, including at least one of the following: analyzing data type of the data, converting the data according to preset data format and storing the data; the tool management module is configured to store at least one tool, each tool being used to execute a preset processing flow; the process management module is configured to perform model training according to the tool provided by the tool management module and the data provided by the data management module; the model management module is configured to store a model obtained by the model training.

A system and methods for designing single-stage hardware sorting blocks, and further using the single-stage hardware sorting blocks to reduce the number of stages in multistage sorting processes, or to define multiway merge sorting networks.

Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An example apparatus for decoding receipts includes, vertex feature representation circuitry to extract features from optical-character-recognition (OCR) words, polar coordinate circuitry to: calculate polar coordinates of the OCR words based on respective ones of the extracted features, graph neural network circuitry to generate an adjacency matrix based on the extracted features, post-processing circuitry to traverse the adjacency matrix to generate cliques of OCR processed words, and output circuitry to generate lines of text based on the cliques of OCR processed words.

Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An example apparatus for decoding receipts includes, vertex feature representation circuitry to extract features from optical-character-recognition (OCR) words, polar coordinate circuitry to: calculate polar coordinates of the OCR words based on respective ones of the extracted features, graph neural network circuitry to generate an adjacency matrix based on the extracted features, post-processing circuitry to traverse the adjacency matrix to generate cliques of OCR processed words, and output circuitry to generate lines of text based on the cliques of OCR processed words.