Configurations for data center component monitoring are disclosed. In at least one embodiment, movement of a server component is determined based on sensor data and the movement is used to diagnose a root cause for a server component failure.

Provided are a cyber threat information processing apparatus, a method thereof, and a storage medium storing a cyber threat information processing program. It is possible to provide a cybersecurity threat information processing method including disassembling an input executable file to obtain disassembled code, and reconstructing the disassembled code to obtain reconstructed disassembled code, into a hash function, and converting the hash function into N-gram data (N being a natural number), and performing ensemble machine learning on block-unit code of the converted N-gram data to profile the block-unit code by an identifier of an attack technique performed by the block-unit code and an identifier of an attacker generating the block-unit code. It is possible to detect and address a variant of malware, and identify malware, an attack technique, an attacker, and an attack prediction method within a significantly short time even for a variant of malware.

A method for analyzing a viewing direction of an electronic component includes inputting a package type and a file image of an electronic component, with the file image having at least one engineering drawing image, and the at least one engineering drawing image being a view of the electronic component in at least one viewing direction; querying and acquiring a viewing direction detection model meeting the package type from a database, with the database storing respective viewing direction detection models of different package types of electronic components; inputting the file image into the viewing direction detection model of the package type to identify the viewing direction of the at least one engineering drawing image; and outputting the viewing direction of the at least one engineering drawing image of the electronic component.

A method for analyzing a viewing direction of an electronic component includes inputting a package type and a file image of an electronic component, with the file image having at least one engineering drawing image, and the at least one engineering drawing image being a view of the electronic component in at least one viewing direction; querying and acquiring a viewing direction detection model meeting the package type from a database, with the database storing respective viewing direction detection models of different package types of electronic components; inputting the file image into the viewing direction detection model of the package type to identify the viewing direction of the at least one engineering drawing image; and outputting the viewing direction of the at least one engineering drawing image of the electronic component.

The present disclosure relates generally to a gaming system, device, and method supportive of an enhanced electronic gaming machine auto play mode. A gaming system, device, and method are provided that identify data associated with a set of previous gameplay sessions, the data including a set of previous gameplay decisions associated with the set of previous gameplay sessions; generate a set of playstyle models based on the set of previous gameplay decisions; and enable a gameplay mode in which a playstyle model of the set of playstyle models may be utilized to provide automated player inputs to a gameplay session.

The present disclosure relates generally to a gaming system, device, and method supportive of an enhanced electronic gaming machine auto play mode. A gaming system, device, and method are provided that identify data associated with a set of previous gameplay sessions, the data including a set of previous gameplay decisions associated with the set of previous gameplay sessions; generate a set of playstyle models based on the set of previous gameplay decisions; and enable a gameplay mode in which a playstyle model of the set of playstyle models may be utilized to provide automated player inputs to a gameplay session.

A system and method for detecting anomalies in very large datasets is disclosed. The method includes calculating statistics for data elements in a data set over a range of time periods. These statistics are arranged into a 2D array and analyzed using a machine learning algorithm to detect anomalous regions. The method also includes steps of analyzing time series of the data based on detected anomalous regions, correcting any errors in the datasets, and storing the corrected values in a separate database to maintain data integrity.

A system and method for detecting anomalies in very large datasets is disclosed. The method includes calculating statistics for data elements in a data set over a range of time periods. These statistics are arranged into a 2D array and analyzed using a machine learning algorithm to detect anomalous regions. The method also includes steps of analyzing time series of the data based on detected anomalous regions, correcting any errors in the datasets, and storing the corrected values in a separate database to maintain data integrity.

Data compression includes: inputting data comprising a vector that requires a first amount of memory; compressing the vector into a compressed representation while preserving information content of the vector, including: encoding, using one or more non-quantum processors, at least a portion of the vector to implement a quantum gate matrix; and modulating a reference vector using the quantum gate matrix to generate the compressed representation, wherein the compressed representation requires a second amount of memory that is less than the first amount of memory; and outputting the compressed representation to be displayed, stored, and/or further processed.

A technique is described herein for automatically logging journeys taken by a user, and then automatically classifying the purposes of the journeys. In one implementation, the technique obtains journey data from one or more movement-sensing devices as a user travels from a starting location to an ending location in a vehicle. The technique generates a set of features based on the journey data, and then uses a machine-trainable model (such as a neural network) to make its classification based on the features. The machine-trainable model accepts at least one feature that is based on statistical information regarding at least one aspect of prior journeys that the user has taken. Overall, the technique provides a resource-efficient solution that rapidly provides personalized results to individual respective users. In some implementations, the technique performs its personalization without sharing journey data with a remote server.