Abstract:
Embodiments include method, computer program products and apparatuses for normalizing non-numeric features of files and corresponding apparatus. Aspects include segmenting at least one pair of positive instances of a non-numeric feature of a file into a number of tokens and -comparing the tokens in the at least one pair of positive instances to obtain matching tokens. Aspects also include calculating weights of their matching the file, for the matching tokens, and storing the tokens and their weights in a token base.
Abstract:
Systems and methods for obtaining vehicle operational data and driving context data from one or more monitoring systems, including converting the obtained vehicle operational data and driving context data into sequential vehicle operational feature data and sequential driving context feature data, calibrating the sequential vehicle operational feature data and the sequential driving context feature data temporally to form calibrated sequential vehicle operational feature data and calibrated sequential driving context feature data, constructing a sequence table of temporal sample points based on the calibrated sequential vehicle operational feature data and the calibrated sequential driving context feature data, feeding the sequence table into a deep neural network model for applying network learning to form a trained deep neural network model, extracting driving behavior features from the trained deep neural network model and analyzing the extracted driving behavior features to determine driving behavior characteristics of the driver.
Abstract:
A method for recognizing a primitive in an image includes recognizing at least one primitive in the image to obtain at least one candidate shape of the at least one primitive, which at least one candidate shape has a respective confidence; determining whether the recognizing of the at least one primitive has a potential error based on the confidence; obtaining auxiliary information about the at least one primitive from a user in response to determining that the recognizing has the potential error; and re-recognizing the at least one primitive at least in part based on the auxiliary information.
Abstract:
A performance prediction model for a target data analytics application, where: (i) a reference data analytics application similar to the target data analytics application is determined; (ii) a configuration-performance data pair of the target data analytics application are acquired; and (iii) the performance prediction model for the target data analytics application is determined based on the configuration-performance data pair of the target data analytics application and a configuration-performance data pair of the at least one reference data analytics application. This can reduce the time required to accumulate the configuration-performance data pairs for determining the performance prediction model by combining the configuration-performance data pairs of the existing data analytics applications, thereby accelerating determination of the performance prediction model.
Abstract:
A method of generating a predictor to classify data includes: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data. The first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively.
Abstract:
Embodiments of the present disclosure relate to a new approach for image orientation detection. In the computer-implemented method, at least one character area in an image is sampled. The orientation of the sampled character area is determined, and the orientation of the image is determined based on the determined orientation of the sampled character area.
Abstract:
Determining a characteristic of a configuration file that is used to discover configuration files in a target machine, a computer identifies, using information associated with a configuration item of a machine, a candidate configuration file related to the configuration item of the machine, from among a plurality of files from the machine. The computer extracts a value of a feature of the candidate configuration file and aggregates the candidate configuration file with a second candidate configuration file related to the same configuration item identified from among a plurality of files from a second machine, based on the extracted value. The computer then determines a configuration file related to the configuration item from among the aggregated candidate configuration files based on a result of the aggregation, and determines a characteristic of the configuration file related to the configuration item.
Abstract:
A vehicle domain multi-level parallel buffering and context-based streaming data pre-processing system includes a first data processing level and a second data processing level. The first data processing level includes a first-level buffer configured to buffer data provided from a plurality of raw data streams output from a plurality of vehicles. The second data processing level includes an electronic task-queue-dictionary (TQD) module and a plurality of second-level data processing buffers. The TQD module is configured to create a plurality of tasks in response to receiving a serial data stream output from the first-level buffer. The TQD module is further configured to assign each task to a corresponding second-level buffer, and separate the serial data stream into individual data values that are delivered to a specific second-level buffer based on the task so as to generate a multi-level parallel context-based buffering operation.
Abstract:
Determining a characteristic of a configuration file that is used to discover configuration files in a target machine, a computer identifies, using information associated with a configuration item of a machine, a candidate configuration file related to the configuration item of the machine, from among a plurality of files from the machine. The computer extracts a value of a feature of the candidate configuration file and aggregates the candidate configuration file with a second candidate configuration file related to the same configuration item identified from among a plurality of files from a second machine, based on the extracted value. The computer then determines a configuration file related to the configuration item from among the aggregated candidate configuration files based on a result of the aggregation, and determines a characteristic of the configuration file related to the configuration item.
Abstract:
Embodiments are described for minimizing a wait time for a rider after sending a ride request for a vehicle. An example computer-implemented method includes receiving a ride request, the request being for travel from a starting location to a zone in a geographic region during a specified timeslot. The method further includes predicting travel demand based on a number of ride requests in the zone during the specified timeslot. The method further includes requesting transport of one or more vehicles to the zone in response to the predicted number of ride requests when the travel demand is predicted to exceed a number of vehicles in the zone during the specified timeslot.