Methods, systems, and computer readable media for generating and using a web page classification model are disclosed. The method may include identifying a plurality of web pages for generating a web page classification model, assigning a label to each of the plurality of web pages, accessing Transmission Control Protocol/Internet Protocol (TCP/IP) traffic traces associated with downloading content from each of the plurality of web pages, processing TCP/IP headers from the TCP/IP traffic traces to identify and extract features that discriminate between the labels, that are uncorrelated and whose discriminatory accuracy remains stable across time and/or browser platform. The method may further include generating a web page classification model by training a trainer to learn a combination of the features that accurately discriminates between the labels. The model is usable to classify unlabeled web pages by applying the model to TCP/IP traffic traces used to access the unlabeled web pages.

An explanatory variable display means 81 extracts an explanatory variable used as a condition from a classification model classified by the condition for selecting a component used for prediction and displays the explanatory variable in association with any of dimensional axes of a multi-dimensional space in which a prediction value is displayed. A prediction value display means 82 specifies the component that corresponds to a position in the multi-dimensional space specified by each of the explanatory variables associated with the dimensional axis, and then, displays the prediction value calculated on the basis of the specified component, on the same position. A space display means 83 displays the multi-dimensional space that corresponds to the position in which the prediction value is displayed, in a mode that corresponds to the component used for calculating the prediction value.

An anonymization processing device that anonymizes input data and outputs anonymized output data, includes an input unit configured to receive the input data; a processing unit configured to anonymize the input data, to generate anonymized data corresponding to the input data that has been anonymized; a first storage unit configured to store the anonymized data; and an output unit configured, in a case where a plurality of anonymized data items stored in the first storage unit satisfy an anonymity index, to generate and output a plurality of output data items corresponding to the anonymized data items, respectively, and to delete the anonymized data items from the first storage unit.

Examples disclosed herein relate to segmentation based on clustering engines applied to summaries. In one implementation, a processor segments text based on a comparison of the output of multiple clustering engines applied to multiple summarizations of documents associated with the text. The processor outputs information related to the contents of the segments.

A multi-layer semiconductor structure includes a first semiconductor structure and a second semiconductor structure, with at least one of the first and second semiconductor structures provided as a superconducting semiconductor structure. The multi-layer semiconductor structure also includes one or more interconnect structures. Each of the interconnect structures is disposed between the first and second semiconductor structures and coupled to respective ones of interconnect pads provided on the first and second semiconductor structures. Additionally, each of the interconnect structures includes a plurality of interconnect sections. At least one of the interconnect sections includes at least one superconducting and/or a partially superconducting material.

A method for generating a trip prediction specific to a given user includes acquiring a first dataset of trip histories taken in a given transportation network; dividing a trip history of a given user at a specific time point into user training and validation datasets; acquiring training datasets each associated with candidate neighboring users; identifying useful neighbors from the training and validation datasets; combining the user trip history and the trip history of each useful neighbor; applying a similarity function to the combined dataset, wherein a sum of similarities between a given trip and all other trips in the combined dataset is computed; associating a trip having the highest weighted similarity (weighted by frequency) with a prediction for a future trip; and outputting the prediction to an associated user device.

Systems and methods for pre-processing data to facilitate efficient and accurate machine learning are provided. The data may include market data. The pre-processing may include partitioning the data into windows assigning categories to windows generate a series of vectors.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as bollard receivers. A method for generating virtual sensor data includes simulating a 3-dimensional (3D) environment that includes one or more objects, such as bollard receivers. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions. The ground truth includes information about at least one bollard receiver within the sensor data. For example, the ground truth may include a height of the at least one of the parking barriers. The method also includes storing and associating the virtual sensor data and the virtual ground truth.

There is disclosed a computer-implemented method of generating a content recommendation for a user of an electronic device, the method executable by a recommendation, the content recommendation being associated with a content item available at one of a plurality of network resources accessible via the communication network. The method comprises: executing a first machine learning algorithm module in order to determine a sub-set of recommended content sources from a plurality of possible content sources that is based on at least some of a first sub-set of user-specific content sources and a generated second sub-set of user-non-specific content sources; analyzing the sub-set of recommended content sources to select a plurality of potentially-recommendable content items; executing a second machine learning algorithm module in order to select, from the plurality of potentially-recommendable content items, at least one recommended content item; the selection being made on the basis of a user-profile-vector.

In an example, a processing device of a database system may identify a set of machine learning features; generate a first hash map of said set of machine learning features and a second different hash map of said set of machine learning features. The processing device may generate a memory compact model for an online machine learning system using the first and second hash maps, and store the memory compact model in the memory device.