Abstract: A machine-learning classification system includes a first machine-learning classifier that classifies each element of a plurality of data items to generate a plurality of classified data items. A second machine-learning classifier identifies misclassified elements of the plurality of classified data items and reclassifies each of the identified misclassified elements to generate a plurality of reclassified data items. A second machine-learning classifier identifies unclassified elements of the plurality of classified data items and classifies each of the identified unclassified elements to generate a plurality of reclassified data items. An ensemble classifier adjusts the classifications of the elements of the plurality of classified data items in response to the plurality of reclassified data items and the plurality of newly-classified elements.

Abstract: A method for loading boxes in containers is provided. Characteristics of a set of containers having substantially equal dimensions are obtained. Characteristics of a plurality of boxes to be packed into one or more of the set of containers are obtained. A set of layer descriptors is created, each layer descriptor including a layer thickness, identifiers of boxes assigned to the layer, and a description of an arrangement of the boxes in the layer. Each of the boxes is assigned to a single layer descriptor. An optimized assignment of the layer descriptors to containers is determined. An ordered list of box loading instructions for each container is generated. The boxes are loaded in their assigned containers according to the ordered list for the container.

Abstract: A machine-learning classification system includes a first machine-learning classifier that classifies each element of a plurality of data items to generate a plurality of classified data items. A second machine-learning classifier identifies misclassified elements of the plurality of classified data items and reclassifies each of the identified misclassified elements to generate a plurality of reclassified data items. A second machine-learning classifier identifies unclassified elements of the plurality of classified data items and classifies each of the identified unclassified elements to generate a plurality of reclassified data items. An ensemble classifier adjusts the classifications of the elements of the plurality of classified data items in response to the plurality of reclassified data items and the plurality of newly-classified elements.

Abstract: A system and method of automatically assigning a priority rank to messages. The system and method accesses a message data store and assigns a priority rank to each message. The priority rank is selected from a priority rank scale by, for each message, parsing the message for features present in the message and calculating a predicted intensity score for the message using a user-specific classifier. The classifier is trained from user training data which includes prior user messages on which a machine learning algorithm operates. The training data is labeled by scores calculated based on the actual activates performed by the user to each message. The priority rank of each message can be used to improve message processing in message processing systems.

Abstract: A method for loading boxes in containers is provided. Characteristics of a set of containers having substantially equal dimensions are obtained. Characteristics of a plurality of boxes to be packed into one or more of the set of containers are obtained. A set of layer descriptors is created, each layer descriptor including a layer thickness, identifiers of boxes assigned to the layer, and a description of an arrangement of the boxes in the layer. Each of the boxes is assigned to a single layer descriptor. An optimized assignment of the layer descriptors to containers is determined. An ordered list of box loading instructions for each container is generated. The boxes are loaded in their assigned containers according to the ordered list for the container.

Abstract: A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers.

Abstract: A computer implemented method of controlling energy consumption of a battery powered device includes determining, by the device, a state of the device responsive to the device playing a video wherein the state of the device is based on a CPU utilization rate of a CPU of the device, selecting, by the device, a policy of a plurality of different policies based on the determined state, wherein each policy comprises a respective CPU frequency setting and a respective memory bandwidth setting, and applying the CPU frequency setting of the selected policy to the CPU and the memory bandwidth setting of the selected policy to a speed setting of a memory bus of the device.

Abstract: The disclosure relates to technology for generating a data set comprising random numbers that are distributed by a multivariate population distribution. A set of empirical cumulative distribution functions are constructed from a collection of multidimensional random samples of the multivariate population, where each empirical cumulative distribution function is constructed from observations of a random variable. A number of multidimensional sample points are sampled from the collection of multidimensional random samples and the number of multidimensional sample points are each replaced with random neighbors to generate cloned data.

Abstract: A method includes obtaining via a programmed computer, a first set of n random samples and a second set of n+k random samples from a base set of samples where k is a lag, iteratively adding more random samples to the first and second sets from the base set via the programmed computer, obtaining a distance between the first and second sets of random samples by calculating via the programmed computer, an empirical cumulative distribution function (ECDF) for the first and second sets in each iteration until the distance between the ECDFs is below a threshold, and constructing a stable empirical distribution representation via the programmed computer using a number of samples that is a function of the first and second sets whose distance is below the threshold.

Abstract: The disclosure relates to technology for estimating a number of samples satisfying a database query. One or more subsets from a sample dataset of a collection of all data are randomly drawn. The one or more subsets are queried to determine a number of cardinalities as training data. A prediction model based on the training data is then trained using machine learning or statistical methods, and a sample size satisfying the database query of the collection of all data is estimated using the trained prediction model.

Abstract: An apparatus and method are provided for ensemble transfer learning. One or more first (machine learning) projects that are similar to a second (machine learning) project are identified by comparing metadata of the one or more first projects and the second project, where the metadata comprises a plurality of characteristics and the characteristics of the first projects are compared to the characteristic of the second project to identify the one or more first projects. One or more (machine learning) models associated with the one or more first projects are selected as a plurality of models that each share a common feature set with the second project. Each model in the plurality of models is applied to input data for the second project to generate a set of results. Output data corresponding to the input data is produced for the second project based on the set of results.

Abstract: A method for cloning data samples in a data set based on statistic information of the data samples. The method does not use any of the data samples to perform the cloning. The statistic information includes a first set of statistic parameters obtained from a data matrix formed by data entries of the data samples based on Eckart-Young theorem, and a second set of statistic parameters indicating statistical properties of the data entries of the data samples. The data samples are reconstructed using the first and the second sets of statistic parameters based on Eckart-Young theorem.

Abstract: The disclosure relates to technology for generating a data set comprising random numbers that are distributed by a multivariate population distribution. A set of empirical cumulative distribution functions are constructed from a collection of multidimensional random samples of the multivariate population, where each empirical cumulative distribution function is constructed from observations of a random variable. A number of multidimensional sample points are sampled from the collection of multidimensional random samples and the number of multidimensional sample points are each replaced with random neighbors to generate cloned data.

Abstract: The disclosure relates to technology for generating random numbers that are distributed by the population distribution. An empirical cumulative distribution function is constructed from random samples of the population, and a first random number is generated that is uniformly distributed over a first interval. A second interval in the empirical cumulative distribution function is found such that a range of values of the second interval cover the first random number. A second random number is then generated as an approximation to the random number drawn from the population, where the second random number is employed as part of a testing process.

Abstract: The disclosure relates to technology for generating random numbers that are distributed by the population distribution. An empirical cumulative distribution function is constructed from random samples of the population, and a first random number is generated that is uniformly distributed over a first interval. A second interval in the empirical cumulative distribution function is found such that a range of values of the second interval cover the first random number. A second random number is then generated as an approximation to the random number drawn from the population, where the second random number is employed as part of a testing process.

Abstract: A system and method of automatically assigning a priority rank to messages. The system and method accesses a message data store and assigns a priority rank to each message. The priority rank is selected from a priority rank scale by, for each message, parsing the message for features present in the message and calculating a predicted intensity score for the message using a user-specific classifier. The classifier is trained from user training data which includes prior user messages on which a machine learning algorithm operates. The training data is labeled by scores calculated based on the actual activates performed by the user to each message. The priority rank of each message can be used to improve message processing in message processing systems.

Abstract: An apparatus comprises a non-transitory memory that stores a query for of electronic files and instructions. One or more processors execute the instructions to represent the plurality of electronic files as a plurality of column vectors. Each entry in a column vector represents a frequency of a word used in an electronic file. The query is represented as a query vector with each entry representing a frequency of a word used in the query. A topic space is formed from the plurality of column vectors. Each column vector in the term-document-matrix is projected into the topic space to obtain new representations of the plurality of electronic files. The query vector is projected into the topic space to obtain a new representation of the query. A similarity score is calculated between each representation of the electronic files with the representation of the query to obtain a plurality of similarity scores.

Abstract: An apparatus and method are provided for spatial processing of concepts. Included is a non-transitory memory comprising a distance matrix and instructions, where the distance matrix includes values representing dissimilarities among a plurality of concepts stored in a knowledgebase. Further included is one or more processors in communication with the memory. The one or more processors execute the instructions to derive an inner product matrix, based on the distance matrix. Further, a spectral decomposition of the inner product matrix is performed. Based on the spectral decomposition of the inner product matrix, a plurality of concept vectors are generated. Information associated with the plurality of concept vectors is then output for spatial processing.

Abstract: A sampling method includes responsive to a sequence of elements, of length n, determining a number of samples k as a step function k(n) of the number of elements, and selecting k(n) samples from the n elements as a sample list.

Abstract: A method includes obtaining, at one or more processors, data comprising multiple variables corresponding to multiple samples in a very large dataset, defining, via the one or more processors, multiple sets of variables occurring in the samples comprising a set of x variables and a set of y variables, where the intersection of the sets is zero, for each set of variables, determining, via the one or more processors, a support for each set and a union of each set, determining, via the one or more processors, an interest for each of the multiple association rules of the sets of variables, and determining, via the one or more processors, a chi squared interest, (?2 interest), for each association to identify related sets of variables, including almost exclusive relationships.