Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data. Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

Abstract: The tree harvesting tool is a tool adapted for attachment to a conventional lifter or lifting device for performing harvesting and pre-harvesting operations on a fruit tree. The tree harvesting tool includes a cylindrical shell, having an upper end and a lower end, at least one portion of the cylindrical shell defining a door. The cylindrical shell is adapted for encircling the trunk of the tree. A plurality of panels are pivotally secured to the lower end of the cylindrical shell to define an openable floor having a central opening for receiving the trunk of the tree. A circular track is mounted on the upper end of the cylindrical shell, such that a movable platform may be mounted thereon. A robotic arm is mounted on the movable platform for selectively operating and manipulating a tool for performing tree harvesting and pre-harvesting operations.

Abstract: The present disclosure provides systems and methods for in-database generation of generalized linear models within a relational database. Generalized linear models form the basis of many machine learning algorithms and applications. In particular, in some implementations, the database commands that enable generation and use of the models include only pure SQL queries, thereby eliminating the need for user defined aggregates (UDAs), which are not offered by many cloud database service providers. For example, a set of client-side driver scripts can implement respective sets of pure SQL queries to import training data, generate and train the generalized linear model, and employ the model to generate inferences.

Abstract: The present disclosure provides systems and methods for in-database generation of generalized linear models within a relational database. Generalized linear models form the basis of many machine learning algorithms and applications. In particular, in some implementations, the database commands that enable generation and use of the models include only pure SQL queries, thereby eliminating the need for user defined aggregates (UDAs), which are not offered by many cloud database service providers. For example, a set of client-side driver scripts can implement respective sets of pure SQL queries to import training data, generate and train the generalized linear model, and employ the model to generate inferences.

Abstract: Systems and methods of throttling incoming network traffic requests are provided. A data processing system can receive a request from a computing device via a computer network. The data processing system can determine a predicted number of incoming requests and a current available capacity of the data processing system. The data processing system, responsive to determining that the current available capacity of the data processing system is insufficient to process the predicted number of incoming requests, can assign a prioritization value to the request and determine a throttling threshold value based on the current available capacity of the data processing system, the predicted number of incoming requests, and a distribution of historical prioritization values. The data processing system can throttle the request responsive to determining that the prioritization value is below the determined throttling threshold value.

Abstract: Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for generating personalized user models. The method includes receiving automatic speech recognition (ASR) output of speech interactions with a user, receiving an ASR transcription error model characterizing how ASR transcription errors are made, generating guesses of a true transcription and a user model via an expectation maximization (EM) algorithm based on the error model and the respective ASR output where the guesses will converge to a personalized user model which maximizes the likelihood of the ASR output. The ASR output can be unlabeled. The method can include casting speech interactions as a dynamic Bayesian network with four variables: (s), (u), (r), (m), and encoding relationships between (s), (u), (r), (m) as conditional probability tables. At each dialog turn (r) and (m) are known and (s) and (u) are hidden.

Abstract: Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for generating personalized user models. The method includes receiving automatic speech recognition (ASR) output of speech interactions with a user, receiving an ASR transcription error model characterizing how ASR transcription errors are made, generating guesses of a true transcription and a user model via an expectation maximization (EM) algorithm based on the error model and the respective ASR output where the guesses will converge to a personalized user model which maximizes the likelihood of the ASR output. The ASR output can be unlabeled. The method can include casting speech interactions as a dynamic Bayesian network with four variables: (s), (u), (r), (m), and encoding relationships between (s), (u), (r), (m) as conditional probability tables. At each dialog turn (r) and (m) are known and (s) and (u) are hidden.