Abstract: Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

Abstract: The present application discloses a data processing method and apparatus. A specific implementation of the method includes: receiving floating point data sent from an electronic device; converting the received floating point data into fixed point data according to a data length and a value range of the received floating point data; performing calculation on the obtained fixed point data according to a preset algorithm to obtain result data in a fixed point form; and converting the obtained result data in the fixed point form into result data in a floating point form and sending the result data in the floating point form to the electronic device. This implementation improves the data processing efficiency.

Abstract: A multiple imputation (MI) based fuzzy clustering with visualization-aided MI validation that improves the accuracy and the stability of identified patterns, generally the structure of HD data with missing values.

Abstract: Systems and methods are provided for categorizing products using Al. One method comprises retrieving initial training data including products associated with one or more categories; pre-processing the initial training data to generate synthesized training data; generating a hierarchical model using the synthesized training data, the hierarchical model containing at least two layers of nodes below a root node; receiving information associated with a first uncategorized product; and receiving a request to predict a set of N categories with the highest N total probability scores.

Abstract: Augmenting a dataset in a machine learning classifier is disclosed. One example is a system including a training dataset with at least one training data, and a label preserving transformation including an occluder, and an inpainter. The occluder occludes a selected portion of the at least one training data. The inpainter inpaints the occluded portion of the at least one training data, where the inpainting is based on data from a portion different from the occluded portion. In one example, the augmented dataset is deployed to train a machine learning classifier.

Abstract: A system for information exchange comprises a vehicle electronics data broker gateway for exchanging information between vehicle electronics certified applications and uncertified applications. The data broker gateway comprises configuration files generated with a dedicated modeling tool; a source selection module operative to seamlessly choose a best data source; a source abstraction and data collection module operative to receive data from the data source; a data conversion module operative to convert the data received into a standard format; a data cache operative to store the data received before transmitting the data; and a client abstraction and data dispatch module operative to transmit the data to the client. The data broker gateway also includes a data pre-fetch module comprising a rule based engine operative to determine a time to pre-fetch data based on pre-defined rules; and a machine learning based engine operative to learn data fetching conditions for a given data source.

Abstract: This document generally describes a neural network training system, including one or more computers, that trains a recurrent neural network (RNN) to receive an input, e.g., an input sequence, and to generate a sequence of outputs from the input sequence. In some implementations, training can include, for each position after an initial position in a training target sequence, selecting a preceding output of the RNN to provide as input to the RNN at the position, including determining whether to select as the preceding output (i) a true output in a preceding position in the output order or (ii) a value derived from an output of the RNN for the preceding position in an output order generated in accordance with current values of the parameters of the recurrent neural network.

Abstract: Change points of a system represented by a plurality of time series are detected more appropriately. An information processing system includes means for learning, with respect to each of a plurality of time series, models that approximate partial time series respectively and are defined by parameters of the partial time series respectively, the partial time series being obtained by dividing a corresponding time series into a plurality of segments at change point candidates; and means for detecting, with respect to each of the change point candidates for the plurality of time series, a global change point that is a change point for the plurality of time series based on a difference between a parameter of a first partial time series starting from a time point of a corresponding change point candidate and a parameter of a second partial time series before the corresponding change point candidate, and outputting the global change point.

Abstract: A first indication from a user is received. The indication includes a task to be performed using at least one application programming interface. A machine learning model is determine. At least one application programming interface is determined using the machine learning model and the request. The at least one application programming interface is provided to the user.

Abstract: Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.

Abstract: A computational system for solving a non-deterministic polynomial problem, involving a parellel processor operable by a set of self-updatable executable instructions storable on a non-transient memory device and configuring the parallel processor to interface with an information acquisition program, heuristically acquire information, through the information acquisition program, via a first transference and a second transference, whereby heuristic information is acquired, parametrically evolve the heuristic information, whereby parametrically evolved heuristic information is provided, reuse the parametrically evolved heuristic information to further heuristically acquire information, whereby iterative parametrically evolved heuristic information is provided, and self-update the set of executable instructions based on the iterative parametrically evolved heuristic information, whereby a self-updated set of executable instructions is provided, whereby the self-updated set of executable instructions facilitates i

Abstract: Methods and systems for estimating latent service and latent wait times include extracting transition times between activities from a partial event log. Parameters for respective gamma distributions are estimated that characterize latent waiting time and latent service time for each activity. A latent waiting time and latent service time for each activity is estimated based on the estimated parameters using a processor.

Abstract: A neural network conversion method and a recognition apparatus that implements the method are provided. A method of converting an analog neural network (ANN) to a spiking neural network (SNN) normalizes first parameters of a trained ANN based on a reference activation that is set to be proximate to a maximum activation of artificial neurons included in the ANN, and determines second parameters of an SNN based on the normalized first parameters.

Abstract: An anomaly detection module is configured to apply a plurality of machine learning models to received technical status data to detect one or more indicators of an abnormal technical status prevailing in the industrial process system. The plurality of machine learning models are trained on historic raw or pre-processed sensor data and the anomaly detection module configured to generate the anomaly alert based on the one or more indicators. The received technical status data is assigned to signal groups and the generated anomaly alert is a vector with each vector element representing a group anomaly indicator for the respective signal group. Each vector element is determined by applying a respective group specific machine learning model.

Abstract: A method of inferring intent in a hybrid network includes monitoring communications in the hybrid network between a plurality of members, triggering an estimation of an intent of one or more members of the hybrid network, estimating the intent, determining a confidence level of the intent, and triggering an action based on the confidence level.

Abstract: A machine learning model training method includes: classifying samples having risk labels in a training sample set as positive samples and classifying samples without risk labels in the training sample set as negative samples; training a risk model with a machine learning method based on the positive samples and the negative samples; obtaining a risk score for each of the negative samples based on the trained risk model; identifying one or more negative samples in the training sample set that have a risk score greater than a preset threshold value; re-classifying the one or more negative samples in the training sample set that have a risk score greater than the preset threshold value as re-classified positive samples to generate an updated training sample set from the training sample set; and re-training the risk model with the machine learning method based on the updated training sample set.

Abstract: A client device determines a local user gradient value based on a current user preference vector and a local item gradient value based on a current item feature vector. The client device updates a user preference vector by using the local user gradient value and updates an item feature vector by using the local item gradient value. The client device determines a neighboring client device based on a predetermined adjacency relationship. The local item gradient value is sent by the client device to the neighboring client device. The client device receives a neighboring item gradient value sent by the neighboring client device. The client device updates the item feature vector by using the neighboring item gradient value. In response to the client device determining that a predetermined iteration stop condition is satisfied, the client device outputs the user preference vector and the item feature vector.

Abstract: A method for analysis of problems is described, comprising receiving values for a plurality of input parameters specifying a problem, analyzing the values of the plurality of input parameters with a fuzzy expert system thereby calculating a fuzzy result, including a value of a linguistic variable and a crisp value, and determining a priority of the problem, wherein the priority is determined based on the value of the linguistic variable and the crisp value of the fuzzy result. Furthermore, a corresponding problem analysis system is provided.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for executing a signal generation neural network on parallel processing hardware. One of the methods includes receiving weight matrices of a layer of a signal generation neural network. Rows of a first matrix for the layer are interleaved by assigning groups of rows of the first matrix to respective thread blocks of a plurality of thread blocks. A first subset of rows of the one or more other weight matrices are assigned to a first subset of the plurality of thread blocks and a second subset of rows of the one or more other weight matrices are assigned to a second subset of the plurality of thread blocks. The first matrix operation is performed substantially in parallel by the plurality of thread blocks. The other matrix operations are performed substantially in parallel by the plurality of thread blocks.

Abstract: A network analysis tool receives network flow information and uses deep learning—machine learning that models high-level abstractions in the network flow information—to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis.