Abstract: A risk assessment system and a data processing method are provided. The risk assessment system includes an analyze device and an electronic device. The analyze device generates at least one decision table according to a plurality of data and context features of the plurality of data. Each of the decision table has a plurality of entries, and each of the entries includes at least one of determining condition and probability information corresponding to a specific result. The electronic device communicates with the analyze device. The electronic device receives the decision table and compares the determining conditions of each entries with current conditions of an assessee. When the current conditions are the same to the determining conditions of a specific entry, the electronic device displays the determining conditions and the probability information corresponding to the specific entry.

Abstract: Optimized execution order results (e.g., from a Rete algorithm graph) are correlated with a production ordering selected by a user, thereby more accurately modeling the user's understanding of how productions relate to one another during execution in a rule system. An execution report shows in the user-selected order for each of the rules, whether the rule was executed, the inputs matched to partial conditions of the rule, and partial condition evaluation results. User rule management experience is also enhanced in other ways. For example, a graphical user interface permits user selection of a schema xpath, bulk selection of XML schema nodes to define vocabulary used in rules, if-then-else rules, rules with embedded SQL, marking rules as active or not, receiving multiple kinds of input from a single text input box, locally executing rules, importing rules authored elsewhere, iterating over a collection of objects, and validating rules against vocabulary data types.

Abstract: Generating a data model may include receiving a raw data set and generating a first repository based on a first set of features of the raw data set, a second repository having a second set of features based on an aggregation of features of the first repository, and a third repository having a third set of features based on the first and second features sets. The data model may be generated based on a tradeoff between accuracy and provenance of the model.

Abstract: Mechanisms are provided for monitoring quality and correctness of content in communications handled by a vendor. The mechanisms sample a set of communications handled by the vendor to generate a sample set of communications and extract content from the sample set of communications. The mechanisms compare the extracted content with expected content of communications handled by the vendor and analyze the extracted content and the expected content to thereby identify differences between the extracted content and the expected content based on results of the analysis. In addition, the mechanisms determine a level of significance of the differences and generate a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for centralized management of predictive models. One of the methods includes receiving a plurality of model versions from respective teams within the organization. Each received model version is processed to generate a respective predictive model corresponding to the model version, including identifying one or more collections of training data specified by the model version, identifying one or more training engines specified by the model version, training, by the identified one or more training engines, a predictive model using the identified one or more collections of training data, computing one or more performance metrics for the predictive model, and associating the one or more performance metrics for the predictive model with the corresponding model version in the version repository.

Abstract: Techniques are provided for determining predicted results for entities based on relatedness of the entities in a graph of nodes. In an embodiment, the graph of nodes is generated based on the determined relatedness of the entities. A node in the graph of nodes represents an entity, and nodes representing entities with known results are assigned those results as their respective node values. The assigned node values are then propagated between the neighboring nodes throughout the graph of nodes in the amount determined by the relatedness of the nodes. Based on the propagation, node values for entities with unknown results are determined and represent the predicted results for those entities. Additionally, various classifiers may be combined with the propagated node values to increase the accuracy of the predicted results.

Abstract: Provided is an information processing apparatus, including a calculation section which calculates a proficiency level of a user for operations performed by the user for achieving a prescribed objective based on history information related to the operations and attribute information related to physical features of the user, and a generation section which generates advice for achieving the objective based on the proficiency level calculated by the calculation section.

Abstract: In one embodiment, a method includes receiving text query that includes n-grams. A vector representation of each n-gram is determined using a deep-learning model. A nonlinear combination of the vector representations of the n-grams is determined, and an embedding of the text query is determined based on the nonlinear combination. The embedding of the text query corresponds to a point in an embedding space, and the embedding space includes a plurality of points corresponding to a plurality of label embeddings. Each label embedding is based on a vector representation of a respective label determined using the deep-learning model. Label embeddings are identified as being relevant to the text query by applying a search algorithm to the embedding space. Points corresponding to the identified label embeddings are within a threshold distance of the point corresponding to the embedding of the text query in the embedding space.

Abstract: An analytical method and apparatus is provided for analyzing and interpreting signals from unstructured data to identify and reason about underlying concepts. The method and apparatus include functions of generating qualitative and quantitative representations of explicit semantic concepts and implicit related or associated concepts, and defining a Semantic Boundary Index used for real-time processing of unstructured data fields or streams in a manner that characterizes, stores, measures, monitors, enables transactional updates or analyses of implicit and explicit information or evidence to identify explicit and implicit or hidden semantic concept, the semantic boundary index being produced by dynamic partitioning through semiotic-based signal processing. The semiotic-based signal processing occurs through agent-based dynamic sensing, characterizing, storing, monitoring, reasoning about and partitioning of unstructured data into core semantic elements.

Abstract: A computer-implemented method for efficient backup deduplication may include (1) identifying a file to be divided into chunks for deduplication, (2) requesting, from a server, a chunk size to use when dividing the file for deduplication by submitting at least one attribute of the file to the server, the server selecting the chunk size based at least in part on a projected chunk reuse rate when the file is deduplicated according to the chunk size, (3) receiving from the server, in response to requesting the chunk size, the chunk size to use when dividing the file for deduplication, and (4) dividing the file for deduplication into a plurality of chunks according to the chunk size. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A method and apparatus for training a language model, include generating a first training feature vector sequence and a second training feature vector sequence from training data. The method is configured to perform forward estimation of a neural network based on the first training feature vector sequence, and perform backward estimation of the neural network based on the second training feature vector sequence. The method is further configured to train a language model based on a result of the forward estimation and a result of the backward estimation.

Abstract: Embodiments are directed to a two-terminal resistive processing unit (RPU) having a first terminal, a second terminal and an active region. The active region effects a non-linear change in a conduction state of the active region based on at least one first encoded signal applied to the first terminal and at least one second encoded signal applied to the second terminal. The active region is configured to locally perform a data storage operation of a training methodology based at least in part on the non-linear change in the conduction state. The active region is further configured to locally perform a data processing operation of the training methodology based at least in part on the non-linear change in the conduction state.

Abstract: A computer implemented method of automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects using one or more hardware processors for executing a code. The code comprises code instructions for extracting a plurality of features from a plurality of sample media objects, generating a plurality of feature samples for each of the plurality of features by augmenting the plurality of features, training a classification function with the plurality of features samples and outputting the classification function for classifying one or more new media objects.

Abstract: A method, system, and computer program product for performing cognitive-based dynamic tuning of a software-based system include monitoring live operation of the system, and determining whether tuning is needed based on the monitoring. Analyzing information and suggesting a change in one or more parameters is based on the determining, the information including an output of a learning algorithm that learns an effect of changes in one or more of the one or more parameters on performance of the system.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The characteristics of the analytics model are adjusted and/or tuned to control/reduce uncertainty in identifying effective user experience options, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.

Abstract: The concepts relate to task completion and specifically to aiding a user to complete an unfinished task at a subsequent time and/or on another device. One example can identify that a user is working on a task on a computing device associated with the user. In an instance when the user stops using the computing device without completing the task, the example can predict a likelihood that the user will subsequently resume the task on a second computing device associated with the user. In an instance where the likelihood exceeds a threshold, the example can attempt to aid the user in completing the task on the second computing device.

Abstract: Aspects herein describe new methods of determining optimal actions to achieve high-level goals with minimum total future cost. At least one high-level goal is inputted into a user device along with various observational data about the world, and a computational unit determines, though a method comprising backward and forward sweeps, an optimal course of action as well as emotions. In one embodiment a user inputs a high-level goal into a cell phone which senses observational data. The cell phone communicates with a server that provides instructions. The server determines an optimal course of action via the method of backward and forward sweeps, and the cell phone then displays the instructions and emotions to the user.

Abstract: A design verification problem includes a design description and a property to be verified. Feature data is identified from the design verification problem and a result is predicted for the design verification problem based on the feature data. A plurality of verification engines is then orchestrated based on the prediction. Supervised machine learning may be used for the result prediction. Feature data and verification results from a plurality of training test cases are used to train a classifier to create a prediction model. The prediction model uses the feature data of the design verification problem to make a result prediction for the design verification model.

Abstract: Architecture that addresses page information lost as part of a selection process in a search engine framework. An aggregation process collects all page or document information from the same image cluster and uses the aggregated page information to annotate one or more selected image-page pairs within the same image cluster. Once the entire set of descriptive terms is received, the entire set of descriptive terms or only an optimum set of top N descriptive terms of the entire set is for annotation of one or more of the representative images in the cluster.

Abstract: Apparatus and method for generating an optimal set of choices. The apparatus includes: a generating section operable to generate a new set of choices to be shown to a chooser, wherein the new set of choices includes at least two selectable subjects among a plurality of selectable subjects; and an output section operable to output the new set of choices generated by the generating section. There is also provided a method and a computer program product.