Abstract: A computer-implemented method for simultaneous feature selection and hyperparameter optimization of non-linear models of machine learning is provided including setting a first solution having first hyperparameters and a first set of features of a plurality of features of a training data set, initializing a weight table providing a score for each feature of the first set of features, and initializing a discrepancy. The method further includes performing a limited discrepancy search (LDS), according to an order based on the weight table, to obtain a second solution having second hyperparameters and a second set of features by swapping the first set of features and switching the first hyperparameters from the first solution with the discrepancy, while updating the weight table during LDS, comparing the second solution with the first solution, and obtaining a new solution with improved features and hyperparameters, as an optimized solution.

Abstract: A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

Abstract: Embodiments for providing data content consumption support by a processor. Data from one or more data sources may be captured and received by one or more data capturing devices while a user is consuming the data on the one or more data sources. A domain knowledge may be automatically updated with the data. A response may be provided to one or more queries based upon information accessed from the knowledge domain.

Abstract: In an approach for building an automated customer support system, a processor receives a set of sentences extracted from a natural language conversation occurring between an IT support system and a user. A processor extracts an initial state and a goal state from the set of sentences using a Natural Language Classifier. A processor extracts one or more actions from the set of sentences. A processor creates a formal planning model. A processor determines the one or more formal actions are not complete using a first machine learning model. A processor completes the one or more formal actions with one or more missing parts. A processor produces an executable plan using a planner. A processor implements one or more executable scripts according to a sequence of the one or more formal actions of the executable plan using a plan executor.

Abstract: A computer-implemented method for automated policy decision making optimization is disclosed. The computer-implemented method includes creating a dataset from a tabular database, wherein the dataset includes one or more columns selected as state variables, a column selected as action variables, and a column selected as reward variables. The computer-implemented method further includes determining a candidate function approximator Q based on applying at least one state variable, one action variable, and one reward variable to a trained regression model. The computer-implemented method further includes learning a decision policy based on applying the candidate function approximator Q to a reinforcement learning algorithm. The computer-implemented method further includes determining, based on the learned decision policy, an expected reward.

Abstract: A lighting system includes a plurality of lighting fixtures and a terminal device—that controls the plurality of lighting fixtures, wherein: each of the plurality of lighting fixtures transmits, to the terminal device, identification information and acquired information; and the terminal device classifies the plurality of lighting fixtures—on the basis of the information acquired respectively by the plurality of lighting fixtures.

Abstract: Various embodiments are provided for learning policy explanations in a computing environment by a processor. One or more explanations may be provided that justify validity or invalidity of a claim based on one or more rules extracted from one or more segments of text data of a policy data source using a machine learning operation.

Abstract: A swiveling working machine includes a swivel base; a working device provided on a front side of the swivel base; a battery unit; an electric motor that is driven by electric power output by the battery unit; and a hydraulic pump that delivers a hydraulic fluid by driving of the electric motor. The battery unit is disposed at a rear portion of the swivel base. The electric motor and the hydraulic pump are disposed sideward of the battery unit side by side in a front-rear direction.

Abstract: A neural network has an input layer, one or more hidden layers, and an output layer. The input layer is divided into a situation context input sublayer, a background context input sublayer (in some embodiments), and an environmental input sublayer. The output layer has a selection/sequencing output sublayer and an environmental output sublayer. Each of the layers (including the sublayers) have a plurality of neurons and each of the neurons has an activation. Situation context, environmental information, and background context can be inputted into the neural network which create an output used to dynamically select and sequence selected storylines that are used to modify a story based on the sentiment, environment, and/or background of the audience.

Abstract: Various embodiments are provided for integrating multiple domain learning and personalization in a dialog system for a user in a computing environment by a processor. One or more problem instances may be defined for multiple domains according to a problem instance template, identified user intent, links to one or more problem solvers associated with the multiple domains, or a combination thereof. A dialog plan may be determined to further define the one or more problem instances in response to user input. A solution may be provided to the user for the one or more problem instances.

Abstract: Various embodiments are provided for using a dialog system for integrating multiple domain learning and problem solving for a user in a computing environment by a processor. One or more problem instances may be defined for one or more selected domains in a multi-domain database according to a problem instance template, identified user intent, links to one or more problem solvers associated with the one or more selected domains, or a combination thereof. A dialog plan may be determined for the one or more problem instances using a dialog system associated with the multi-domain database, wherein each record in the multi-domain database corresponds to a selected database for the one or more selected domains. A solution may be provided to the user for the one or more problem instances. One or more preferences of a user may be learned according to the solution.

Abstract: A system receives messaging, video and/or audio input streams including dialogue spoken by users at a group meeting. From these inputs, the system obtains single or multiple interaction records including natural language text memorializing content spoken by each speaker at a meeting, analyzes the content, and identifies single or multiple action item tasks in the interaction records. The system then generates summaries indicating the action item tasks for the users. From the dialogue content, the system further detects whether each action item is addressed, and whether the action item for a user has a solution, or not. The system further detects whether one action item is a precondition for resolving another action item by the user or in conjunction with another user. Using a pre-configured template, the system generates action item summaries, any associated solution, and any relationship or precondition between action items and presents the summary to a user.

Abstract: Techniques regarding generating and/or training one or more symbolic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train a symbolic model via active machine learning. The symbolic model can characterize a formal planning language for a planning domain as a plurality of digital image sequences.

Abstract: In an approach for generating and recommending optimized shopping orders for a group of users that collectively purchase bundles of goods, a processor generates an initial shopping order for each user in a group of shopping users, based on one or more preferences and constraints of each user on one or more items to buy from a stock. A processor optimizes the initial shopping order for each user based on one or more objectives of each user. A processor outputs the optimized shopping order for each user.

Abstract: A titanium foil or a titanium sheet is produced by electrodeposition from molten salt using constant current pulse, the method comprising: forming an electrodeposited titanium film on a surface of a cathode electrode made of glassy carbon, graphite, Mo, and Ni, and separating thereafter the electrodeposited titanium film from the cathode electrode by performing one or both of applying an external force to the electrodeposited titanium film and removing the cathode electrode. This enables the electrodeposited titanium film electrodeposited on the cathode electrode to be peeled from the cathode electrode simply and at low cost.

Abstract: In an approach for recommending a safe path for people to navigate an environment, a processor generates a contact graph for a user in a group of users in an area, based on a pre-defined distance measurement between the user and another user in a same timestamp during a pre-defined time period. A processor builds a profile for the user based on the contact graph, the profile including a probability model corresponding to the contact graph to estimate an infection probability for the user. A processor calculates an initial route for the user from a first location to a second location in the area. A processor analyzes an infection risk for the user based on the profile and other users within a pre-defined distance to the user in the initial route. A processor updates the initial route based on the analysis to minimize the risk to be infected.

Abstract: In an approach to predictive contact tracing, a computer receives a query associated with contact tracing of a person with an infection. A computer retrieves timestamped location data associated with the person over a period of time. Based on the retrieved data, a computer creates a contact graph associated with the person, where the contact graph depicts one or more other people that were in contact with the person over the period of time. A computer retrieves medical data associated with the person and the one or more other people that were in contact with the person over the period of time. Based on the retrieved data, a computer builds a probabilistic model. A computer runs the probabilistic model to provide a prediction of a probability of infection of the one or more other people over the period of time as a result of being in contact with the person.

Abstract: A method for generating a summary text composition can include obtaining historical reading data of a user. The method can include generating, based on the historical reading data, a reading proficiency level of the user. The method can include selecting, based on the reading proficiency level, a summarization model from a set of summarization models. The method can include obtaining a target composition. The target composition can be selected from the group consisting of a literary work, a video recording, and an audio recording. The method can include generating, by the summarization model, the summary text composition. The summary text composition can correspond to the target composition and have a first reading level classification that matches the reading proficiency level. The method can include transmitting the summary text composition to a computing device.

Abstract: Aspects of the present invention disclose a method for generating speech recommendations for a user based on feedback data corresponding to a plurality of viewers of the user. The method includes one or more processors identifying speech of a user in audio data of the user. The method further includes identifying feedback of one or more audience members of the user associated with the speech of the user. The method further includes generating an assessment of the speech of the user, wherein the assessment is based at least in part on the feedback of the one or more audience members. The method further includes generating a speech recommendation for the speech of the user based at least in part on the assessment of the speech.

Abstract: In an approach for discourse-level text optimization, a processor receives an initial text in a first language. A processor applies one or more operators to modify the initial text. A processor evaluates the modified text using a scoring function. A processor determines whether a score generated from the scoring function on the modified text is above a predefined threshold. In response to determining the score is above the predefined threshold, a processor outputs the modified text.