Abstract: Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

Abstract: An approach is provided that identifies a current mood state of a requestor from whom a question has been submitted to a question answering (QA) system. The approach determines, based on the identified mood state, an urgency associated with the requestor. Data pertaining to a number of candidate answers is analyzed with the candidate answers being generated by the QA system. The analysis results in an urgency characteristic that pertains to each of the candidate answers. Scoring of the candidate answers is adjusted based on a comparison of the requestor's urgency and the urgency characteristic associated with the candidate answers. Answers are selected from the candidate answers and returned to the requestor with the selected answers based on the adjusted scoring.

Abstract: Technical solutions are described for representing health of a cognitive system. An example computer-implemented method includes displaying an animated set of icons, each icon representing a question input to the cognitive system. Each icon has a respective movement pattern. The computer-implemented method also includes receiving a selection of a first icon from the animated set of icons. The computer-implemented method also includes in response, identifying a category of a first question corresponding to the first icon, determining a subset of the icons corresponding to question from the category, and displaying connectors between the subset of the icons.

Abstract: Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

Abstract: Petroleum Analytics Learning Machine (or PALM) system is a machine learning based, “brutally empirical” analysis system for use in all upstream and midstream oil and gas operations. PALM system optimizes exploration, production and gathering from at least one well of oil and natural gas fields to maximize production while minimizing costs. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual data are classified to correlate with optimal production to capture the dynamics of at least one or more wells of oil and natural gas fields and to provide categorization results from labeled data sets to identify patterns.

Abstract: An approach is provided that identifies a current mood state of a requestor from whom a question has been submitted to a question answering (QA) system. The approach determines, based on the identified mood state, an urgency associated with the requestor. Data pertaining to a number of candidate answers is analyzed with the candidate answers being generated by the QA system. The analysis results in an urgency characteristic that pertains to each of the candidate answers. Scoring of the candidate answers is adjusted based on a comparison of the requestor's urgency and the urgency characteristic associated with the candidate answers. Answers are selected from the candidate answers and returned to the requestor with the selected answers based on the adjusted scoring.

Abstract: Methods and systems for performing electronic transactions involve receiving, using a processor coupled to memory, from a mobile application on a user's mobile device processor, a transaction message consisting at least in part of the user's account information obtained by the mobile application reading user account information encoded on a token of the user that is physically distinct from the mobile device processor and a transaction request for the user. Using the processor, the user's account information is verified and a transaction confirmation message is generated and sent to the mobile application on the user's mobile device processor.

Abstract: An approach is provided that selects three attributes that correspond to objects included in a dataset, where each of the three attributes is assigned to a different coordinate value (x, y, and z coordinates). The approach creates a simulated three dimensional (3D) scene of the objects on a display screen by using the x, y, and z coordinate values corresponding to the attributes of each of the objects. The approach further displays, on a 2D screen, a gyroscopic graphical user interface (GUI) that provides three dimensional (3D) control of the simulated 3D scene. In the approach, a gesture from a user receiving at the gyroscopic GUI. Responsively, the approach adjusts the 3D scene displayed on the 2D screen based on the gesture that was received.

Abstract: Methods, devices, and systems are described for diverting a computer hacker from a physical or other targeted production computer to a decoy software-based host emulator that emulates the physical computer. The decoy has the exact same IP address as the physical computer. In order to avoid packet collisions, a programmable physical switch and a virtual networking switch are employed, both of which can use software-defined networking (SDN). The virtual switch prevents packets from the decoy from flowing out of its virtual network until commanded. Upon a command, the physical switch redirects specific flows to the virtual switch, and the virtual switch opens specific flows from the decoy. The specific flows are those with packets containing the hacker's computer IP address, production computer IP address, and production computer port. The packets are associated with TCP connections or UDP sessions. The decoy host emulator can be a virtual machine (VM) running alongside many other VMs in a single computer.

Abstract: An approach is provided that displays, on a two dimensional (2D) screen, a gyroscopic graphical user interface (GUI). The gyroscopic GUI provides three dimensional (3D) control of a simulated 3D scene displayed on the 2D screen. In the approach, a gesture from a user receiving at the gyroscopic GUI. Responsively, the approach adjusts the 3D scene displayed on the 2D screen based on the gesture that was received.

Abstract: A refrigerator cooling system and method provides cooling to one or more features of a refrigerator by employing a secondary cooling loop that utilizes the excess cooling capacity of an evaporator to selectively provide supplemental cooling to the features when a thermal demand arises.

Abstract: The differential adiabatic compensation calorimeter comprises sample and reference containers, sample and reference temperature sensors connected back-to-back, in series, sample and reference compensating heaters coupled to the sample and reference containers, and a temperature-controlled chamber. In this differential adiabatic mixing and reaction calorimeter, the sample heat-sink heat loss to the sample container is compensated so that the exothermic reaction is conducted in an adiabatic state, resulting in an undistorted adiabatic process gaining the highest adiabatic temperature rise that corresponds to the theoretical value and an experimentally measured time to maximum rate value. The calorimeter is designed for measuring the time-resolved adiabatic temperature rise, the rate of temperature rise, the time to maximum temperature peak and time to maximum rate of an exothermic chemical reaction.

Abstract: An in vitro method of imaging a cancer antigen includes providing a cell sample including a cancer antigen selected from the group consisting of a Tn antigen, a T antigen, a sialylated-Tn antigen, and a sialylated-T antigen, treating the sample with a glycosyltransferase to incorporate a carbohydrate with a click chemistry moiety into the cancer antigen, adding a label to the sample that includes a click chemistry moiety that reacts to the click chemistry moiety of the carbohydrate such that the label attaches to the carbohydrate to form a labeled cancer antigen, and imaging the sample with a camera.

Abstract: A method for assaying endoglycosidase activity includes providing a proteoglycan having a glycosaminoglycan chain with a non-reducing end; treating the proteoglycan with a glycosyltransferase to incorporate a carbohydrate into the non-reducing end of the glycosaminoglycan chain, wherein the carbohydrate includes a click chemistry moiety; adding a label to the proteoglycan, wherein the label includes a click chemistry moiety that reacts to the click chemistry moiety of the carbohydrate such that the label attaches to the carbohydrate to form a labeled proteoglycan; immobilizing the labeled proteoglycan on a multi-well plate, wherein the multi-well plate includes a specific anti-proteoglycan antibody for binding the labeled proteoglycan; treating the labeled proteoglycan with an endoglycosidase specific to the glycosaminoglycan chain; and detecting the labeled proteoglycan.