Abstract: A method for labeling a data set by a coding model includes generating multiple sets of related initial labels based on processing a data set with a group of initial labels. The method also includes determining a quantity of occurrences, within the data set, of each one of the group of initial labels and each related initial label of the multiple sets of related initial labels. The method further includes determining, for each initial label of the group of initial labels, a breadth score based on the number of occurrences of each related initial label. The method still further includes updating one or more of the group of initial labels based on respective breadth scores satisfying a label updating condition. The method also includes labeling the data set based on the group of initial labels and the multiple sets of related initial labels.

Abstract: An apparatus for determining an erroneous cause for a belief and presenting interventions. The apparatus includes one or more memories comprising processor-executable instructions; and one or more processors configured to execute the processor-executable instructions and cause the apparatus to receive a communication through at least one of a direct method or indirect method, extract a target belief, identify an attributable cause based on the communication, compile known causes of the target belief and determine if the known cause is false, compare the attributable cause to the known causes and generate a score based on an amount of similarities, determine that the attributable cause corresponds to a known cause that is false, when the score is greater than a predetermined threshold, and generate interventions for presentation on the user interface device based on the determination that the attributable cause corresponds to the known cause that is false.

Abstract: A method for monitoring user purchase making activity is described. The method includes logging a potential user purchase and purchase communications corresponding to a potential option available for purchase by a user. The method also includes predicting whether loss aversion is a factor in a purchase making process of the potential option available for purchase by the user. The method further includes determining a purchase recommendation in response to identifying that the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user. The method also includes displaying the purchase recommendation based on a use frequency of the option purchased by the user.

Abstract: A method for a credible information guide is described. The method includes identifying a source of content accessed by a user as a non-credible information source if a credibility score is less than a credibility threshold. The method also includes determining a similarity of a message between content of the non-credible information source and a credible content from one or more credible information sources. The method further includes recommending a selected information source to the user, having a credibility score between the non-credible information source and the one or more credible information sources when a lack of message similarity is determined. The method also includes continuing the recommending of selected information sources having gradually increasing credibility scores until a credible source accessed by the user.

Abstract: A method for inferring intent and discrepancies in a label coding scheme is described. The method includes compiling data indicating how one or more individuals labeled unstructured content according to the label coding scheme comprising a plurality of labels. The method also includes analyzing a context associated with a content labeled in a particular manner by the one or more individuals. The method further includes detecting discrepancies of meaning for a particular label used by the one or more individuals. The method also includes inferring a strategic thinking of the one or more individuals associated with the discrepancies of meaning detected for the particular label. The method further includes displaying recorded metadata associated with the strategic thinking and the discrepancies of meaning detected for the particular label between the one or more individuals regarding a coded dataset.

Abstract: A method for inferring intent and discrepancies in a label coding scheme is described. The method includes compiling data indicating how one or more individuals labeled unstructured content according to the label coding scheme comprising a plurality of labels. The method also includes analyzing a context associated with a content labeled in a particular manner by the one or more individuals. The method further includes detecting discrepancies of meaning for a particular label used by the one or more individuals. The method also includes inferring a strategic thinking of the one or more individuals associated with the discrepancies of meaning detected for the particular label. The method further includes displaying recorded metadata associated with the strategic thinking and the discrepancies of meaning detected for the particular label between the one or more individuals regarding a coded dataset.

Abstract: A method for inferring intent and discrepancies in a label coding scheme is described. The method includes compiling data indicating how one or more individuals labeled unstructured content according to the label coding scheme comprising a plurality of labels. The method also includes analyzing a context associated with a content labeled in a particular manner by the one or more individuals. The method further includes detecting discrepancies of meaning for a particular label used by the one or more individuals. The method also includes inferring a strategic thinking of the one or more individuals associated with the discrepancies of meaning detected for the particular label. The method further includes displaying recorded metadata associated with the strategic thinking and the discrepancies of meaning detected for the particular label between the one or more individuals regarding a coded dataset.

Abstract: A method for labeling a data set by a coding model includes generating multiple sets of related initial labels based on processing a data set with a group of initial labels. The method also includes determining a quantity of occurrences, within the data set, of each one of the group of initial labels and each related initial label of the multiple sets of related initial labels. The method further includes determining, for each initial label of the group of initial labels, a breadth score based on the number of occurrences of each related initial label. The method still further includes updating one or more of the group of initial labels based on respective breadth scores satisfying a label updating condition. The method also includes labeling the data set based on the group of initial labels and the multiple sets of related initial labels.

Abstract: Systems and methods for generating predicted preferences are disclosed. The method includes implementing, with a computing device having a processor and a non-transitory computer-readable memory, a conjoint architecture comprising: an autoencoder trained to transform input data including one or more choices and one or more features into a latent representation, and a choice classification network trained to predict one or more predicted preferences from the latent representation extracted by the autoencoder. The method further includes outputting, from the choice classification network, the one or more predicted preferences.

Abstract: A mechanism is described for proximity detection based on Wi-Fi signals. A method of embodiments of the invention includes determining strength of one or more signals being communicated between one or more antennae of a first device and two or more antennae of a second device. The one or more signals are associated with a broadcast packet. The method further includes determining proximity of the first device and the second device based on the determined strength of the one or more signals.

Abstract: A mechanism is described for proximity detection based on Wi-Fi signals. A method of embodiments of the invention includes determining strength of one or more signals being communicated between one or more antennae of a first device and two or more antennae of a second device. The one or more signals are associated with a broadcast packet. The method further includes determining proximity of the first device and the second device based on the determined strength of the one or more signals.

Abstract: A mechanism is described for proximity detection based on Wi-Fi signals. A method of embodiments of the invention includes determining strength of one or more signals being communicated between one or more antennae of a first device and two or more antennae of a second device. The one or more signals are associated with a broadcast packet. The method further includes determining proximity of the first device and the second device based on the determined strength of the one or more signals.

Abstract: A mechanism is described for proximity detection based on Wi-Fi signals. A method of embodiments of the invention includes determining strength of one or more signals being communicated between one or more antennae of a first device and two or more antennae of a second device. The one or more signals are associated with a broadcast packet. The method further includes determining proximity of the first device and the second device based on the determined strength of the one or more signals.