Abstract: Taste and smell classification from multilanguage descriptions can be performed by extracting, by one or more processors using natural language processing, a text including one or more words associated with taste and smell perceptions from an input received from a plurality of users. The input includes multilanguage information regarding at least one of changes in smell and changes in taste perceived by each of the plurality of users. Feature vectors are generated for the text extracted from the input using global vectors, and a distance between the feature vectors and a plurality of reference descriptors associated with taste and smell is calculated for determining a similarity between the text and the reference descriptors and creating a training dataset based on which a classification model is generated for categorizing the plurality of users according to the at least one of changes in smell and changes in taste.

Abstract: Embodiments of the invention include computer-implemented methods, computer systems, and computer program products for predicting sensory perception. A non-limiting example of the computer-implemented method includes receiving at a processor a library including a plurality of indexed sensory descriptors. A sensory target descriptor is also received at the processor. The processor is configured to calculate a coefficient matrix based in part on the semantic distance between an indexed sensory descriptor and a sensory target descriptor. The processor is further configured to generate a perceptual descriptor prediction for the sensory target.

Abstract: Exemplary embodiments disclose a method, a computer program product, and a computer system for generating dialogue via hashing functions. Exemplary embodiments may include detecting dialogue between one or more participants, converting the dialogue to a hashcode, and determining one or more responses to the dialogue by applying one or more models to the hashcode, wherein the one or more models correlates one or more hashcodes with the one or more responses.

Abstract: Taste and smell classification from multilanguage descriptions can be performed by extracting, by one or more processors using natural language processing, a text including one or more words associated with taste and smell perceptions from an input received from a plurality of users. The input includes multilanguage information regarding at least one of changes in smell and changes in taste perceived by each of the plurality of users. Feature vectors are generated for the text extracted from the input using global vectors, and a distance between the feature vectors and a plurality of reference descriptors associated with taste and smell is calculated for determining a similarity between the text and the reference descriptors and creating a training dataset based on which a classification model is generated for categorizing the plurality of users according to the at least one of changes in smell and changes in taste.

Abstract: Embodiments of the invention include methods, systems, and computer program products for predicting olfactory perception. A non-limiting example of the method includes receiving a library including a plurality of indexed olfactory descriptors. The method also includes receiving an olfactory target descriptor. The method also includes calculating a coefficient matrix and a perceptual distance between an indexed olfactory descriptor and an olfactory target descriptor. The method also includes generating a perceptual descriptor prediction for the olfactory target.

Abstract: A system for compressing data during neural network training, comprising of memory that stores computer executable components; a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise of a compilation component that compiles respective molecular descriptors regarding a first set of molecules; a perception component that learns human perception information related to olfactory perceptions of the first set of molecules, and generates predictions of human olfactory perceptions of a second set of molecules; a fitting component that fits distance predictions from the perception component regarding the second set of molecules against measured correct classifications regarding the second set of molecules; and a vector component that generates a perceptual vector distance between two olfactory targets.

Abstract: Exemplary embodiments disclose a method, a computer program product, and a computer system for generating dialogue via hashing functions. Exemplary embodiments may include detecting dialogue between one or more participants, converting the dialogue to a hashcode, and determining one or more responses to the dialogue by applying one or more models to the hashcode, wherein the one or more models correlates one or more hashcodes with the one or more responses.

Abstract: Embodiments of the invention include computer-implemented methods, computer systems, and computer program products for predicting sensory perception. A non-limiting example of the computer-implemented method includes receiving at a processor a library including a plurality of indexed sensory descriptors. A sensory target descriptor is also received at the processor. The processor is configured to calculate a coefficient matrix based in part on the semantic distance between an indexed sensory descriptor and a sensory target descriptor. The processor is further configured to generate a perceptual descriptor prediction for the sensory target.

Abstract: Predicting human olfactory perception based on molecular structure is described. Molecular descriptor data indicative of molecular descriptors associated with a group of molecular samples can be obtained. Olfactory perception indicator (OPI) data for a set of OPIs can also be obtained with respect to the molecular samples. A training model can be executed on the molecular descriptor data and the OPI data to yield an output model that correlates molecular attributes with OPIs for a single individual or across an aggregate of individuals. The output model can be used to predict olfactory perception for a particular compound or mixture based on which OPIs are correlated with molecular descriptors of the compound or mixture in the output model. The output model can also be inverted and used to identify molecular descriptors that are correlated with a desired set of OPIs. A molecular construct having the molecular descriptors can then be generated.

Abstract: Embodiments are directed to a computer implemented business campaign development system. The system includes an electronic tool configured to hold data of a user, and an analyzer circuit configured to derive a cognitive trait of the user based at least in part on the data of the user. The system further includes a targeted business strategy development system configured to derive a targeted business strategy based at least in part on the cognitive trait of the user.

Abstract: Embodiments are directed to a computer implemented business campaign development system. The system includes an electronic tool configured to hold data of a user, and an analyzer circuit configured to derive a cognitive trait of the user based at least in part on the data of the user. The system further includes a targeted business strategy development system configured to derive a targeted business strategy based at least in part on the cognitive trait of the user.

Abstract: A system for compressing data during neural network training, comprising of memory that stores computer executable components; a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise of a compilation component that compiles respective molecular descriptors regarding a first set of molecules; a perception component that learns human perception information related to olfactory perceptions of the first set of molecules, and generates predictions of human olfactory perceptions of a second set of molecules; a fitting component that fits distance predictions from the perception component regarding the second set of molecules against measured correct classifications regarding the second set of molecules; and a vector component that generates a perceptual vector distance between two olfactory targets.

Abstract: A method, system, and computer program product for selecting forecasting model complexity using eigenvalues are provided in the illustrative embodiments A process is represented in a model. The model comprises a mathematical representation of the process in a certain degree. A first portion of historical data generated by the process during a first period is selected and includes an actual value of an outcome of the process and a value of a feature influencing the process during the first period. A prediction is made of a predicted value of the outcome. A difference between the prediction and the actual value of the outcome is determined. The difference is represented as a change in a distribution of eigenvalues. According to the change, a second model is to represent the process. The second model comprises a second mathematical representation of the process in a different degree.

Abstract: Embodiments relate to facilitating a meeting. A method for reducing an amount of communications to analyze in order to determine a cognitive state of an entity is provided. The method determines a first likelihood of an entity to have a particular cognitive state based on a set of physiological measures of the entity. The method receives communications from the entity. The method generates a graph of communications of the entity. The method performs a graphical text analysis on the graph to determine a second likelihood of the entity to have the particular cognitive state. The method determines whether the entity has the particular cognitive state based on the first likelihood and the second likelihood.

Abstract: Embodiments relate to facilitating a meeting. A method for reducing an amount of communications to analyze in order to determine a cognitive state of an entity is provided. The method determines a first likelihood of an entity to have a particular cognitive state based on a set of physiological measures of the entity. The method receives communications from the entity. The method generates a graph of communications of the entity. The method performs a graphical text analysis on the graph to determine a second likelihood of the entity to have the particular cognitive state. The method determines whether the entity has the particular cognitive state based on the first likelihood and the second likelihood.

Abstract: Embodiments relate to facilitating a meeting. A method for facilitating a meeting of a group of participants is provided. The method generates a graph of words from speeches of the participants as the words are received from the participants. The method partitions the group of participants into a plurality of subgroups of participants. The method performs a graphical text analysis on the graph to identify a cognitive state for each participant and a cognitive state for each subgroup of participants. The method informs at least one of the participants about the identified cognitive state of a participant or a subgroup of participants.

Abstract: Embodiments of the invention include methods, systems, and computer program products for predicting olfactory perception. A non-limiting example of the method includes receiving a library including a plurality of indexed olfactory descriptors. The method also includes receiving an olfactory target descriptor. The method also includes calculating a coefficient matrix and a perceptual distance between an indexed olfactory descriptor and an olfactory target descriptor. The method also includes generating a perceptual descriptor prediction for the olfactory target.

Abstract: Embodiments are directed to a computer implemented neural stimulation system having a first module configured to derive neural data from muscle contractions or movements of a subject. The system further includes a second module configured to derive a neural state assessment of the subject based at least in part on the neural data. The system further includes a third module configured to derive at least one neural stimulation parameter based at least in part on the neural state assessment. The system further includes a fourth module configured to deliver neural stimulations to the subject based at least in part on the at least one neural stimulation parameter.

Abstract: Embodiments are directed to a computer implemented neural stimulation system having a first module configured to derive neural data from muscle contractions or movements of a subject. The system further includes a second module configured to derive a neural state assessment of the subject based at least in part on the neural data. The system further includes a third module configured to derive at least one neural stimulation parameter based at least in part on the neural state assessment. The system further includes a fourth module configured to deliver neural stimulations to the subject based at least in part on the at least one neural stimulation parameter.

Abstract: Embodiments are directed to a computer system for securing an electronic device. The system includes at least one processor configured to receive at least one communication from an entity seeking to access the device. The at least one processor is further configured to generate a graph of the at least one communication from the entity seeking access to the device. The at least one processor is further configured to determine a difference between a cognitive trait of the entity seeking access to the device, and a cognitive identity of an entity authorized to access the device. The at least one processor is further configured to, based at least in part on a determination that the difference is greater than a threshold, deploy a security measure of the device.