Abstract: Examples are described herein for determining aggregate psychological states of multiple individuals. In various examples, sensor data indicative of affects of multiple individuals may be analyzed. Based on the analyzing, the multiple individuals may be partitioned into multiple distinct clusters of individuals. An aggregate psychological state of the individuals of a given cluster of the multiple distinct clusters of individuals may be determined. Data indicative of the aggregate psychological state may be transmitted to cause a computing device to render output that conveys the aggregate psychological state of the individuals of the cluster.

Abstract: Methods, systems, apparatus, and computer-readable media (transitory or non-transitory) are described herein for inferring psychological states. In various examples, data indicative of a measured affect of an individual may be processed using a regression model to determine a coordinate in a continuous space. The continuous space may be indexed based on a plurality of discrete psychological labels. In a first context, the coordinate in the continuous space may be mapped to one of a first set of the discrete psychological labels associated with the first context. In a second context, the coordinate in the continuous space may be mapped to one of a second set of the discrete psychological labels associated with the second context.

Abstract: An example computing device includes a user input device, a network interface, and a processor connected to the user input device and the network interface. The processor is to execute a process, monitor user input at the user input device to determine user interaction with the process, and, in response to determining a lack of user interaction with the process, provide the process to a remote computing device via the network interface to continue execution of the process by the remote computing device.

Abstract: A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.

Abstract: A system may comprise a processor to execute an application, a keystroke analyzer module to determine a frequency with which each key on a keyboard is utilized during execution of the application, and a key illumination module to illuminate the keys on the keyboard with an illumination pattern that visually distinguishes a first subset of keys on the keyboard from a second subset of keys on the keyboard.

Abstract: An application-classification and peripheral device settings (APDS) system may comprise a categorization subsystem to capture descriptive information about an application, analyze the descriptive information, determine a classification of the application, and assign the application to an application category that includes applications that each have a similar classification. The system may also include a peripheral device configuration subsystem to identify a peripheral device setting from the application category and apply the peripheral device setting to an operating configuration of the application.

Abstract: A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.

Abstract: A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.