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: 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: An example non-transitory computer-readable storage medium includes instructions that, when executed by a processing resource, cause the processing resource to collect user data for a plurality of users and computing device data for a plurality of computing devices. The instructions further cause the processing resource to create a user profile for each of the plurality of users based on the user data. The instructions further cause the processing resource to create a computing device profile for each of the plurality of computing devices based on the computing device data. The instructions further cause the processing resource to identify a quality threshold for a user of the plurality of users. The instructions further cause the processing resource to match, to satisfy the quality threshold, the user of the plurality of users to a computing device of the plurality of computing devices based on the user profile and the computing device profile.

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.

Abstract: A method, for preventing the intelligible voice recording is provided. The voice of a subject or interlocutor is recorded for a given time interval thereby providing a voice recording. The voice recording is cut into shorter time interval segments thereby providing a set of voice recording segments. The set of voice recording segments is mixed in a randomly rearranged order. The mixed set of voice recording segments is spliced into a single randomly mixed voice recording. Emitting the randomly mixed voice recording during speaking of the subject or interlocutor prevents the intelligible recording of the voice of the subject or interlocutor.

Abstract: A method, for preventing the intelligible voice recording is provided. The voice of a subject or interlocutor is recorded for a given time interval thereby providing a voice recording. The voice recording is cut into shorter time interval segments thereby providing a set of voice recording segments. The set of voice recording segments is mixed in a randomly rearranged order. The mixed set of voice recording segments is spliced into a single randomly mixed voice recording. Emitting the randomly mixed voice recording during speaking of the subject or interlocutor prevents the intelligible recording of the voice of the subject or interlocutor.

Abstract: A method, device, system and kit for preventing the intelligible voice recording Is provided. The voice of a subject or Interlocutor Is recorded for a given time Interval thereby providing a voice recording. The voice recording Is cut Into shorter time interval segments thereby providing a set of voice recording segments. The set of voice recording segments is mixed in a randomly rearranged order, The mixed set of voice recording segments is spliced into a single randomly mixed voice recording. Emitting the randomly mixed voice recording during speaking of the subject or interlocutor prevents the Intelligible recording of the voice of the subject or interlocutor.

Abstract: A constructive arrangement of a flexible wheel for wheelbarrow or manually tractioned industrial carts is constituted by a single block that comprehends three parts: the outer ring (1), the center wall (2), and the inner ring (3); and the outer ring (1) has on its extremity a tread surface (15), and as tangents on each side it has side tread surfaces (12); and each side tread surface (12) has a plurality of radial cavities (11) spaced along the wheel; and the radial cavities (11) of a side tread surface (12) are not aligned in relation to the radial cavities (11a) of the opposite side tread surface (12a); and the center wall (2) is a flat, solid, ring-shaped wall contained in the central plane which contains the body of the wheel, and on each side of the flat wall there are a plurality of flexible structures (14) radially aligned to the radial cavities (11) contained in the outer ring (1).

Abstract: A method, device, system and kit for preventing the intelligible voice recording Is provided. The voice of a subject or Interlocutor Is recorded for a given time Interval thereby providing a voice recording. The voice recording Is cut Into shorter time interval segments thereby providing a set of voice recording segments. The set of voice recording segments is mixed in a randomly rearranged order, The mixed set of voice recording segments is spliced into a single randomly mixed voice recording. Emitting the randomly mixed voice recording during speaking of the subject or interlocutor prevents the Intelligible recording of the voice of the subject or interlocutor.

Abstract: CONSTRUCTIVE ARRANGEMENT OF A FLEXIBLE WHEEL FOR WHEELBARROW OR MANUALLY TRACTIONED INDUSTRIAL CARTS is constituted by a single block that comprehends three parts: the outer ring(1), the center wall(2), and the inner ring(3); and the outer ring(1) has on its extremity a tread surface(15), and as tangents on each side it has side tread surfaces(12); and each side tread surface(12) has a plurality of radial cavities(11) spaced along the wheel; and the radial cavities(11) of a side tread surface(12) are not aligned in relation to the radial cavities(11a) of the opposite side tread surface(12a); and the center wall(2) is a flat, solid, ring-shaped wall contained in the central plane which contains the body of the wheel, and on each side of the flat wall there are a plurality of flexible structures(14) radially aligned to the radial cavities(11) contained in the outer ring(1); and the inner ring(3) is constituted of a circular wall(31), a reinforcement rim(32), and mass reducers(33); and the mass reducers(33) are