In accordance with some embodiments of the disclosed subject matter, mechanisms (which can, for example, include systems, methods, and media) for maintaining components of a unit are provided. In some embodiments, a method comprises: receiving an indication that a unit is ready to be turned; generating a customized user interface for assessment of the unit based on a template; receiving a request, from a remote computing device, for the user interface; causing the remote device to present the user interface; receiving, via the remote device, input indicating that a component should be replaced; receiving, from the remote device, an image of the component; receiving, from a second remote computing device associated with a designated user, input indicating that replacement of the first component is approved; and causing a notification to be presented via the remote computing device indicating that replacement of the first component is approved.

Methods, systems, apparatuses, and computer program products for incentivizing UAV use are disclosed. In a particular embodiment, incentivizing UAV use includes UAV pilot recommendation by a computing system. In this embodiment, the computing system receives at least one parameter related to a prospective UAV mission and retrieves UAV flight records of a plurality of UAV pilots. According to this embodiment, the computing system recommends, based on the at least one parameter and the UAV flight records, at least one UAV pilot for the prospective UAV flight.

Disclosed is a platform that manages worker users in a temporary staffing environment via an AI machine learning model. The machine learning model predicts dispatch outcomes of a plurality of pairings of worker users to potential shifts. A dispatch outcome predicts whether a worker will show up for and work a given shift. The machine learning model is based on a set of training data surrounding historical dispatch outcomes. The data surrounding the historical dispatch outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both. An implementation of the machine learning model stitches together multiple shifts for up to a schedule horizon based on predicted dispatch outcomes.

An information processing apparatus disclosed is run by a business entity that provides the service of buying and selling used vehicles or a business entity that provides the service of leasing vehicles. When the business entity buys a first vehicle having a first coating film including an easily removable layer applied thereon from a first user or receives such a first vehicle returned by a first user, if the first coating film was removed at a specific site, a controller of the information processing apparatus executes a processing for giving an incentive to the first user, The value of the incentive to be given to the first user is calculated on the basis of the length of time elapsed since the time at which the first coating film was removed at the specific site.

A system includes: a memory operable to store a predictive model; a first processor communicatively coupled to the memory, the first processor operable to execute the predictive model to perform operations including generating knowledge score metrics based on a set of attributes for individuals included in a specified population, where the knowledge score metrics quantify a prediction of a capability of an individual for performing metadata labeling; a second processor communicatively coupled to the memory and the first processor, the second processor is operable to perform operations including comparing the knowledge score metrics to a specified threshold, and identifying attributes of individuals from a specified population having knowledge score metrics exceeding the specified threshold as attributes of individuals capable of performing metadata labeling.

A multicore apparatus determines fitness of a candidate for a role. The apparatus includes a multicore system processing device, a plurality of parallel multicore graphics processing devices, a network interface device, a storage device, and a system interface bus. The network interface device provides remote connection to the multicore system processing device. The storage device stores training data including positive and negative examples. The positive examples represent candidates who would be invited to an interview, and the negative examples represent candidates who would not be invited to an interview. The positive and negative examples are used by the plurality of parallel multicore graphics processing devices to train a deep learning model, which is used by the multicore system processing device to determine fitness of the candidate for the role while eliminating unwanted bias.

An illustrative method includes a computing system receiving a request from an entity to fill a shift associated with a task. The method further includes accessing values for a set of shift criteria associated with the entity and the shift and accessing values for a set of operative criteria associated with an operative. The method further includes determining, based on the values for the set of shift criteria and the values for the set of operative criteria, a value for a compatibility rating of the operative with the shift. The method further includes providing, by the computing system, an output based on the value for the compatibility rating of the operative with the shift.

A device may receive a request for a schedule and scheduling constraints to utilize when generating the schedule, and may process, based on the request, a first portion of the scheduling constraints and first optimization variables, with a first optimization solver model, to generate capacity data for the schedule. The device may process the capacity data, a second portion of the scheduling constraints, and second optimization variables, with a second optimization solver model, to generate shift assignment data for the schedule, and may process the shift assignment data, a third portion of the scheduling constraints, and third optimization variables, with a third optimization solver model, to generate skill and task assignment data for the schedule. The device may generate the schedule based on the capacity data, the shift assignment data, and the skill and task assignment data, and may perform one or more actions based on the schedule.

A computerized-method for prioritizing agents for working-from-office, in a hybrid contact-center work-environment, is provided herein. The computerized-method includes operating an Agent-Work-From-Office (AWFO) Prioritization Analytics module. The AWFO Prioritization-Analytics-module includes: (i) for each agent in the data store of agents' metrics calculating an Agent Health (AH) score; (ii) when the AH score is ‘1’ then calculating: (a) Agent Home Productivity (AHP) score; (b) Agent Skills Prioritization (ASP) score; and (c) agent's preferences to work from office indicator; (iii) determining an AWFO-score based on the AHP score, the ASP score and the agent's preferences to work from office indicator: (iv) adding the determined AWFO-score to a list-of-agents-AWFO-score and after the calculating of the AH score for all agents, sending the list-of-agents-AWFO-score to a WFM-application to allocate by the WFM-application agents to work-from-office according to a preconfigured office capacity and a scheduled-shift requirements by selecting agents which are having a lowest AWFO-score.

An information processing device 1C mainly includes a segmentation unit 32C and a display control unit 33C. The segmentation unit 32C is configured to divide a similar job group, to which multiple workers engaged in a common or similar job belong, into multiple segments based on one or more attributes of the workers. The display control unit 33C is configured to display segment information, which is information regarding workers per segment, on the display device 4C.