Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for expanding user segments automatically utilizing user embedding representations generated by a trained neural network. For example, a user embeddings system expands a segment of users by identifying holistically similar users from uniform user embeddings that encode behavior and/or realized traits of the users. Further, the user embeddings system facilitates the expansion of user segments in a particular direction and focus to improve the accuracy of user segments.

Abstract: Approaches are provided for normalizing performance data for the operation of industrial vehicles. The approaches comprise categorizing operations of an industrial vehicle into distinct, measureable activities. The approaches further comprise measuring the actual usage of the industrial vehicle as a function of each category of operation and wirelessly transmitting that measured usage to a remote server. The approaches also comprise associating for each category, a weighted impact on the work performed by each category as a function of energy consumed in performing that work.

Abstract: Systems and methods for evaluating a customer premise equipment (CPE) device to determine whether it is being used to perpetuate theft of service (TOS). A computing system may be configured to collect raw data from a customer premise equipment (CPE) device associated with a subscriber account, determine TOS features based on the collected raw data, determine a risk score for the CPE based on the TOS features, determine whether the risk score exceeds a threshold value, and classifying the CPE device as a TOS device in response to determining that the risk score exceeds the threshold value.

Abstract: A system, method and apparatus is described for a dynamic mobile application that provides a real time collection, analysis and reporting information management platform for building and construction projects, emergency services and disaster management tasks. The mobile device application includes multiple modules interconnected including a finance module, an operations module, a utilities module and a safety module.

Abstract: A computerized-method for automatically generating a two-part readable Suspicious Activity Report (SAR) from high-dimensional data in tabular form is provided herein. The computerized-method may include receiving high-dimensional data in tabular form of evidence financial transactions to be reported under Anti Money Laundering (AML) regulations. Then, displaying the received data to a Subject Matter Expert (SME) for ordering each displayed transaction in a predefined construction; Then, training one or more Natural Language Generation (NLG) translation models, for each transaction type, according to a deep learning model. Then, operating the one or more NLG translation models on each transaction type to generate for each transaction type a narrative of SAR; Then, operating a prebuilt summary model on the generated narrative of SAR of each transaction type to generate a summary of the plurality of narratives of SAR; and combining the plurality of narratives of SAR and the summary to one SAR.

Abstract: A device, system and method for electronically implementing corrective actions based on keep-separate attributes is provided. A keep-separate attribute associated with respective inmate profiles is generated using electronic data indicative of monitoring inmates. Schedule data, associated with the inmate profiles, which violates the keep-separate attribute within a given time period, is modified using the keep-separate attribute, to indicate a physical separation between the inmates. The keep-separate attribute and/or modification of the schedule data occurs via one or more machine-learning algorithms. The modified schedule data is stored and/or a notification of the modified schedule data is transmitted, to one or more communication devices, to instruct one or more correctional officers to physically separate the inmates within the given time period.

Abstract: The present embodiments relate to generation of an object model for unifying a proration calculation process for one or more clients of service providers. A first set of input parameters associated with a client subscribing to a service offered by a service provider may be identified. The first set of input parameters may include at least a scheduled duration, a value associated with the scheduled duration, and a proration calculation. A first time period associated with the client representing a duration that the client utilized the service may be identified. An overlap period representing the overlap between the scheduled duration and the first time period may be determined. A first prorated value may be generated by executing the proration calculation associated with the client. The prorated value may be based on the overlap period and the first set of input parameters.

Abstract: A mining system for directing mine operations including a flow planner and a dispatcher. The flow planner receives operating parameters and global mine data and calculates a flow plan based on the operating parameters and the global parameters. The dispatcher then determines dispatch assignments based on the flow plan from the flow planner, and effects a dispatch of mining equipment based on the dispatch assignments.

Abstract: A method is described and includes acquiring sensor data produced by sensors of a plurality of vehicles traversing an area including a location of a user, wherein the vehicles traversing the area comprise a subset of a fleet of vehicles for providing rideshare services; processing the acquired sensor data to determine a category of the user; selecting a vehicle from the fleet of vehicles based on the category of the user, wherein the selected vehicle comprises at least one accommodation corresponding to the category of the user; and dispatching the selected vehicle to a pick-up location designated by the user.

Abstract: An automatic service monitor in an information technology environment has its operation controlled by information that, in part, defines entities that perform services and defines key performance indicators (KPIs) that indicate measures of performance of the services. Additional information controls the operation of the service monitor with respect to identifying and adapting for KPIs based on the non-normal data caused by maintenance work or other causes. Such adaptation may include changes in how reported information appears to the user.

Abstract: Method, system, device, and non-transitory computer-readable medium for classifying a vehicle trip. In one aspect, a computer-implemented method includes: obtaining a first set of historic telematics data associated with work; obtaining a second set of historic telematics data associated with personal use; training a classification model; obtaining a set of unlabeled telematics data associated with an unlabeled vehicular trip; and classifying the unlabeled vehicular trip by at least: identifying a first set of baseline operation features associated with the first set of historic vehicular trips; identifying a second set of baseline operation features associated with the second set of historic vehicular trips; identifying a set of representative operation features associated with the unlabeled vehicular trip; comparing the set of representative operation features against the first set of baseline operation features and the second set of baseline operation features; and classifying the unlabeled vehicular trip.

Abstract: Various examples are directed to determining an impact of a subunit on the value of a unit. A web-based analytics system receives unit value data via a user interface page provided to a supplier computing device by a web application, the unit value data including relationship data describing a relationship between a plurality of subunit values and a unit value. Based on relationship data, the system increases a value for a first subunit while holding values for the other subunits constant until the value of the unit increases to a unit value threshold and increases a value for a second subunit while holding values for the other subunits constant until the value of the unit increases to a unit value threshold. The system compares an increase in the value of the first subunit to increase the value of the unit to the unit value threshold with an increase in the value of the second subunit to increase the value of the unit to the unit value threshold.

Abstract: A system includes a virtual queue controller configured to receive an indication of a reduced capacity event from an amusement park attraction, determine a reduction in capacity of the attraction, identify each guest having a return time in a virtual queue of the attraction that is affected by the reduced capacity event, remove guests having affected return times from the virtual queue, and generate a reaccommodation time slot for the guests removed from the virtual queue, select two or more updated return times within the reaccommodation time slot for each of the guests removed from the virtual queue, provide a notification to each guest removed from the virtual queue requesting guest input to select a single updated return time from the two or more updated return times, and return each guest to the virtual queue upon receiving a corresponding selection of the single updated return time.

Abstract: A method may include a configuration engine receiving inputs for generating a customized function whose performance requires a first software application at a first host system to perform a first subprocess and a second software application at a second host system to perform a second subprocess. The configuration engine may apply a first customization to a first template to change a first manner in which data is stored in a first database table operated upon by the first software application while performing the first subprocess. A second customization may be applied to a second template to change the second manner in which data is stored in a second database table operated upon by the second software application while performing the second subprocess. The customized function may be implemented by sending the first template to the first host system and the second template to the second host system.

Abstract: Techniques and embodiments are described herein for detecting and mitigating fraudulent activity within user experience (UX) test applications. In some embodiments, a system applies a set of rules and/or machine learning (ML) models to each respondent of an online survey or UX test. Different ML models may be trained to learn domain-specific patterns indicative of fraudulent activity. The system may then select the ML models based on attributes of the UX test and/or respondent. The selected rules and/or ML models may generate a probabilistic score representing a likelihood that the respondent is currently engaging in or will engage in fraudulent activity with respect to a UX test. If the score exceeds a threshold, then the system may take action to mitigate the fraudulent activity, such as triggering the removal of the user from an accepted respondent pool, halting further engagement between the respondent and the UX test, and generating alerts.

Abstract: A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval.

Abstract: A system includes one or more processors; and at least one non-transitory machine-readable storage media comprising instructions which, when executed by the one or more processors, cause the one or more processors to: generate a set of shift candidates based on labor demand data; determine a set of decision variables representing whether a particular shift candidate is selected in a final schedule and whether a particular worker is assigned to the particular shift candidate; determine a set of constraints based at least in part on the set of decision variables, the set of shift candidates, the labor demand data, and scheduling configuration data, wherein a constraint of the set of constraints is transformed into linear form in terms of the set of decision variables; and determine simultaneously, using a MIP solver, a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of the shift candidates.

Abstract: In one embodiment, a method includes receiving, by one or more processors of an information processing system, one or more results of a task. The method includes determining, by the one or more processors, an accuracy confidence score for each result of the task. The method includes determining, by the one or more processors, that the results satisfy an accuracy quality threshold set for the task based on the accuracy confidence score for each result of the task. The method includes providing, by the one or more processers, a determination for the task to a customer system in response to the determination that the results satisfy the accuracy quality threshold.

Abstract: In a system of automatically analyzing infrastructure operation data, the system includes: an infrastructure operation determination unit configured to determine whether or not to execute an automatic analysis on infrastructure operation data; a data reception unit configured to receive operation data of a previous operation period and operation data of a succeeding operation period according to the automatic analysis execution; a statistics calculation unit configured to calculate statistics for the operation data of the previous operation period and the operation data of the succeeding operation period; and a significant difference analysis unit configured to analyze a significant difference between the operation data of the previous operation period and the operation data of the succeeding operation period based on the statistics.

Abstract: A system and method for preparing structural analyses for solar installation projects, designing photovoltaic solar electrical systems and thermal solar systems processing solar installation plans, including the processing and interpretation of user input and jobsite information, supplementation of user input with data queried from relevant databases, processing and analyses of jobsite information according to requisite analyses, automated generation of forms and reports for review by a reviewing entity, delivery of signed and sealed documents for process by appropriate regulatory bodies, and addition of third-party verified eSignature and/or file encryption to sign and/or seal reports and plan documents.