Abstract: The present invention relates to a method and a system for detecting anomalies in a robotic system in an industrial plant. The robotic system is associated with a computing system configured to detect an anomaly in the robotic system. The computer system monitors configuration parameters of the robotic system and process parameters associated with the robotic system. Further, the computing system detects an association between at least one configuration parameter and at least one process parameter for obtaining optimal configuration parameters and optimal process parameters. The optimal configuration parameters and optimal process parameters are analyzed for detecting an anomaly. At least one parameter among the configuration parameters and the process parameters is identified causing the anomaly. Thereafter, the detected anomaly is validated, valid setpoint is estimated and the estimated valid setpoint is updated in the analytics model.

Abstract: Methods, systems and computer program products for evaluating contributions in collaborative environments are provided herein. A computer-implemented method includes obtaining indications of tasks of a given workload that are completed by respective ones of a plurality of client devices. The method also includes identifying first and second tasks completed by first and second client devices at first and second times, determining whether the first task provides a clue for the second task and whether the second time is within a designated time window starting after the first time, and analyzing eye-gaze information to determine whether a second user of the second client device observed a first user of the first client device performing the first task. The method further comprises modifying contribution scores for the first and second users responsive to the determinations.

Abstract: Methods, systems and computer program products for evaluating contributions in collaborative environments are provided herein. A computer-implemented method includes obtaining indications of tasks of a given workload that are completed by respective ones of a plurality of client devices. The method also includes identifying first and second tasks completed by first and second client devices at first and second times, determining whether the first task provides a clue for the second task and whether the second time is within a designated time window starting after the first time, and analyzing eye-gaze information to determine whether a second user of the second client device observed a first user of the first client device performing the first task. The method further comprises modifying contribution scores for the first and second users responsive to the determinations.