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 lighting audio playing apparatus comprising: a printed circuit board, for bearing the following elements; a connector, disposed on the printed circuit board for being coupled to a portable storage device; a playing controller, coupled to the connector; an audio decode controller, coupled to the playing controller; at least one lighting device, coupled to the playing controller; an audio output device, coupled to the audio decode controller; and at least one function key, coupled to the playing controller; whereby while the portable storage device being inserted into the connector, the playing controller will check whether a compressed audio file stored therein or not, if there exists a compressed audio file, the playing controller will transfer the compressed audio file to the audio decode controller to decompress it then output, meanwhile, the playing controller will drive the lighting device to light various color lights.