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: An expandable stent (12) for use in the implantation of a valve prosthesis (11) using a delivery system (10) is disclosed. The self-expandable stent (12) includes a tubular lattice structure (13) defined by longitudinally aligned rods (16) connected to V- shaped struts (17) for forming a plurality of interconnected chevron-shaped six-sided polygons (24) that define a distal end zone (21) and a middle zone (20) of the tubular lattice structure (13). A flare (27) or bend may be defined along opposite ends of each rod (16) to properly seat and prevent undue torsion of the stent (12) and valve prosthesis (11) during deployment and placement of the stent (12) within the lumen.